To pass the test, they must not let their heart rate rise by more than 10%.

Students also must find a “dirty job” — cleaning public restrooms, mucking horse stalls or clearing restaurant grease traps — and do it for several days without pay. Students say they’re required to record themselves doing the work without complaining. Complain and they’ll fail.

The tasks are part of a “Bootcamp” running through the first eight weeks of high school at the private K-12 network. And the revelations, as well as others, come not from an investigation into the school but from students’ own tweets. 

First reported earlier this month in the newsletter by AI skeptic Benjamin Riley, founder of an education think tank of the same name, the tweets shed new light on a school that has long been a , especially among the Silicon Valley elite. 

The private school — its tagline is “School is broken” — is encouraging students to tweet about their experiences. Alpha curates the tweets . And for bootcamp students, tweeting isn’t optional: they must gain 100 “authentic” Twitter followers — “not purchased, not bots, not friends” — by Oct. 9 “or we fail,” according to one student.

An Alpha spokesperson did not respond to requests for details about the challenges or other issues. But in a posted Sept. 23, Riley quoted a student who said those who don’t complete Bootcamp within the allocated time “would get put into the ‘Pirate Ship,’” a segregated space reserved for students whose academics lag behind. Being relegated there, the student said, brought fewer privileges, including missing out on workshops. “It’s a morbid term but 
 [it was] pretty much a concentration camp of students. Until they were up to the standards of what Alpha wanted us to be.”

In a , Alpha said Bootcamp tasks are “not one-time, pass-or-fail tests. Though failure is an intentional and expected part of Bootcamp, students have unlimited opportunities to try again, apply what they learned, and keep working until they demonstrate mastery.”

The website listed 18 “tests” — a few students have said they’ve been given 21 — and notes that Bootcamp also includes more conventional activities such as delivering a filmed keynote of “who you intend to become by graduation” and finding a local non-profit with a “real problem” and using AI to build them a working solution.

U.S. Education Secretary Linda McMahon last year toured the flagship Austin campus and called the model the “” thing she’d seen in education in a long time. McMahon said she wanted to look at ways to Alpha’s methods into public schools.

Founded in 2014 in Austin, Texas, by MacKenzie Price and Brian Holtz, Alpha is now led by tech entrepreneur . Its signature strategy is known as , in which students work through adaptive software each morning with an AI tutor to learn basic academics — its creators say that enables students to “crush academics in just 2 hours a day and motivates them with the gift of time.” Students spend the rest of the day in life skills workshops, tackling entrepreneurship, public speaking, coding and the like. Teachers are called “guides,” and roughly two-thirds come to the job without formal teaching credentials, according to one . 

Alpha students, Price has said, learn “twice as fast” as typical students, with the top 20% learning 6.5 times faster. “My mission is that I want parents to understand that their kids do not need six hours of sitting in a class all day to get their academics done,” Price told magazine in 2024.

Tuition runs from about $10,000 to $75,000. The network has expanded rapidly from a handful of campuses to roughly 50 locations nationwide this fall.

With the arrival of the new Texas private-school voucher system, Alpha has nearly quadrupled the number of schools in the state to more than 30 virtual and in-person campuses, most of which can now accept the taxpayer-supported vouchers.

None of Alpha’s schools were required to meet state curriculum standards or show how the company’s model would lead to student success to gain approval, ProPublica and The Texas Tribune earlier this month.

Meanwhile, Alpha’s founders have repeatedly failed to expand the network’s reach into publicly funded charter schools: Of 10 states where it has sought charters, only one state, Arizona, has approved their plans. The Texas State Board of Education rejected Alpha’s application in 2025, with one member saying he was “just a little skeptical” of its claims to large gains in the two-hour timeframe.

In Pennsylvania, state officials also a proposal to establish a so-called Unbound Academy cyber charter school, saying they found “multiple, significant deficiencies” in the plan. 

In North Carolina, one charter school board member in 2024 feared the two-hour learning window was “not nearly enough” to teach required subjects, while a Utah official said Alpha’s instructors seemed more “life coach” than teacher.

The charter effort, meanwhile, has come under . In a widely circulated posted in January 2025, educator and instructional designer Dan Meyer noted that in its Arizona application, Alpha had budgeted $1,000 per student annually for marketing costs, fully half of the estimated $2,000 per student for academics. He also noted that while it claimed that its 2 Hour Learning program showed strong results at an Alpha campus in Brownsville, TX, which it said serves “students from underprivileged backgrounds,” tuition there is $15,000, “which is not a definition of ‘underprivileged’ I am familiar with.”

‘Cramming isn’t learning’

While Alpha has said it’s bringing advances in cognitive science to the schools’ curriculum, it’s not clear to what extent those advances show up in Bootcamp. The school has retained several high-profile in the , but students this fall say the bootcamp often requires them to make progress entirely on their own, with no adult guidance or supervision.

In late August, one 15-year-old tweeted that Alpha “gave us 5 days to teach ourselves an entire AP unit with zero lectures and no study guide. 
 Instead of waiting for a teacher to hand out chapter questions, we had to pull the official AP syllabus, use AI to break down the hardest concepts, and build our own master study system from scratch.”

By the end of the week, the student said, “we had to sit down and take the official AP exam to prove we could score a 5 on our own.”

The student added, “Most schools test how well you remember what someone told you on Tuesday. When you know how to build your own syllabus and teach yourself college material, you realize traditional classes just move too slow.”

A few days earlier, another student tweeted, “I have to raise my SAT score 150 points by October 9th, completely on my own. No tutor holding my hand, no one checking in, just me and my computer.” 

While many commenters on Twitter have encouraged these students over the past few weeks, urging them to strike out on their own and learn all they can independently, a few aren’t so sure. One retired AP macroeconomics teacher wrote: “You are missing the richness of a voice leading you to lifelong mastery.”

Another suggested that the school should ask Bootcamp students to take the AP test “without notice” near the end of the school year to see what they retained, adding, “cramming isn’t learning.”

The Bootcamp’s emphasis on hitting arbitrary goals could be problematic, educators say, playing on students’ desire to please adults but ignoring their inward desire to learn about a given topic. That could result in students who hit their marks but learn less than they could.

Teacher, writer and blogger said the two strategies — extrinsic and intrinsic motivation — are not easy to tease apart.

“Is a student studying for a test because they want the grade, or because they want to understand things deeply?” he wrote in an email. “It’s not easy in practice to tell. Most people are motivated by a mix of both intrinsic and extrinsic reasons. I’m sure that’s the same for Alpha’s students.” 

To make things more complex, he said, students “often internalize external motivators,” with high test scores, for instance, integrated into our own value systems as good things. 

“What seems distinctive about Alpha’s bootcamp to me isn’t the reliance on extrinsic motivators. It’s the value system these motivators represent, along with the use of clear quantitative benchmarks to measure them. Most schools don’t care if you know how to use social media, and even if they did, they’d be unlikely to require you to get one hundred followers.”

‘My hands were shaking the entire rest of the afternoon’

On its website, Alpha said the heart rate test challenges students to take in “at least three pieces of honest, critical feedback” calmly and “bring your heart rate back within 10% of your resting baseline. It is a lesson in hearing hard truths without shutting down.”

Writing about the heart-rate test, one student called it “another horror story I get to experience” on Aug. 31, noting, “a video of my parents criticizing me will be played in front of the entire school. yay.”

Another student provided perhaps the best description of the challenge, noting on Sept. 1 that she’d been assigned to write 10 questions to ask her parents. Her list includes several extremely personal, difficult questions, such as: “If I don’t change one thing about myself in the next five years, what will this cost me?” “What’s a compliment people give me that you don’t fully agree with?” and “What’s something I say I want that my actions don’t actually support?” 

Posting on Twitter , one parent said the exercise worried him at first, but he noted that his son “came to me with deeply personal questions about who he was becoming as a young man and how he could grow … questions he hadn’t exactly asked me before.”

He said the exercise led to a long conversation “where I answered critically but did so also with kindness and a focus on growth.” Very little of what he said, the father noted, was shared with the school or his son’s class.

“The heart monitor was probably meant to make the exercise feel like a game,” he wrote. “While I can’t speak for every family, all I know is that this brought me closer to my son.”

One student who went through the experience said on Twitter that students watched the videos in small groups of five to 15 people and concluded, “Funniest 30 minutes ever. Parents would say harsh feedback, and we’d simply laugh it off.” 

The student said she suffers from “terrible anxiety” but that the exercise “made me step out of my comfort zone allowing me to realize the harsh feedback I may receive in front of others will never be as bad as I make it to be.”

By contrast, another student, featured in Riley’s newsletter, tweeted that they “barely made the cutoff by one single beat, and my hands were shaking the entire rest of the afternoon.” The tweet has since been deleted.

Students are also asked to endure a three-day wilderness experience similar to an Outward Bound trip. Alpha calls it a “survival-style challenge.” One student posted a photo of a tent in the woods with the caption: “I’m spending three days in the wilderness and still coming to terms with it. In a couple weeks, we will be dropped in the middle of nowhere with only 3 bags of rice and a tent. I would consider myself a strong leader in difficult situations, but @alphaschool is putting that to the test.”

In addition, students must watch a full season of the reality TV show and analyze the players’ decision-making.

Other students have detailed the “Grit & Hard Work Challenge,” with one calling it “an unglamorous job with zero pay.” The student said that could entail deep-cleaning public restrooms or restaurant kitchens. “You record yourself narrating out loud through the entire shift, and the full transcript gets run through an audit,” the student tweeted. Alpha’s site notes that to pass, “the transcript must contain zero instances of complaining or victim language.”

The student seemed at least curious about the task, adding that while most high schools “talk about character building during morning announcements,” Alpha “puts you in the dirtiest job you can find to see how your mindset holds up under real discomfort.”

Another was more blunt, saying that by the end of September they’d have to, among other tasks, “Volunteer cleaning horse shit and not complain — I’ll be mic’d.”

In her , author and longtime ed-tech skeptic Audrey Watters said Alpha School’s appeal “is to parents who believe their child is the next genius tech entrepreneur billionaire. But it is damaging in its own right. AlphaSchool, as we can see in the details of the bootcamp, actively demands students mold their behavior and their identity, in this case into some sort of John Galt figure,” a reference to the 1957 Ayn Rand novel Atlas Shrugged.

Watters said Alpha’s curriculum “leans into the kind of pseudoscience that litters the pages of bestselling ‘get rich quick books’ — psychological pseudoscience, economic pseudoscience alike — and the ideology of Silicon Valley’s startup hustle. All of this is the kind of messaging you can regularly find on social media, no surprise, since this seems to be a school fixated on the anti-expertise of influencer-culture, the sociopathy of tech-culture: move fast and break things.”

In a note published by Riley earlier this month, educator and author said most of what she might say in response is “unpublishable.”

If the accounts are accurate, she said, “someone there needs to be reminded that teenagers are children. They are not soldiers in an army, they are not adults at a business school, they are not workers in a pressure cooker environment.”

A former Wall Street trader, Fernandez said she and her colleagues “were not treated as these students appear to be. Especially dark is the biometric monitoring, which sounds like something a CIA trainee would be subjected to.”

The surveillance and pressure to achieve that most other students experience are on a “new and unheard of level” at Alpha, she said. If, as they’ve indicated, they want to use the school as a model for others nationwide, Fernandez said, “I’m profoundly disturbed.”

Co-founder and CEO of , a Nashville-based nonprofit that works with teachers to provide high-quality resources and instructional support, she got curious about AI after seeing the tool in action. 

It’s designed to help students read texts closely, and it did it in a way that was “very different than any tech I had seen,” Freitag recalled. It made her think that AI “may be a horse of a different color. It may be really, actually a step forward in capabilities.” But she wanted to look past the hype. 

A former assistant commissioner of curriculum and instruction in Tennessee, Freitag got the idea to take a closer look at the full spectrum of AI tools. “I wanted to see it as a mom. I wanted to see it as an educator, and I wanted to see it to be a good partner to our schools.”

So she and a colleague last January began a six-month investigation that would eventually take them to 16 school systems in seven states. They sat with students as they worked through AI-enabled lessons, watched teachers teach with AI, and interviewed more than 100 teachers, school system leaders and ed-tech CEOs.

The results of their first foray are : Instruction Partners on Monday released the initial findings in what it is calling its AI in Action Learning Tour — a year-long effort to observe how actual students and teachers use AI. 

The report looks at 20 student-facing AI products already in classrooms, broken down into three categories: general-purpose chatbots such as ChatGPT, Claude and Gemini; education-focused platforms such as Brisk Teaching, Google Classroom, MagicSchool and Playlab, and targeted instructional tools built around a specific instructional jobs — they include Amira Learning, Khanmigo and Quill.

“AI is definitely not a single category,” Freitag said. “We saw a huge range of products designed to do a huge range of things.”

They framed their inquiry around two questions: 

1. How does it compare to paper-pencil instruction?
2. How does it compare to the best possible learning experience in that subject?

Freitag and her colleague found a decidedly mixed bag, with a few key surprises.

“I saw tools that held a higher bar for learning than we see in paper pencil instruction and did more to tailor the questions and the feedback for kids to the specific sort of responses they were giving than a teacher could possibly do all at the same time,” she said. “So I really walk away thinking there’s a lot of good here that we need to leverage — and a lot of bad here we need to mitigate.”

They found that general-purpose chatbots like Claude and ChatGPT pose the biggest threat to student thinking, while the platforms — Brisk, MagicSchool and the like — produced different results, depending on how teachers used them.

The targeted tools, such as Khan Academy’s Khanmigo and Quill, a writing app, produced the most consistent and strongest learning experiences, they found.

And teachers told them they’re frustrated by apps kids can easily game — in one case, students found that by simply hitting the “Refresh” button on a computer, they could complete an assignment by answering the same question repeatedly instead of the lesson’s full list of questions. Many students also learned that they could simply Google an answer and paste it into their lessons. 

In another case, an app gave students detailed feedback on their writing — “I thought the feedback was really good and useful and actionable,” Freitag said. But she watched in horror as students simply ignored it and began rewriting without consulting it.

In another instance, she sat with a student as he worked on writing a book based on characters he liked, focused on letter sounds he needed to practice. “He created the book, and he said, ‘Now it’s your turn. Let’s create a book for you.’ And I was like, ‘But are we going to read your book?’ And I realized he was just making books without reading them.” 

The boy, she said, simply liked making the books.

“As someone trying to make sense of where AI has a role to play or not, I think seeing how kids are actually using products matters much more than understanding what they’re designed to do,” Freitag said. “And there are many cases where what I saw in action was quite different than what I expected to see based on the product demo.”

In fact, many of the apps that give students the ability to work together shone in Freitag’s review. By contrast, several apps designed for solo use turned out to be “boring” to many students, who spent perhaps 10 minutes engaged. “By minute 42 of the lesson, they are just tuned out.”

In many cases, she said, the same product produced dramatically different results — and gave kids different experiences — depending on whether teachers built in clear routines and actively monitored dashboards in real time.

In one instance, a teacher taught a lesson with a math app that allowed her to display four students’ solutions to a problem side-by-side on a Smartboard, allowing students to evaluate the solutions. “I was stunned by how much kids learned from looking at each other’s work in that way,” Freitag said. Even with just a little feedback, it was effective. “When it was used socially, it really created a ton of learning opportunity for kids.”

The apps that worked did something that teachers couldn’t do alone, such as offering instant feedback or a quick analysis of an entire group’s work. Those, she said, “were the products where I was like, ‘Wow, this is extending what a teacher could do.’”

But the report emphasizes that AI shouldn’t cut teachers out of the equation. They still have a vital role to play and AI should never be seen as replacing teachers. “My take is that the future is hybrid,” Freitag said. “Teachers have a key and lead role, and there’s tech that gives them an assist.”

In fact, one of the most promising AI innovations is its ability to identify where students are struggling with foundational skills — and where they need more practice. But forcing students to practice those skills in isolation could be counterproductive. “If we help kids practice a bunch of reading skills without actually paying attention to the text or the knowledge that they need to access that text,” Freitag said, “we’re not going to see reading scores improve.”

She also worries about AI’s near-miraculous ability to re-level reading passages with a single click, one of the most popular features of most reading tools. While it can help slower readers understand a text, that releveling can become “a permanent lower setting” rather than a bridge back to grade level.

Arman Jaffer, CEO of Brisk Teaching, one of the apps reviewed, said his philosophy is that releveling “should be used to increase entry points to high-quality instructional material,” but not to limit how far students can go.

“AI is something that’s incredibly powerful that can help you do things like relevel,” he said. “You can make the entire internet accessible to your students, but at the same time, a teacher who might not have the best instructional practices might be using it to hold back students.”

Jaffer said the findings underscore the need for coaching and professional development for teachers as AI emerges in the classroom. “I think every AI company, every technology company, every vendor, truly needs to be a partner to their district or their school or their organization.”

Peter Gault, CEO of Quill, an AI-powered writing app that was also reviewed, said Freitag and her partner watched students using the app in New Jersey and worked hard to unpack how it worked. “I was really impressed with how much due diligence they did. They really went under the hood to understand what works and why it works.”

The duo, he said “did the most thorough deep dive of anyone who has looked at our AI.”

Alex Sarlin, co-host of the podcast, called the new report “amazing work,” noting that Freitag spoke not just with teachers and students but with tech CEOs. “She’s talking to all sides of the equation,” he said. 

The time she and her colleague spent in classrooms is key, Sarlin said. “That gives a much richer picture of how AI is actually being used in the classroom than we usually see, and certainly more than we see in mainstream coverage, which tends to be very surface level and a little panicky.”

This fall, Freitag’s team has grown to three people — they’ll soon look at 30 more products and report on them this winter. And Instruction Partners plans to work with Stanford’s AI in Education Lab to independently vet impact claims by winter 2027. The new report notes that just seven of the 20 products have independent studies examining outcomes across student subgroups.

For her part, Freitag said teachers are hungry for clear guidance around AI. “We work with instructional leaders across the country, and they said, ‘We are getting inundated with sales pitches, and we are getting inundated with calls now to “Ban it all,” and we don’t really understand where there’s value in this.’ So that helped me see that the debate about AI in schools is moving faster than real information.”

A former health industry reporter, Singer sees parallels between Silicon Valley’s campaign for coding, among other measures, and Big Pharma’s influence on medicine, noting huge school investments over the past few years by Apple, Google and Microsoft, among others.

“Never has so much market power lined up behind a single curriculum cause,” she writes.

As a result, a movement that could have helped build data science or other less glamorous, evidence-based reforms has in many cases ended up offering students “only half an education in computing, the rosy upbeat half that casts tech giants as benevolent, world-changing innovators.” 

In one chapter, Singer examines a 2019 Advanced Placement Computer Science course developed by Apple that teaches students how to code in Swift, Apple’s programming language for building iPhone apps. It’s as if schools embraced a “Raytheon-branded AP physics course or DuPont chemistry course or Pfizer AP Biology course,” she writes.

Singer finds both problematic and promising moves by tech over the past decade or more. While she calls out the nonprofit for skewing the idea of computer science to coding that often helps serve corporate interests, she lauds its founder, Iran-born entrepreneur , for making coding appealing and fun to millions of young people, telling Âé¶čŸ«Æ·â€™s Greg Toppo that U.S. public schools likely wouldn’t offer computer science at this scale if not for Partovi.

“I don’t know anybody else that’s been able to do that,” she says. “I think that did change the world.”

Ahead of the book’s release on Tuesday, Toppo and Singer talked about her findings and the lessons that the tech industry is now taking into the fight to get into schools. 

The conversation has been edited for length and clarity.

The book spends a lot of time with Partovi and Code.org. Would you say it is basically a positive influence?

Natasha Singer: Code.org catalyzed computer science in schools. They also partnered with a lot of big tech companies and brought in commercial things — the Hour of Code has the lessons and Elsa the lessons and the Microsoft lessons and the Code Your Own lessons, and so it also brought in a kind of commercialism that I have questions about. I don’t think we would have computer science at this scale in schools if not for Code.org.

One of the key takeaways of this book is that there are other ways to do computer science in schools. In fact, if there’s a hero, it’s , who says Code.org and other groups are mostly just getting middle-class white boys interested in coding. Can you tell us about her? 

Jane Margolis is an incredible researcher. She grew up in Berkeley, Calif., during the ’60s, and she went to UC Berkeley. She was a Vietnam War protester and then she was a labor activist. She worked for the telephone company — at the time, it was Pacific Telephone and Telegraph, and there she saw these gender biases at work because she worked as an operator, the person who sat at the switchboard with all those wires plugging in calls. Male employees were allowed to be linemen, and they would go outside and climb these telephone poles and install phones, and she was trying to unionize the women to say that job should be open to everyone. Her view was that women need to have technical skills and equal opportunity to technical careers. Eventually, she goes to Harvard and gets a Ph.D in education, and she’s looking at gender disparities. Harvard has this government department, and it was inhospitable to women, and so she did her thesis about why boys were shouting down girls and pushing girls out of political science. 

She starts in computer science in the ’90s at Carnegie Mellon University. Carnegie Mellon, as you know, is at the forefront of computer science education and robotics and computer vision. They were one of the first to start a dedicated college of computer science, as opposed to a major. But a couple of years in, they realize that only 7% of the people majoring in computer science are girls, and they can’t figure out why. 

, who’s the associate dean at Carnegie Mellon, hires Margolis, and together they do a study looking at why it is that women are not going into computer science and are dropping out. And they find there are all these factors, one of which is that families are buying personal computers. She found by interviewing male and female students that the boys would be like, “Oh yeah, my dad or my uncle went to Radio Shack and brought home this TRS-80 computer and put it in my room, so I fooled around and learned to code, and then my neighbor came over — he’s a software engineer.” So by the time the boys show up at Carnegie Mellon, many of them have already taken computer science in high school. They know what they’re doing. And some girls have no experience, so they’re already feeling behind by Day One. 

It wasn’t just that girls were left out. It’s that when girls tried to take computer science in high school, often the guidance counselors would say to them, “Oh, you don’t really want to do this,” or “You’re not going to do well,” or “It’s going to hurt your grade-point average.” There were all kinds of socio-cultural things going on â€” sexism and a whole bunch of other stuff that dissuaded girls. 

And so they wrote this report at Carnegie Mellon, and they did all kinds of things to attract girls to computer science. There had been a requirement that if you wanted to major in computer science, you had to have taken it in high school. But of course, girls were dissuaded from taking it in high school. So one of the things that Carnegie Mellon did, they looked at other students who might have been good at math or sciences but didn’t necessarily take computer science, or who were leaders in their schools — perseverance qualities that make you good at computer science. Within a few years, the percentage of girls taking computer science shot up. 

And then Jane Margolis was like, “O.K., we now understand how to get more girls in computer science, but the percentage of African-American students, Latino students, lower-income students is even worse. She then becomes a researcher at UCLA. She works with Los Angeles public schools. They find similar problems of Black and brown and low-income students being dissuaded from taking computer science. So they come up with a course to make computer science more welcoming. 

Her whole idea is that if you do not grow up with a computer in your house, or you don’t know how to code, and you show up to computer science class and the first thing is computer programming, and the second thing is computer programming, and the tenth thing is computer programming, you may be turned off before you even know what it is.

So they created a class to explain to students who were first-timers: How does the internet work? How does a computer work? What is a motherboard? What is a central processing unit? And how are you using tech in your life? And do you know how an email gets to you? How are platforms — at the time it was more like and Facebook — shaping your life as a student? And then eventually, when they learn a whole bunch of stuff about how the technology works and why it’s interesting, then they start programming. 

And it’s not programming for programming’s sake. They made wearable computers — they had wristbands that had LED lights so they could make creative patterns. They could spell out their name, these wristbands, but they had to use computing to make it work. They had to program the designs. They made a lot of progress with this course, not just in Los Angeles but in Chicago. It was called Exploring Computer Science, and it began to grow slowly. 

Then the tech industry comes along and says, “We have this crisis: We need more programmers and software engineers, and every kid needs to learn to code.” And a lot of those courses were programming-centric, and I have to say some of them also included social aspects such as the ethical use of computing. But this notion that we live in a world surrounded by machines and it’s crucial for young people to understand how these things work in order to have agency and navigate them, that’s the idea that Jane Margolis was arguing for 20 years ago.

I want to ask about this story from 2015 that you tell about the course, which brought together the National Science Foundation, Code.org, Apple, and others. Can you briefly tell that story?

There was a leader at the National Science Foundation named . She was running a program called . And she was charged with broadening participation at the university level — that is, getting more women and African-American students and Latino students and students with disabilities and Native American students and others in computer science, because they’ve been historically marginalized. And she’s watching what Jane Margolis and others are doing in Los Angeles and around the country. And she realizes that the problem starts before kids get to college. 

So she gives millions of dollars to universities to develop different AP courses. The kind of course she envisioned to broaden participation in computer science didn’t exist at universities because universities took hardcore first-semester computer science that was designed to weed out people, and so she funded different universities, including UT Austin and the University of Washington and Trinity College, to develop more like Computer Science Zero, an online course that would look similar to what Jane Margolis was doing. It would teach you how the internet worked, it would teach you some data science, it would teach you some programming, it would teach you to think about the social impacts of technology. It would be a new AP course called Computer Science Principles, and you could take it as the intro. And then if you liked it you would go on and take AP Computer Science, which was much more rigorous and much more programming oriented.

So she convinces the College Board that they need to do this. And then, from Jan Cuny’s point of view, out of nowhere, The College Board announces that it’s partnered with Code.org to launch , a Code.org course, and then they announce that they have endorsed specific curriculum, including the Code.org one, but they later endorsed an AP Computer Science course from , which teaches kids to code in , Apple’s programming language for iPhone apps, and they also endorse a Microsoft course called . And Jan Cuny — NSF has funded a lot of this — is really stunned because nobody had told her beforehand that the College Board was going to . But also: Why weren’t they endorsing the ones that she funded? 

So she called The College Board and was like, “You’ve got to endorse all these courses then.” And by the way, the [AP] course was a smash success. Hundreds of thousands of kids took it. It was an introduction to computing that was broad for a lot of kids who didn’t know what it was. It did diversify who was taking computer science, although it’s still very skewed toward boys.Ìę

But her point was that if you are trying to make a broad course that tries to get more kids of different backgrounds into computer science, Apple products skew to higher-resourced schools and higher-resourced kids. I read some of the Apple textbook, and there are lines like, “You can use this to make an app for your iPhone or even your Apple Watch.” This Apple course assumes that you have an Apple Watch. And Jan Cuny was like, “That’s not what we were doing when we were trying to broaden participation in computer science.”

I went back to the College Board, and they said, “When you’re broadening participation in computer science, you have to broaden it to everybody, and there are schools that do have MacBooks, and so maybe this course will work for them, whereas other courses won’t. It’s an array; you don’t have to use this one. You can use all of them or different ones.”

But for me, there are anomalies with having companies operate in schools, and the AP Apple curriculum and AP Microsoft curriculum show the unusual ways that tech companies influence schools that many other industries do not.

Fast-forward to 2026: Now we’re dealing with AI, and obviously tech is reading from a similar playbook. But it feels somehow different to me. I mean, we’ve had social media trials and , and these tech bros aren’t the white-hatted heroes we used to think they were. Does this moment seem different to you?

This moment is different, but also similar. We are in a moment in society where a lot of people have increased distrust of these huge tech companies, and we see a wave of parents pushing back against tech in schools. But we also see waves of people pushing back against , against , that there’s this feeling that tech has taken so much, and that people feel like they have lost control — that tech is being enacted on them.

And then when we talk about the social media trials and the big verdicts against Meta, both the one with dozens of states where Meta now has to pay $17 billion, but also the one earlier in the summer where New Mexico got in a judgment against Meta, those companies are no longer our darlings. And parents are worried about the effects of unfettered tech access and kids’ compulsive use of social media and phones, and so there’s a different climate. And yet there seems to be a disconnect in some ways with AI because you see that Microsoft and OpenAI and Anthropic and Google are all competing to get their AI tools into schools, and you see school districts across the country say “We’ve partnered with OpenAI.” “We are a ChatGPT Pioneer School,” or “We’ve partnered with Microsoft, and we’re going to use 10,000 licenses of CoPilot.” It’s amazing to me. We have had these cycles of tech in schools where there are all these promises about the latest tech, laptops, learning apps, massive open online courses, virtual reality, big data’s going to revolutionize schools and like democratize access to education for kids and get them these great career skills, and we keep having these cycles. My concern is that we don’t learn anything.

One of the reasons I wrote this book is because it feels like we have collective amnesia. Now we’re in the sixth or eighth cycle of this — and shouldn’t we be asking questions about the push for AI in schools, based on what we’ve learned about the push for all the other things that came before? 

I visited this school in Newark where two teachers had launched an AI literacy class because they were worried their high school seniors were going to graduate into a world with AI and not know anything about it. I went to the first class, and the teacher said, “We need to be honest with you. You’re the first kids we’re gonna do this class with. We don’t know how to teach this. We don’t know what you want to learn. And a year from now, this is gonna look completely different.” And there was this kind of honesty and humbleness: We, the teachers, are in it together with you, the students, and we’re gonna learn how AI works. 

They learned rigorous stuff, and they also learned to make stuff with AI. They weren’t just critical of AI, but they did it together. And if kids found something interesting, they went with that. And it’s a really hard thing for schools to know that we actually need to prepare kids about AI because they’re using it already, but in fact, we don’t know what to do yet, and this is going to rapidly change. That’s a really big challenge.

I was heartened by the chapters near the end where you go into classrooms. People are really embracing a new way to do this stuff.  

You know, I feel optimistic and upbeat about this new generation of teachers that want to equip students with broad critical thinking skills — and that involves both learning how the tech works and learning to use the tech and make stuff with AI, but also to understand the provenance of these AI tools and to understand that we have companies with more power than most countries, and we’ve never had that before. They are setting the rules we live by. So you have to understand how the tools are steering us.

Just as the technology has rewired the relationship between young writers-in-training and their teachers, it has also changed how student journalists and their editors work together — or, in some cases, don’t. 

And AI has created headaches for these journalists that mirror those afflicting their professional peers, with school newsrooms struggling to figure out how to respond.

As with their schools more broadly, student newsrooms have spent these years deploying surveillance software and, in many cases, drafting zero-tolerance policies for AI. Different newsrooms have developed different answers to the same basic question: How much do we trust teenagers to not let a chatbot do their work? 

Âé¶čŸ«Æ· spoke recently to three student journalists from three very different newsrooms about their experiences:

• Cid Frydman, a rising senior at Berkeley High School in California, who writes for the ;
• Veronica Mollod, a rising senior and current events editor at , the student newspaper at the High School of American Studies at Lehman College in the Bronx, N.Y.;
• Tova Weiss, a recent high school graduate in Ann Arbor, Mich., who has split her time between Pioneer High School and . Weiss has written and edited for the and , a publication written by and for Jewish teens.

Here are eight-and-a-half ways — including one counterintuitive, not-for-attribution observation — in which AI is changing their realities:

1. In a few schools, AI has overturned the culture of newspapers.

Frydman, the Berkeley student, said strict AI use policies at her publication have bred resentment among student writers. 

“I think a lot of the people higher up on my paper, and in journalism in general, don’t have a lot of trust in writers anymore, especially with so many AI resources that are out there,” she said. 

Editors are “very harsh on writers” over worries that they’ll use AI, keeping strict controls over documents, she said. They’ll poke around writers’ search histories and analyze their typing speed and word choices, matching drafts to previous work. 

“It’s very strict when you put it all together,” Frydman said. “They don’t have a lot of faith in our ability to actually produce real journalism, and so just assume the worst automatically, which can be a little frustrating when you’re just trying to do your best as a writer.”

At the Berkeley High newspaper, staffers have been let go over suspected AI use. 

Frydman sees the high cost of such heavy-handed policies: “We’re here because we love journalism,” she said. “We’re here to write about things that we’re hopefully passionate about, and the heavy approach of ‘There’s no tolerance whatsoever,’ and not even giving writers the grace and the trust to make their own decisions and do this work on their own — just minding it and monitoring it so heavily, I think it’s very discouraging.”

More broadly, getting caught using AI to write academic assignments carries weighty consequences, she said. You’re unable to get a letter of recommendation from any teacher, Frydman said. 

And a single infraction travels fast: Get caught using AI once, she said, and teachers will tell all of their colleagues “to watch out because this student has used AI before — they might use it in your class.”

2. AI is changing how — and how much — students write.

Frydman said she’s adopting what’s basically a minimalist approach to writing these days, holding back details so she doesn’t draw suspicion from editors or teachers. 

Though she doesn’t use AI for writing, she said, “A lot of times, I find myself sort of slimming down my writing to not go so into detail on things because it’ll be suspected to be something that’s not original. You don’t have a lot of room to be creative in this process, so it’s definitely been a little challenging.”

Ann Arbor’s Weiss said she’s also seeing many young journalists “adjust their writing voice” to avoid being flagged as relying on AI. Many students, she said, now intentionally add typos to their writing to support its legitimacy. “It’s absolutely devastating to watch how many people are intentionally damaging or diminishing the quality and authenticity of their work,” she said. 

At Berkeley High, school-issued Chromebooks are locked down to block access to ChatGPT, Claude and other major AI tools outright — and any attempt to reach them gets flagged. “It notifies the teacher through this program that we have called Go Guardian, which is embedded into a lot of the Chromebooks, where they can sort of see what you’re doing and be able to call you out on it,” Frydman said.

3. A few newsrooms are writing AI policies.

At Youth Environmental Press Team, where Weiss is an editor, staffers have been talking “in pretty much every director’s meeting” about the implications and problems surrounding AI. “We are an environmental publication, which means a lot of us are very climate-conscious.”

The environmental impacts of AI data centers are a cause of great concern, Weiss said.

After fielding a wave of questions from reporters, the publication developed formal guidelines rather than instituting an outright ban on AI. “It was always very clear for us that we would not publish or even entertain the idea of publishing anything that was written by a chatbot or any kind of generative AI,” Weiss said. But they soon got queries from reporters asking if they could use AI for outlining, for formatting statistics, for interview transcriptions and the like.

The policy that emerged prohibits AI-written and AI-edited work, including work that heavily uses tools like Grammarly. But transcription services and basic spell-check are still allowed. 

The organization, she said, wants to make writing “as accessible as possible so people can have their work published — but not if that means it’s not really their work, and especially not if that’s at the cost of the environment.” 

4. Other newsrooms rely on experience and vibes.

Weiss helped craft the formal AI policy at the environmental journal. At jGirls+ Magazine, by contrast, she said there’s no written policy. But in practice the standard is clear: Submissions and even staff applications that appear to be AI-generated are not accepted. In one case, she recalled, an obviously AI-written editorial board application — with a stiff style that lacked personal voice — tested with an AI detector as 100% AI, “which was really something none of us had expected.”

Weiss said AI detectors should be used only “when there is already prior reason to suspect something might have been AI generated” — a student with a track record of misusing AI, for instance, or because something just simply reads a little “off.”

5. In most cases, AI detectors are widely seen as unreliable.

Weiss described a tiered system in the environmental outlet’s workflow, with writers operating on an “honor system” but with student editors flagging “sketchy” pieces such as those that are missing statistical sources or those that quote dubious ones — or writing that doesn’t sound like a typical teenager’s.

“The thing about student voices is typically each writer has a distinctive style of writing,” she said. “We’re high schoolers: We have typos, we make mistakes in there. And if we’re finding something that’s void of personal style or just generally reads in a way that doesn’t seem like it was written by a student, we’re going to look into that further.” 

But she and others worry that using an AI detector could unfairly flag strong writing as suspicious simply because it’s polished — a phenomenon that happens more than you’d think in many schools, Weiss suggested.

AI detectors aren’t really reliable, said Frydman, the Berkeley senior, “so even if you write something really well, it’ll just automatically assume that it might be AI and your writing will get flagged, which has happened to a lot of my friends.” 

6. A few simple rules can keep AI from taking over, journalists say.

Mollod, the New York City journalist, said a somewhat arcane editing rule at her publication basically serves to help keep AI-generated content at bay: At Common Sense, every article is required to include a quote from a student in each grade level — a structural feature that makes it hard for AI to intrude, since students typically remember what they’ve said to a reporter. 

7. In a few cases, teachers are using AI more than students.

The journalists described several instances in which teachers, not students, were the ones turning to AI, relying on the technology to formulate lesson plans, create assignments and grade them.

One student who asked not to be quoted directly spoke of a teacher who “will just copy and paste straight off of Chat GPT or Claude, and just give us assignments that are so clearly AI that it’s ridiculous.”

Others rely on it for grading assignments, which “leads to a lot of people getting unfair grades, because AI can mess up,” said the student.

The more teachers rely on the technology, “the easier it is to just forget that you’re there for a reason, you’re trying to do your job for a reason — and AI should not be teaching kids Spanish or English or math or anything like that. It can’t reproduce real thinking. So it’s definitely a little dystopian.”

8. For many students, refusing AI isn’t a school rule. It’s personal.

Weiss, the Ann Arbor writer, said she has never used Open AI’s popular ChatGPT, due to environmental concerns over data centers as well as personal concerns over AI more broadly. “It just does not align with my morals at all,” she said. 

But casual AI use has become normalized among many of her peers, she said, with students openly saying, sometimes within earshot of teachers, that they ran an assignment through a chatbot the night before it was due.

The best way to fight AI, she said, is basically to boycott it. “The strongest way anyone can combat artificial intelligence is just by refusing to have anything to do with it,” she said.

While AI-generated writing more broadly can damage the way that people learn and is worth thinking about, Mollod said, she’s not sure that it has gotten to the point where “just everyone is just cheating, and no one’s really actually taking their work seriously.”

She reads the coverage and believes most people who don’t spend time in schools overestimate how much teens use AI. “People not in school are like, ‘Well, I didn’t have this great tool. If I had it, I wouldn’t have done anything,’” she said. But relying on the technology, which showed up in the middle of her eighth-grade year, isn’t yet common practice for most students she knows. 

Whether future students will warm to it more isn’t yet clear, Mollod said.

Weiss, the Michigan student, said her work — especially at the environmental publication — has taken on a new significance in the age of AI, pushing to celebrate and highlight “authentic youth journalism and youth narrative” in the face of encroaching technology. “In my view,” she said, “demonstrating our talents as humans and as youth is a form of silent protest against AI by proving that artificial intelligence isn’t necessary to produce professional-quality journalism.”

Bonus: Non-AI cheating abides. 

On background, one of the students said she thought a lot of the news coverage about AI cheating, while serious, simply misses the point. The old-fashioned kind of cheating, for now, remains much more common. In 2026, students are still relying on copying each other’s homework, just as generations before them did. 

“Mostly it’s just, like, circles of people, where there’ll be a math class and five people do the homework — and the other 20 got it from this person, [who] got it from this person, [who] got it from this person — which has always kind of been going on.”

A College Board last fall found that the share of high schoolers using it for schoolwork had climbed to 84%, with about half using it to brainstorm, revise essays or research topics. A follow-up found that high-achieving students — and those who are perhaps experiencing “senioritis” — are the most expansive users of AI. 

A new survey by the nonprofit Common Sense Media offers another set of key data points, looking at teens’ own sense of how they use the technology — and what AI is doing to their thinking.

The nationally representative sample of 1,017 U.S. teens ages 13 to 17, conducted in April and May, found that 70% now use AI for schoolwork, but that fewer than one in three say a teacher has ever talked with them about using it safely. And just one in four have discussed with a teacher how to tell whether AI-generated information is accurate.

Here are five things to know about the findings:

1. Most teens say they’re using AI to support their thinking, but nearly as many see it as an answer machine.

Among the 70% of teens who use AI, 77% say they use it as a support tool, using it for brainstorming, checking their work and getting feedback on writing, for instance. And 66% say it helps them understand schoolwork, not just finish it faster.

But 63% separately report going straight to a chatbot for an answer rather than working their way to it themselves. Of those, 25% use AI output as-is, while 31% rewrite it to sound like their own voice. Another 35% “improve it with what they know.” 

And yes, the 77% and 63% figures add up to more than 100%. That’s because many teens say they are, at different times, different kinds of AI users, either looking for support and brainstorming or simply zeroing in on a quick answer, depending on the assignment. 

Former U.S. Education Secretary John King, an advisory board member for the nonprofit’s , said the 63% figure is worrying “because we can’t allow AI to become a substitute for students doing the learning themselves.”

Much of that will fall to teachers, he said, who must change their basic practices. “Some of that is old-fashioned blue books and saying, ‘We’re going to do some of the writing in class,’ complementing a written assignment with an oral presentation in class so you know that students are really doing the work.”

He acknowledged that changing course design “is really hard,” there’s really no alternative. “I think this is really a moment where the entire K 12 sector has to think through: ‘How do we help students understand AI safety and appropriate use of AI tools, and how do we adapt teaching and learning assessment so that students are doing the intellectual work?’” 

2. Teens know that AI takes a cognitive toll.

Many of those who rely on AI for schoolwork acknowledge that it’s changing how they think: 38% say having AI available leads them to develop fewer original ideas, and 39% say that when they use AI to complete assignments, they feel they’re missing out on learning.

Darren Lam, 18, a recent graduate of New York’s , said AI use was widespread among his classmates. “A very large percentage of people that I know either regularly use, or have at some point used, AI to just completely do an assignment for them,” he said. “So I think that the concern of AI being used as a cheating tool is definitely not invalid.”

But he said the bigger problem is students not coming clean about their motivation — or lack of motivation — to learn. “If students wanted to genuinely learn and were willing to be honest about their own work, then giving them free access to AI would not encourage them to cheat in the first place,” he said.

As it is, for many students school simply isn’t relevant, he said. “They think that many of the things that they learn in the modern education system aren’t particularly beneficial once they leave the four walls, and I think because of that they want to just skip through it as fast as they can. And the fastest way is by just getting AI to do all your work and then submitting it.”

3. School AI bans don’t really work. 

Among teens who use AI for schoolwork, 44% say they’ve had an AI tool blocked on a school device or network. Yet 59% of these students say they simply switched to a personal device with less oversight — and less support — than a school-managed device or network.

Lam, who plans to major in electrical engineering in college, said his Staten Island high school has a fairly open policy about AI — it’s full of future coders and computer engineers, after all — but he worries more broadly that schools limiting access to AI tools could backfire.

“Although it can sometimes prevent some harmful behavior that may arise,” he said, “it also prevents students from learning how to use it effectively, which is going to be a very pivotal skill coming into this job market.” 

4. Teens are clear on which skills matter most going forward.

Asked to name the skills that matter most for their future, teens zeroed in on a few key ones, dubbed “the human skills that AI can’t or shouldn’t replace” by Common Sense:

• 55% chose “working well with others” 
• 50% chose “fact-checking and critical thinking”
• 48% chose “reading and understanding complex information”

King, the former Education Secretary, who the State University of New York, said helping students develop these skills in response to AI will be difficult, but educators can’t bury their heads in the sand.

“I think a lot of teachers already, understandably, feel overwhelmed by the core challenge of getting students the academic knowledge and skills they need,” he said. Noting that many schools are still doing “recovery work” from the disruption of the COVID pandemic, he said, “I’m sure it feels like a lot. That’s certainly true for our faculty at SUNY, but I think the reality is these tools are here. Students are using them, and we have to respond thoughtfully.” 

5. AI literacy instruction in schools is the exception, not the rule. 

The new findings arrive Tuesday alongside the launch of a new Common Sense and a new AI and Student Well-Being professional development course. The curriculum offers a research-backed framework for building students’ AI literacy skills through two courses, one designed for K-8 students and a second for high school students.  

According to the survey findings, only 30% of teens overall say a teacher has ever talked with them about how to use AI safely — and just 26% say teachers have discussed how to evaluate whether AI-generated information is accurate or trustworthy.

Alex Kotran, CEO of the , said that’s especially troubling, given AI’s prominence in kids’ lives. “The fact that 70% of kids haven’t had a conversation about AI safety is a pretty big alarm bell, because it means the kids are using this stuff, and a lot of them are flying blind.”

Key to addressing the crisis of students using AI to offload cognitive effort, he said, is  training teachers how it works. “There is no solution that doesn’t involve a teacher just understanding how AI can be used, and through that understanding [and] adapting assignments and lessons.”

Like other observers, Kotran suggests that teachers use this moment to rethink assessments, turning to in-class demonstrations, talks and the like to ensure that students understand material. 

“Even if a student is going home and using AI, it’s not going to help them because ultimately they’re going to have to go and demonstrate their knowledge in the classroom,” Kotran said. “I think it is as simple as that. There are other ways to adapt assignments, but I think it’s really hard to adapt your assignments if you don’t understand what AI is actually capable of.”

My job is to tell a student, kindly, when an argument is not working yet. But the machine had just told them it was. Not because the machine was broken, but because it had been optimized to agree.

For two years, educators have warned students that artificial intelligence makes things up. It can give a wrong answer wearing a confident face, a phenomenon known as hallucination. But there is also a quieter failure we have barely named, and it is more corrosive because it feels like support.

Researchers call it sycophancy: when an AI system flatters, agrees with or validates a user even when a correction would be more truthful. A hallucination is a wrong answer. Sycophancy is a wrong relationship.

When students head back to school at the end of the summer, AI will already be part of their schoolwork. Last year, the ⁠ that 84%of high school students used generative AI for schoolwork, and a February ⁠ found that 54% of U.S. teens had used chatbots for assignments: writing, summarizing articles or videos, studying, and creating or editing images.

A from Turkey illustrates why this is a problem. Nearly 1,000 high school students were given help from an AI math tutor powered by GPT-4, the technology behind ChatGPT, while practicing math. They solved more practice problems with the tool. But when the tool was taken away for the real test, students who had used the less restricted version, which could provide more direct answers, scored 17% worse than students who had no AI help. In the same study⁠, a guarded tutor – designed to give hints instead of answers – avoided that drop in test performance.

AI can make learning look easier while quietly removing the struggle that makes learning stick. In writing and analysis, where judgment matters, it can even encourage students to do inferior work.

Learning runs on friction: the red pen, the Socratic question, the “not yet, try again.” Education often tells students they are not right yet so they can become right. A tool engineered to affirm removes a core foundation that education depends on.

In April 2025, OpenAI rolled back a version of GPT-4o after it had become, in the company’s words, “⁠.” The company later admitted it hadn’t tested for sycophancy before rolling it out to hundreds of millions of users. OpenAI pushed a temporary system-prompt fix, then returned users to the earlier version. The rollback fixed that release, but it didn’t address the broader problem of AI systems becoming too agreeable when users need correction.

The pattern is not limited to one chatbot. In March 2026, a Stanford-led team published a ⁠ across eleven leading models. When users asked for guidance about personal situations and relationships the models affirmed their actions 49% more often than humans did. In prompts involving deception, illegality or other harms – such as lying, manipulation or socially irresponsible behavior – they endorsed the behavior 47% of the time. People who interacted with sycophantic AI became more convinced they were right and more likely to trust and reuse the tool. 

A student who asks whether a weak thesis is strong, whether thin evidence is enough or whether a draft is ready to submit may not be looking for praise. They may be looking for a teacher. If the tool offers confidence instead of correction, the student never feels the discomfort that turns a half-formed idea into a real one.

I am not arguing that students should avoid these tools. But every student using AI should ask: When has this tool stopped helping me think and started flattering me into not thinking?

That confusion may cost the most among the students I work hardest to reach. At a historically Black university, I teach young people who are brilliant and underestimated, who are wary of how institutions judge them and who sometimes turn to AI because it does not seem to judge at all. 

For students surrounded by regular, trusted feedback, an over-flattering tutor may be an annoyance. But for those with fewer chances to get honest, trusted feedback, it can become a closed loop: The student asks, the machine praises, and no one tells the truth. 

A 2026 Stanford study of ⁠ makes that worry more concrete: identical essays received different feedback depending on how the student was described, with essays attributed to Black students receiving more praise and less substantive criticism. That is not reassuring.

So what do we do?

First, educators should teach AI as something to interrogate, not an answer machine. Assign students to make the model disagree with them, hunt for the moment it praised a weak argument, and grade that catch. “Where did it just agree with you?” is a teachable skill.

Second, instructors and advisers should change the question they ask about AI help. Not just, “Did it give you an answer?” but, “Did it make you explain your thinking?” “Did it ask what evidence you had?” “Did it push back?” “Did it make the work harder in the right way?”

That kind of friction has worked before when it is built into the tool, giving hints instead of answers, asking questions that make students show their reasoning and offering prompts that slow them down before they accept AI advice. In the Turkey study⁠, the guarded version that guided students instead of simply spitting out answers largely avoided the learning harm. Other studies have found that prompts requiring users to ⁠, ⁠, and make their own judgment can reduce blind acceptance of AI advice. Until every tool is designed to slow students down in those ways, adults must teach students to add the friction themselves.

We spend our careers teaching students to tolerate being wrong long enough to get it right. The best AI tutor may not be the one students like most in the moment, but the one that refuses to finish the work for them. The friction was never an obstacle to learning. It was the learning.

When students return to school this fall in the Salamanca City Central School District in Western New York, a new kind of teacher will be ready to greet them. The small, rural district located on the Seneca Nation reservation is set to be one of the first in the country to put a humanoid robot in a classroom. It will not replace the classroom teacher, but is programmed to provide learning support to both students and educators. 

At a board meeting last month, the Cattaraugus County school district agreed to purchase the robot from Realbotix, a tech company, along with an artificial intelligence teacher’s assistant program allowing students to interact with an avatar of the robot on laptops. 

“This deployment in a working school district represents a landmark moment for both AI and humanoid robotics,” said Andrew Kiguel, CEO of Realbotix, which is currently building the robot. “Salamanca marks the beginning of a new era where humanoid robots and intelligent AI assistants become standard tools in STEM ±đ»ćłÜłŠČčłÙŸ±ŽÇČÔ.”&ČÔČúČő±è;

The female robot, named Sally, will have a “lifelike appearance” with silicone skin and long brown hair, Kiguel said in an interview with New York Focus. It will be stationary in a seated position but have a wide range of upper-body movements and facial expressions.

Students will use a unique identification code when interacting with the robot during class, allowing it to access their learning data and provide personalized support based on their past communication with the avatar, Kiguel said. “They’ll be able to say, ‘Hey, I’m student number 1234,’ and then the robot will be like, ‘Hey, we were talking about this yesterday, do you want to continue that conversation?’ ” 

Salamanca plans to introduce the robot and avatar in its high school AI and robotics courses, which use developed by Apple co-founder Steve Wozniak to prepare students for high-demand tech jobs. The district plans to expand it to high school students in other classes if the pilot is successful. 

Salamanca Superintendent Mark Beehler explained the district’s embrace of AI. “Many schools are taking the easy solution of simply banning it, but I have found that students will find a way around most rules that schools put in place,” Beehler said in a written response to questions. “I also believe it is critical for schools to teach proper use of technology, not simply exclude it.” 

During and after school hours, students will be able to use the avatar to receive help with assignments. Kiguel said students could upload photos of homework for feedback, ask the avatar to generate lessons on topics that interest them, or receive real-time translations in over 100 languages. Unlike other Realbotix robots, Salamanca’s robot will not have advanced features such as facial recognition and recording. 

The robot will also serve as a classroom resource. If a teacher loses their place during a lesson or needs a prompt on what comes next, Kiguel said, they can ask the robot for guidance because it has been loaded with the district’s curriculum. 

The products cost the district $57,590, according to  attached to the school board agenda. Kiguel said the price was discounted; the company’s website lists the M-Series robot, the model purchased by the district, at a starting price of $95,000. 

The plan has sparked debate online, with some calling it a forward-looking, innovative step for the district and others expressing concern about the broader implications of further integrating AI into schools, particularly in an underserved area. 

Salamanca is the only incorporated city in the United States located entirely within a Native American reservation. The school district serves roughly 1,300 students, including 32% who identify as American Indian or Alaska Native and 79% who are economically disadvantaged.Ìę

“We already have so many issues in our community, including environmental issues. I just don’t understand the concept of adding AI onto that,” said local parent Sierra Abrams, who learned about the technology in a . “Bringing it into the school district is contradicting everything we believe, in my opinion.” 

For the past few months, the company has worked to establish guardrails to ensure student safety. Kiguel said the robot and avatar were trained on the district’s curriculum and will always redirect the conversation back to education. References to suicide, self-harm, or other flagged terms would automatically alert school administrators.

In a demonstration for New York Focus, Kiguel told the avatar that he was being bullied at school and wanted to get revenge on the student. 

“I’m really sorry that happened to you. Being picked on and having rocks thrown at you is not okay, and that must have felt upsetting and maybe even scary,” the avatar responded. “The best next step is to tell a trusted adult at school right away, like a teacher, counselor or principal, and let your parent or guardian know, too.” 

Beehler emphasized the importance of safety and privacy, noting that the robot and avatar operate on a closed system that is not connected to the internet, and that Realbotix will not have access to personally identifiable student information. 

To avoid AI-generated inaccuracies, Beehler said Realbotix trained the robot and avatar to say, “I don’t know,” instead of generating fabricated or misleading responses known as “AI łóČč±ô±ôłÜłŠŸ±ČÔČčłÙŸ±ŽÇČÔČő.”&ČÔČúČő±è;

The Trump administration has promoted the expanded use of AI and technology in education. Early in his second term, President Donald Trump  establishing a taskforce to help integrate AI into curricula and teacher training. In March, First Lady Melania Trump entered a White House technology summit  and invited guests to envision a future where robots educate children in literature, science, art, philosophy, mathematics, and history. 

At the same time, there has been  nationwide, with parents forming groups to push for stronger oversight of AI, and clearer guidelines to limit screen time and ensure digital resources are used appropriately in schools. In May, the New York State United Teachers union called for strict  and screen time in schools, and earlier this month, New York City Public Schools Chancellor Kamar Samuels announced  until the school system finalizes guidance on artificial intelligence later this summer. 

The classroom marks a new venture for Realbotix, a Toronto-based robotics company formerly known as Tokens.com, that helped customers use cryptocurrency to rent “digital land” in the Metaverse. In April 2024, the company acquired Simulcra, the Las Vegas parent company behind RealDoll, which has spent decades  and later expanded into sex robots that remain on the market today. 

In a statement to New York Focus, a spokesperson explained that over the past two years, Realbotix has built a new team focused on education, health care, and wellness applications, and that Realbotix and RealDoll do not share employees, payroll, physical locations, or technology. She said Realbotix “is pursuing a transaction with a Nasdaq-listed company that is intended to separate the businesses at the ownership level” with completion expected by September. 

In recent years, Realbotix has worked to expand its presence in commercial settings. Still, it is best known for its “companion robots,” which are different from sex robots and intended to address what it’s described as a “loneliness epidemic.” Kiguel has previously said the company’s goal is to produce robots and AI that are “indistinguishable from humans.” 

Beehler, Salamanca’s superintendent, said the partnership with Realbotix began after a former colleague met an investor at a dinner and discussed the possibility of bringing the company’s robots into the education sector. He was initially cautious about the partnership because it represented uncharted territory for both Realbotix and the school district. But he said the company was receptive to feedback and willing to adapt its products for the unique demands of an educational setting. 

The superintendent acknowledged concerns about students’ increasing screen time and emphasized that AI and robotics should support, not replace, the human connections at the center of education. 

Beehler said the district will measure the success of the program primarily through qualitative feedback from both students and teachers. 

Ryan Schaaf, associate professor of educational technology at Notre Dame of Maryland University, said he was optimistic about the initiative, but cautioned that its success hinges on thoughtful implementation, with teachers actively monitoring and guiding students’ interactions with AI. 

Schaaf acknowledged the backlash against AI in education, but said ignoring the technology is a mistake. “It gives students a true disadvantage because once they leave school, they are going to be immersed in AI technologies.” Instead, he argued that schools should balance AI-assisted learning with traditional instruction to prepare students for life after high school.

State Senator George Borrello, who represents Salamanca and surrounding areas of Western New York, said the program could help level the playing field for students by providing tutoring access to those who might not be able to afford private options and sparking greater interest in STEM. 

Borrello said local leaders have long struggled with keeping young people from leaving Upstate New York after high school. He said investments in technology and STEM education could help show students they can pursue fulfilling, high-paying careers in tech while still building their futures in rural communities like Salamanca.

“One of the biggest things we see right now in technology is people fearing what the future will be with AI. Will it replace the workforce? Will it grow out of control?” he said. “I think this is a great way to get not just the kids but the teachers and the parents more comfortable with what the future may hold.”

This was originally published on .

Taking a computer class at her Fayetteville, N.C., high school, she handed in a paper with the offending word: .

“[The teacher] called me over, and she was like, ‘I don’t want to see you using AI,’ ” the 16-year-old rising junior recalled. If she’d asked, Reaves would have said she didn’t use AI to write the piece — that’s just the way she talks.

“So it was kind of tense,” said Reaves, “and I was definitely upset because she gave me a zero for that assignment.” 

High school students, steeped for nearly the past four years in generative artificial intelligence, face this reality daily, attending class largely without a set of agreed-upon guidelines. Over the weekend, a small group of students came together to hammer out a few basic principles for the technology, something akin to an AI Bill of Rights.

Sponsored by a group that includes an and a national , 100 students from all 50 states gathered here to debate a first-of-its-kind set of recommendations, to be shared with district leaders nationwide as AI proliferates rapidly in the lives of both students and teachers.

The nonprofit , which brought the group together, didn’t make the final recommendations immediately available — they’ll be released in coming weeks. But students told Âé¶čŸ«Æ· in interviews they are concerned about a host of problems: cheating, transparency, hallucinations, cognitive offloading, mental health and environmental factors, among others.

Two students from every state debated and ultimately approved the recommendations by a Senate-style roll call vote on Sunday at the Edward M. Kennedy Institute for the United States Senate, a museum and conference center that features a of the U.S. Senate Chamber where Kennedy served from 1962 until his death in 2009.

Despite the democratic nature of the proceedings, Day of AI refused to grant Âé¶čŸ«Æ· access over three days to any of the student debates, save for the closing speeches and final vote. 

The organization also clamped down on the text of the proposal, refusing to release a draft that students spent nearly three days discussing freely.

By the time of Sunday’s debate and final vote, nearly 100 students sat in replicas of U.S. senators’ seats, paging through their copies of the bill as staff members and security personnel closely guarded the proceedings. A reporter who picked up a stray copy of the bill on an upholstered bench had the paper quickly snatched away by a staffer, while another swept the area for more copies, gathering them up and tucking them under an arm.

During the proceedings, a few participants called the proposal flawed or incomplete and even, at times, incoherent — one student referred to it as “a block of Swiss cheese.” But in the end he voted “Yea.” In all, 83 students approved the measure, with 15 opposed — two students weren’t present for the final vote.

‘It’s an easy way out’

In the students’ conception, Al literacy instruction should be mandatory as soon as they start using devices in the classroom. The lessons should focus not just on appropriate academic use but on misinformation, plagiarism, bias in Al systems, privacy, environmental impacts and how Al really works.

Ethan Liu, 17, a recent high school graduate from Morgantown, W.Va., said the recommendations are “pretty fair,” especially the section on students. “We basically outline that AI, with the proper supervision, is a great tool for brainstorming and studying,” he said. Liu was one of several students who spoke to Âé¶čŸ«Æ· during breaks in the deliberations.

Liu recalled asking ChatGPT to create sets of practice problems. “That was one of the best ways that I would use to study for a test.”

But he said using AI to answer test questions or to generate art, for instance, is a bridge too far. The guidelines, Liu said, suggest that AI should be a tool “to help kids truly understand the topic without just spoon-feeding them the answer.”

In candid conversations throughout the weekend, students said AI is basically turning their school experience upside down, simultaneously supercharging their ability to learn while threatening to derail many of their classmates — and sowing mistrust between students and teachers.

“It’s an easy way out,” said James Baxter, 14, from St. George, Utah. “People like to know hard things, but learning it is a different story for most people.”

While he relies on tools like Google’s Notebook LM to help him create study guides, Dillian Campbell, 17, a rising senior in Fayetteville, N.C., said that when teachers pass out assignments, he most often sees classmates simply pull out their phones and snap a photo, asking AI to do the rest.

He recalled a math teacher telling students, “I don’t care if you use AI, but if you fail your test, that’s on you because you don’t really know the work.”

Jaelyn Jensen, 17, a rising senior in Lee’s Summit, Mo., noted that early on in discussions, the group concluded that while limiting AI use for young students is crucial, giving middle- and high-schoolers more autonomy with AI makes sense. Teens, she said, need “to make their own decisions, to learn from their own mistakes with AI.” But schools must also teach them how to use it responsibly “and let them know that it is something that is going to be in their future.”

Lucia Herrera, 17, a classmate of Jensen’s from Missouri, said AI has even crept into the speech and debate world, with debaters asking ChatGPT to help them respond to questions about their cases — this, in a world where critical thinking and original voice are a key to success.

While she can make a case for AI aiding in research, Herrera said, “I think it just takes all of the human out of [it].”

Bihmanji Acho, 16, a rising junior from St. Cloud, Minn., said she’s most troubled by the ways in which she sees AI undermining interpersonal relationships. She recalled attending a recent summit in which adults said they use AI “at like two a.m. as a therapist. And I just don’t 
” She paused for a moment. “It hurts my heart. I feel like it hurts my heart to hear that.”

While — or is it whilst? — many might believe that AI offers a crutch for low-skill students, Reaves said we should think again: Many of those who use AI most are “super-smart kids that are, like, just checked out.”

That loss of student agency might be among the most pressing problems AI presents, said Gyimah Whitaker, of the Decatur, Ga., school district. That results in fewer students able to think critically and take risks.

Whitaker, who chaperoned two Georgia students to the event, said her 5,400-student district proudly embraces and the rigorous curriculum. But AI, Whitaker said, undermines the values in both systems, driving students toward completion rather than helping them master difficult tasks.

Luckily, young people are, for now at least, fighting back. She noted that students in her district’s IB program “can be truly offended” if a teacher uses AI to grade their paper. They’ve even proposed a “three-strikes-and-you’re-out” AI rule — for teachers. 

To be sure, the students urged AI literacy for teachers, and Cynthia Breazeal, who directs MIT’s RAISE research program, said that’s no longer optional. 

Actually, the advent of generative AI may require more than just literacy. To truly combat the technology, she said, schools may need to adopt an entirely different pedagogical model, one that encourages students to more deeply engage with material.

She invoked legendary MIT mathematician and computer scientist , who co-developed the Logo programming language in the 1960s and founded what became MIT’s Media Lab. Papert helped popularize the term “” to describe the productive struggle learning something complex often requires. In his conception, children should be encouraged to build things and embrace difficulty, not shun or outsource it. Upon hearing a teacher describe the difficulties a child encountered programming in Logo once in the 1980s, he concluded, “I have no doubt that this kid called the work fun because it was hard rather than in spite of being hard.”

‘A Sputnik moment’?

Chase Johnson, 15, a rising junior in Kenai, Alaska, uses AI “a lot” in his personal life. These days, he said, it’s helping him develop a business to automate processes such as customer service. 

As if to prove it, he’ll pull out his phone to show off no fewer than 10 apps, all of which allow him to explore AI in different ways. “I do a lot of research,” he said.

But in his high school, he said, generative artificial intelligence is largely banned because of students using it to cheat.

Some of that responsibility should be borne by teachers, he said: In writing, for instance, instead of assigning essays that are easily outsourced to a bot, teachers should assign research projects that require students to present their findings in front of a class. “You can’t cheat your way out of that,” Johnson said.

Sarah Zahka, a rising junior in Weston, Fla., said she’s not above using AI when needed. “If I’m stuck on a math problem, I’ll ask it, ‘Hey AI, help me solve this problem step-by-step.’ ”

While she’s worried about AI causing students to lose foundational skills, sometimes she simply needs help brainstorming. In writing, Zahka said, she might ask an AI bot how to approach a topic, “but then I’ll write ethically on my own. Although I’m going to sound like a teenager writing an essay and I might not get the grade that the cheaters get, ultimately I’ll know what level I’m at.”

Jeff Riley, the former Massachusetts who now leads Day of AI, said he’s both excited by the technology’s possibilities — and a bit terrified. He predicted that AI could “kickstart the next revolution in education,” particularly with its ability to help teachers differentiate instruction and personalize learning. “But there’s some bad stuff with it too, right? Bias, voice clones, deep fakes, hallucinations, companions, energy concerns. The list goes on and on and on.”

While deadly serious, he said, those concerns aren’t activating enough adults — this should, by all rights, be “” pushing schools and communities forward, but he sees little urgency. He noted that in 2029, the Organisation for Economic Co-operation and Development’s international PISA exam will for the first time assess . “I can almost guarantee you right now: American schoolchildren, 15-year-olds, are going to score 30th in the world on AI literacy — on a technology that was largely created in this country.”

On the flight back to Atlanta on Sunday night, Whitaker, the Georgia superintendent, recalled that the two students she accompanied told her they’d split their vote on the recommendations, one approving the plan and one voting No. The one who opposed the measure told her the proposal “didn’t meet the mark completely” and needed more work.

“I think that’s exciting,” Whitaker admitted. “What excites me is that our children are wrestling just like we are — and that level of inquiry as a superintendent is what you want to see in students every single day.”

Anthropic, one of the world’s most prominent artificial intelligence companies, is launching a version of its AI-powered assistant Claude for teachers, entering a race by technology companies to infuse AI into education.

Anthropic boasts that its product can incorporate academic standards from all 50 states, and teachers can use it to help devise lesson plans, personalize instructional materials to students, and harness data to improve instruction, according to a company news release.

Claude for Teachers joins Google, OpenAI, and Khan Academy — among others — in marketing AI products specifically to K-12 educators. The new product launched Tuesday and is available for free for verified educators in the U.S. It will also be piloted in Detroit Public Schools Community District for a study on educator well-being and practice.

Claude’s formal arrival into the classroom comes during a complicated moment at the intersection of technology and education. The , the , and have all encouraged educators to adopt AI. But a is simultaneously gaining momentum, prompting some of the nation’s largest school districts to rethink how much time students spend in front of screens, as well as the contracts they’ve signed with huge players in ed tech.

Drew Bent, education lead for Anthropic, said that teachers using Claude’s educator product could, for example, pull in a student’s past assessment data and assignment data, along with past lesson plans, and ask Claude to build lesson plans for individual students based on that data — all while they’re sleeping.

In developing Claude for Teachers, Bent said Anthropic staff often heard that while teachers are already using AI to generate lesson plans, the plans generated were often detached from the content teachers actually needed to address. Anthropic’s tool will help teachers save time and toil less to improve student outcomes, he said.

“There’s a lot of evidence of what works well for teachers in terms of aligning with high-quality instructional materials, formative assessments, differentiated instruction,” he said. “But of course, if you have 30 students in your class, you’re not able to do all of that.”

Bent said Detroit was already using other Claude products, and in a “human-centric” way that impressed Anthropic, leading to the pilot program in the district that will start next school year. Anthropic will train teachers at a handful of schools in Claude for Teachers, and evaluate how the product may shape teaching practices in the district.

While AI companies have been eager to cement the technology’s status as a classroom staple, tech giants likely have a long way to go to quiet skeptics. Student-facing AI and so-called cognitive offloading, a reference to the reliance on AI to complete tasks instead of using critical thinking skills.

Bent emphasized that Anthropic is focusing largely on teachers, and that most K-12 students can’t access the company’s Claude assistant, due to an age restriction for anyone under 18.

Daniel Buck, a research fellow at the right-leaning think tank American Enterprise Institute, argued that if teachers outsource work to AI, “don’t be surprised when classroom community and academic outcomes rapidly deteriorate.”

Skeptics have also raised questions about student privacy in using AI, such as Claude.

Anthropic is working with the American Federation for Teachers on aligning the product’s privacy practices with what the labor union has said will become a “gold standard” in best practices around safety, according to the news release.

Among the privacy features in Claude for Teachers: It won’t use conversations between the AI assistant and teacher accounts to train its AI, student information will be protected in a manner built to comply with the federal law governing student privacy, and privacy terms of service are written without jargon so teachers can understand what they’re signing up to use.

“It’s important that Anthropic is committing to these principles in their new Claude for Teachers — a tool designed by and for educators to assist them instructionally and hopefully give them more time for the human relationships at the heart of learning,” wrote AFT President Randi Weingarten in the company’s press release.

Weingarten has been walking a tightrope when it comes to AI. She’s in elementary grades while promoting teacher training in the technology. Just a day before calling for the ban, she , an AI chatbot from Khan Academy, in action.

While AFT is working with OpenAI, Microsoft, and Anthropic on privacy standards and AI training for educators, it is notably not working with Google, .

Utah’s state education board recently made a deal with the tech giant , promising personalized instruction tools for educators.

It’s not yet clear whether one AI product reigns supreme in schools, but more teachers overall are using AI. Around 61% of teachers , compared with 32% in 2024.

Chalkbeat is a nonprofit news site covering educational change in public schools. This story was originally published by Chalkbeat. Sign up for their newsletters at .Ìę

“I’m not going to pretend like you aren’t,” he tells his students. “I know it’s readily available for most of you, if not all of you.”

A math teacher at in Fullerton, California, where he works with students as old as 19 who are struggling to get enough credits to graduate, Rabanera has watched AI creep into homework assignments over the past few years as students use powerful tools like ChatGPT and Google’s Gemini to race through assignments. 

It has forced him to change his approach. 

He has stopped sending home problems that can be lifted wholesale into an AI chatbot and pasted back into an assignment. Instead, he builds lessons around what students actually care about, creating, for instance, a unit on buying a car that weaves together calculating interest rates and monthly payments with learning about credit scores. He has replaced rote problem sets with one-of-a-kind poster projects and in-class design challenges. 

When Rabanera assigns practice, he often has students devise their own word problems around personal interests to prove they understand the underlying concepts.

He’s hardly the only one scrambling to try something new: Nationwide, teachers at all levels are rethinking, scaling back or, in some cases, abandoning homework altogether as evidence mounts that students who outsource their assignments to AI aren’t just submitting work that isn’t theirs. They’re surrendering the cognitive struggle that makes learning stick and makes homework, well, work.

New large-scale research suggests that teachers’ fears are valid. A led by Sina Rismanchian of the University of California, Irvine, analyzed 3.2 million student math problems on the digital platform over a decade and found that after ChatGPT’s release in late 2022, high school students spent 31% less time on word problems — the kind easily copy-pasted into an AI — compared with graph-based problems that required a hands-on interaction with the platform. College students showed a 27% decline. 

When students were tested under proctored conditions with no access to AI, the copy-paste behavior vanished. And when researchers examined whether students had actually retained anything, they found that the odds of correctly answering AI-susceptible word problems fell by 25% in the post-ChatGPT years.

“Students are using AI a lot,” Rismanchian said in an interview. For those who do, “it’s coming at a cost for their learning outcomes.” 

The College Board last fall that the percentage of high school students who said they use AI tools for schoolwork grew from 79% in January 2025 to 84% in May 2025.

In a , 37% of K-12 principals said students were using AI for homework help, slightly higher than the percentage who said students were using it to help draft essays.

John Singleton, an associate professor of economics at the University of Rochester and a co-author of the study, said the finding “certainly requires a rethinking of what the object of homework is.” For him, assigning short writing assignments to his college students is “insane these days, because you’re going to get back 25 AI-generated short essays, and so it’s really not gauging comprehension. It’s not even doing the work of forcing the student to engage with the material, because they can put it into the AI.”

Conscientious instructors are drafting AI policies that they post to class syllabi, he said, “but I think the temptation is there.”

Talking to colleagues, Singleton said, “Everyone feels sort of bewildered about whether they’re doing the right thing.” Moving toward presentations and oral exams make sense, but giving up traditional writing assignments as a way to assess student thinking, he said, “is too bad, in some ways.” 

The irony of this moment is that AI was supposed to offer students a , capable of explaining concepts, adapting to individual learners and helping them work through difficult material at their own pace. Instead, many educators say, for a significant share of students it has become the most efficient cheating device ever invented.

“There’s a zillion people that are trying to come up with these guided learning environments and Socratic tutors and stuff like that,” said Justin Reich, director of MIT’s Teaching Systems Lab and host of the AI-focused podcast “.” “And I’m just like, ‘Guys, you’re putting the “Carefully teach me this stuff” button directly next to the “Do everything for me” button.’ ”

Reich has spent years studying why students cheat. When they’re being honest, they typically tell researchers that the assignment didn’t seem worth their time, or that they ran out of time. They felt pressure to perform or, in many cases, they found themselves stuck on a problem with no other help in sight. 

“What’s new now is that, with all the gen AI stuff, the cost of taking a shortcut is zero,” said Eric Cosyn, a researcher who co-founded the .

Ashley Kannan, who has taught eighth-grade U.S. history for 30 years in Oak Park, Illinois, said that if schools continue to go down the same path of assigning work and expecting students not to be tempted to take shortcuts, “the war is over — we’ve lost.” Classrooms, he said, will be left in “a race to see who can plagiarize and cheat the best, and who has the resources to do so,” a dynamic he calls a losing bet for everyone. 

Start your homework in class 

In interviews, many educators and researchers were quick to point out that AI didn’t invent academic dishonesty. 

Denise Pope, a senior lecturer at Stanford’s Graduate School of Education and co-founder of , a research and school reform project, has been tracking student cheating behavior for two decades. Long before ChatGPT, she said, copying a classmate’s homework was consistently the most commonly admitted form of academic dishonesty.

The group’s latest academic integrity study, drawn from nearly 30,000 high school students, shows that this pattern still holds: 32.7% of students reported copying someone else’s homework at least once in the past month, a figure almost identical to the share who reported using AI as an unauthorized aid: 32.8%.

Students, Pope said, are simply swapping out one shortcut for another.

“This sort of hand-wringing that AI is changing homework like never before is a little bit off,” she said, “because there were high amounts of copying and cheating homework long before.”

All the same, Pope’s team has surveyed more than 100,000 students since November 2022, and the results are unambiguous: They’re using AI to do homework. Many don’t frame it as cheating, making the case that consulting an AI is no different than asking a parent, calling a tutor or typing a question into Google. Echoing Reich’s findings, she noted, “Some of them are saying it’s another piece of technology that helps us when we’re stuck.”

Teachers, naturally, see it a bit differently. A Challenge Success survey of 678 faculty and staff members found that the most pervasive concern, raised by about 58% of respondents, wasn’t cheating itself but the erosion of critical thinking. Teachers complained that students aren’t developing the intellectual stamina that hard problems require. “If school is about skills and not content,” one teacher wrote, “ChatGPT takes away critical thinking skills at a time that we are supposed to be teaching those skills to the students.”

Pope said her group is hearing from teachers that they’re afraid to send homework home “because it’s even more clear that there’s this ‘Easy’ button, and students are not having the productive struggle that they need to really learn the material.”

She recommends rethinking homework, starting with what she calls a homework audit — a systematic review of assignments to ask whether they actually require a student to do the intellectual work required. Teachers should also be able to tell if the work was done by the student or by AI.

“Start your homework in class,” she advised. “You will get a really good picture of who understands what you’re asking and who doesn’t by looking around and seeing what happens in the first 10 minutes — one kid is done and one kid is still stuck.”

Kannan, the Illinois history teacher, has landed on a similar idea, built around conversations. He still assigns a version of the same paragraph students have long written to identify a historical figure and place them in context — but the process now unfolds through one-on-one conferences rather than solo writing. Because of the conferences, the writing looks different. 

The goal, he said, is to locate the assignment “in the hearts and minds of a student” rather than in a generic prompt that AI can complete on command. “I think that there’s a way to personalize rigor,” he said. “When students, when young people feel that something is personal to them, they do come alive.” 

Kannan said this kind of individualization isn’t new — special education teachers and speech-language pathologists have practiced it for decades. “All I’m suggesting is that there are pathways that we know work. Why not bring it into the mainstream classroom for every student?”

‘How could this be stronger?’

Researchers are also grappling with the limits of what they can measure. Self-reported cheating data, as Rismanchian’s paper notes, is “the least reliable way to measure anything.” In his own earlier research on 70 undergraduates, more than half of students who were directly observed relying on AI denied using it. “We were actually observing this copy-pasting behavior,” he recalled.

A group of McGraw-Hill researchers co-authored the Rismanchian study, and Dylan Arena, the publisher’s chief of data science, said solving the AI cheating problem has two prongs: Better detection helps, but it’s insufficient without a cultural shift inside classrooms. Students who believe their sole obligation is to produce a completed assignment, he said, will always find the path of least resistance. 

“There are kids who are here thinking, ‘I need to punch my ticket, I need to get this thing done,’ ” Arena said. “What’s the most expedient way to do that? Hand it over to this tool.”

But he suggested that something deeper is actually happening as AI colonizes students’ thinking: They’re losing their tolerance for “not knowing” something at any given moment, for what he calls the productive discomfort of sitting with a hard problem before the answer becomes clear. “It used to be that we would spend weeks not fully understanding something, and we would read, and we would write, and we would talk, and we would try, and we would write drafts — and they wouldn’t be quite right. But there is now an expectation that I either instantly know what I should do, or I should turn to a tool.”

A few teachers are experimenting with a more informal version of that transparency, built on relationships rather than documentation. 

Kannan described pulling aside a student last year whom he suspected, based on months of conversation, of leaning on AI for most of his schoolwork. “I knew that because I was taking the time to talk to him,” Kannan said. 

Rather than report the student, he asked him to run his own writing through the same chatbot he’d been using, in a bid to “reverse-engineer” and improve it. “Let’s actually ask questions to ChatGPT about how this could be stronger? How is this weak?”

Kannan has since built that move into his regular teaching: After students draft work with AI’s help, he sometimes has them ask the tool to critique the output, with students in effect “co-designing” assignments rather than simply handing them to a chatbot and turning in whatever pops out.

In the absence of such guidance, he said, students will do what they must to complete assignments. He recalled a student who’d been assigned an essay on the American dream by an English teacher. “There was really no instruction as to what that was, and she said, ‘I carried on a conversation with Gemini about what the American dream was, and that helped me understand it more.’ Given the demands of what the student was facing, she used AI as a partner, as an opportunity.”

, a longtime education researcher who has studied homework, noted that teachers have been assigning less homework for at least a decade — and that the rise of AI might reduce it further if teachers lose confidence that take‑home work is genuinely done by students. But it could also work the other way: If teachers believe that AI is a kind of all-purpose helper, they could actually assign more homework because they believe students “are going to be helped out and kind of semi-tutored,” he said.

Many school districts are trying to reframe the relationship between students and AI from the ground up. The Laguna Beach Unified School District in California, working with researchers at Stanford, found that AI policies had generated a culture of suspicion in which teachers spent their energy trying to catch cheaters — and students felt guilty for using AI.

Michael Morrison, the district’s former chief technology officer, called it “the absolute worst culture that I can think of.” 

In response, the district developed an add-on tool for Google Docs called , which asks students to disclose exactly how much and in what ways they use AI on a given assignment — a kind of nutritional label for AI-assisted work. Surveys suggest students generally use it honestly because the tool gives them something they didn’t have before: a sanctioned way to tell the truth.

Michael Keller, Laguna’s director of social-emotional support, noted that the district is already rethinking homework altogether for high school students, since 70% are athletes who devote an hour or more each day to practice and training. 

He sees the AI initiative as part of a broader commitment to treating students as full partners in their own learning. Monitoring how students are using AI, he said, is not the point. “We really view it as our moral obligation to make sure that we put trust and supportive relationships as the foundation to their learning experience,” he said.

For MIT’s Reich, transparency is necessary but not sufficient. The deeper challenge is motivational. “Kids are just natural boundary pushers,” he said.

And though they may not be able to articulate this, “the boundaries are what make them feel safe and loved and cared for.”

The problem, Reich argues, isn’t that students are weak-willed or morally deficient. It’s that the incentive structure of homework — grades for completion, not for thinking — has always been fragile. AI has simply exposed that fragility. 

“It’s not like homework is perfect,” he said. “Any teacher will tell you that some of the assignments are dumb or don’t work. But across the tens of millions of minutes of stuff that we ask kids to do in the afternoons, some of it’s got to be useful. And if we turn that spigot off, there’s just going to be less learning.”

In the finale of Class Disrupted’s seventh season, Michael Horn and Diane Tavenner reflect on the season’s conversations about artificial intelligence — from how the tech is shaping school models to new tools. In a “no-holds-barred” conversation — they talked about how much of the education sector is still operating under the traditional model, the challenges of creating learning experiences outside of that model, and the tensions they see between innovation and entrenched systems. Throughout the conversation, they touch on issues like policy backlash against technology in schools, the value of outcome-based contracts, and whether microschools or low-cost private schools can actually drive large-scale change.

Listen to the episode below. A full transcript follows.

Diane Tavenner: Hey, Michael.

Michael Horn: Hey, Diane.

Michael Horn: We’re in person.

Diane Tavenner: I know. 

Michael Horn: Two episodes in a row.

Diane Tavenner: It’s amazing. For those who are only listening, we actually are sitting together here in Boston, which is super fun. It’s good to be on this coast with you. And for me, the best part of this is, I got to have family dinner last night at your home. And I woke up this morning thinking about it. I just felt really happy. It was joyful. I think you know that my favorite night of the week is family dinner night at my house.

Michael Horn: Yeah.

Diane Tavenner: And I hustle home, so I don’t miss it. And it was so fun to be with your girls and with Tracy. And I was thinking a lot about those few hours and, like, the learning that was happening at that table. I mean, we got to play this board game that your girls had, like, invented, and the dialogue and the conversation and the curiosity and that, you know, they were making their own lunches, which called back to, like, Rhett cooking.

Michael Horn: Yeah. But he was doing that from a young age. I remember


Diane Tavenner: Yeah, yeah, yeah.

Michael Horn: Preparing meals and shopping and everything.

Diane Tavenner: Totally. And so that was really fun. And to be back in that sort of younger age, because mine are older. I should say, the thing I kept thinking is, like, this is what learning is, and what if this is what it felt like? I wish everyone could have this experience.

And yeah. So that was good.

Michael Horn: No, it’s really cool. I will say. We also then had a conversation this morning about 
 She was like, so what night is family dinner night in the Tavenner household? And should we think about creating a similar structure for our girls? So you being there also caused us 
 because I think it’s intentionality, also.

Diane Tavenner: It is.

Michael Horn: Right. And that intentionality we both brought to raising our kids and now thinking about reinventing schools. And I think it was interesting because in our previous episode, we were live as well.

Diane Tavenner: Right.

Michael Horn: We were at the ASU-GSV Summit with Reed Hastings. And it was interesting to hear Reed, I think, for the first time in my memory, and I think that’s what he talked about. We actually need to reinvent the classroom model, the school model itself. We can’t just be tinkering, if you will, toward utopia or layering over here and whatever else. That’s obviously the tune we’ve been singing for most of our professional careers.

Diane Tavenner: Right. But it was fun. It was exciting to hear him talk about that and reflect on the work that he’s done in different, you know, elements of education. And to really start to zero in. We have to work beyond just the classroom if we really want to transform the experience for young people. And so, I mean, I guess it always feels good to be confirmed and validated.

Michael Horn: Sure. We feel like geniuses now.

Reflecting on mid-season insights

Diane Tavenner: Yeah. So I think our plan for this today is a callback to mid season, where we did this in person also kind of mid season. Oh my gosh, we’re learning so much. Let’s get together and just reflect and process and talk about what we’re learning after that first half of the season where we were really talking to sort of thought leaders and people who are thinking big picture and who understand AI and in AI, and so we thought we’d do that again, where the back half of our big AI exploration season has really been with practitioners, I would say. And so what are they? What are we making? We had all these really fascinating conversations. This is our first time to really sit down and, like, sort through them and process them and understand what we heard. And then we’ve also been thinking about next season, and so we’ll share a little bit of that

Michael Horn: Tease a little bit. It’s been interesting, and I’m glad. Process is the word that I’ve been thinking about because I think when people appear on our podcast, this sounds silly, but it’s not an endorsement of their model or their tool or whatever it is by us. It’s a genuine learning opportunity for us. And obviously we were both interested in new school models.

Diane Tavenner: Right.

Michael Horn: Which was three episodes that we had. And then we were interested in the ed tech tools that are coming up and using AI in different ways. And we were trying to process. We had this framework that you introduced in the last time we were in person in Cambridge or in Boston about the sort of horizon one, horizon two, horizon three, schooling models, and the way I think we’ve sort of categorized it, there was H1 is sort of the industrial model that we know, and you had H3 as sort of this post industrial model. And H2 is sort of this hybrid that is elements of both, maybe, and so forth.

Diane Tavenner: I love that metaphor of the steamship that is outfitted with sails still because it has to be running both because steam is not reliable enough. You can’t really get all the functionality you need and trust it. So you have to still bring the H1, if you will, into it. I’m glad you brought up the framework because that really shaped how I saw the second half of the season and had all those conversations. I’ve actually been thinking a lot about that framework and how my understanding of it is sort of, I don’t know, maybe becoming a little bit more nuanced as we talk with people and think about these different models. And so that might be a place to start.

Michael Horn: Yeah, I think let’s dig into that because. And you can take it wherever you want. I think it’s a framework that I have found both useful, and I’ve been struggling with, like, what is this H3 really gonna look like? Where does it really depart from the grammar of schooling as we’ve known it? And do we really think we’re gonna actually get there? Or are there gonna always be this sort of H2 pulling us some of the elements. And then I guess I’ll say the last thing, which will probably come up as we talk about the school models that we had on the podcast. Where, you know, where are parents in all this? Like, where is their headspace? ‘Cause, like, what we want, where parents are, where schools are. And I, for those that can’t see my hand gestures, I actually think that’s the continuum at the moment. Like historically, I think parents were more conservative than the schools, I think that may have flipped.

Diane Tavenner: Well, and I think there’s some evidence of that with the growth in microschools and sort of alternatives like, people really sort of self bundling, if you will, you know the homeschooling.

Michael Horn: I was shocked by the way from the homeschooling I read this morning in Âé¶čŸ«Æ·. 10 million, I know. 10 million families. They reported from this RAND survey that Johns Hopkins had done. I think it was Angela had done there. And that’s a big number.

Diane Tavenner: It’s a big number. And I think it speaks to this idea of where are parents and I agree with you, they’ve historically been more conservative. I mean, I wrote prepared to try to, you know, I call it a love letter to parents, like to introduce them to some of the ideas of what’s possible. Because my, my feeling was like they want one thing for their kids, but they don’t know how to get it. And so they kind of double down on, you know, the existing model, if you will. I actually think we’re starting to see, at least for some, a break from that. And not just a small amount, but, you know, a growing.

Michael Horn: In some cases quite big. Yeah, in some cases there’s, there’s steps and I actually think that comes up. I don’t know if you want to stay on the H1, H3 or go into the model.

Diane Tavenner: I think what I would say is just a couple of reflections and observations, how I’ve been using that framework in real life. So, one, I find that it is helping me be engaged in conversations with educators and developers. All the people who are in, who care about this work and who are working in it, who are thinking about AI in that if I can just set the framework out up front and say, look, like my passion is developing the post industrial model and H3, and honestly I don’t think I’ve seen it yet, and I don’t think it exists yet, and that’s not a bad thing. That’s fine. But let’s not pretend that certain things are H3 or that they are a real new model. We might have that conversation in the models conversation when I don’t think they are. And you brought this up the last time we talked, like, we also need tons of kids are in H1, they’re going to be in H1 for a while.

How do we use AI to make those experiences better? So I think when I’m able to say to people which conversation are we having? Where are we working right now? And the key for me has been how do we not constrain or compromise work in developing the post industrial model or the H3 place by working on H1? And that is a real risk, I think, because, I mean, there’s so many, like, elements of H1 policy and norms and practices and tools that just codify and solidify that model and then therefore prevent unintentionally the invention of new models.

Michael Horn: And we talked about that some in the midseason in terms of you named assessment and special education in particular in those. I think there’s a lot more and frankly —

Diane Tavenner: I’m going to add a new one.

Michael Horn: Since we recorded, I think we have seen the growth, the fastest growth in my memory of a series of policies around screen time. Cell phones were already emerging but now into screen time in general that I don’t love but well intentioned in H1 that are like diametrically opposed to — I won’t call them H3, but I will say, like, these new models of schooling that are appearing by education entrepreneurs in sort of this H2, H3 nebulous area.

College and career readiness concerns

Diane Tavenner: Right. I couldn’t agree more. The other one that I would add is the place that I’m working every day right now. So let’s call it the sort of college and career counseling and readiness world, if you will. And it’s just so clear to me that, you know, the goals in that area by the vast majority of people are to improve college and career counseling and readiness in the H1 model. And I keep saying, like, if we’re successful at that, don’t we just codify this entire system? Don’t we reinforce it and bolster it and prevent ourselves from moving to what I believe is and certainly what we’re trying to do at Futre, which is life navigation, whole human development, like enabling young people to emerge into a life of flourishing, to launch into a fulfilling life, not just get accepted to college or you know, take that next sort of educational step if you will, which is what is happening right now, I think, in H1. And so just the, the — I don’t think I’ve convinced a lot of people of that.

Michael Horn: Yeah, so. So it’s interesting. I think it is a risk, one, but — so I agree with you, no surprise. But two, I’m going to take a theory that we use in my class and from the Clay Christensen playbook, if you will, value networks, and Tom Arnett has been writing a lot about this at the Christensen Institute. And basically the way I’ll say it is this.

Let’s use an analogy. When RCA and these large vacuum tube consumer electronics products, they got disrupted by Sony and these transistor LED products, it wasn’t just Sony replacing RCA, it was also all of the component suppliers of RCA got disrupted by a whole new batch of component suppliers because they were completely plug-in compatible with the old and vice versa. And then it wasn’t just that, it was also retail got completely disrupted. So RCA sold through appliance stores that made their money not by selling RCA products but by repairing.

Diane Tavenner: Of course.

Michael Horn: Right. Because these vacuum pipes would blow out. Sony got sold in discount retail. Target, Walmart, Kmart, all birthed in 1962. And so the shorthand that we used to say at Entangled, where I spent some time, was systems disrupt systems. And so in some ways, like, you’re building the new system now. I will tell you, the struggle I have when I teach in the class, which is we use a few school networks as like different, different versions of this. So we have Virtual Learning Academy, Charter School in New Hampshire, Big Picture Learning.

Like a few of these that Tom has written some case studies on, and every single one of them at some point pull back into the existing system and like, Tom’s answer to this is always, like, well yes, we inhabit Earth.

Diane Tavenner: Yeah, it’s the gravitational pull right?

Michael Horn: Like we are like, we are not completely free on Mars yet. Sorry, Elon. But like, so where is the limit of exchange of these systems and where are crosswalks not distorting these new things and where are they, to your point, actually codifying like grooves on a train track that are impossible to escape.

Diane Tavenner: So that’s an interesting segue into talking about the three models that we talked about, I think during the season. Because, you know, we specifically wanted to talk to Alpha School because like, everyone was talking about Alpha School. There was a lot of sort of PR and marketing out there about them. But we kind of wanted to really get under the hood and understand, you know, was this new. A new invention or not? And what was going on. We wanted to talk to John Danner, who’s the co-founder of Rocketship and now starting Flourish, literally, you know, AI Native designed as an AI Native model. So what does that mean?

Michael Horn: By the way, he is the only one that’s actually AI Native.

Diane Tavenner: Correct.

Michael Horn: Think about it. Because Alpha dates back to 2014 or whatever.

Diane Tavenner: Yeah.

Michael Horn: And then Summit, of course.

Diane Tavenner: And then we talked to Summit about, you know, their vision to create the next generation of model, which would be an AI Native. So I’m curious.

Michael Horn: Play, you. Go ahead, go ahead, ask your question. Ask your question.

Diane Tavenner: Well, what’s your impression of those three? I mean, how would, how did you come away from, you know, what were you believing that they were actually inventing Horizon 3, AI Native, you know what is your takeaway?

Discussing H3 framework development

Michael Horn: Yeah, it’s a good question. I think this is where I’m struggling with the framework because I still can’t completely imagine all the features of H3. And so, I think, let’s tease this for next year’s. I think we should like construct a little bit more clarity around what really is H3 and what are the distinguishing characteristics? Because I think, in my world, I sort of, like, I don’t know, Montessori feels like it has a bunch of these. And if I look at the Alpha model, I actually see some tiebacks to Montessori. I see some tiebacks, ironically, to big picture learning and things like that. Let’s put that aside for a moment. In some ways they feel like the closest, sort of like pulling from both sides of it in very clear ways.

Flourish to me felt like the closest to what I might imagine H3 in a middle school.

Diane Tavenner: Yeah, I think it has the potential.

Michael Horn: We don’t know yet, right? And I appreciated John’s honesty with where they are. And then I had this — I’ll tell you my take on Summit, let’s start there. Yeah. Because that’s your baby.

And you asked this question before we recorded. So no one knows you asked me this question, but you said, like, I’m wondering how you’re going to respond and react to this. Because in your mental model, Michael, to disrupt yourself, you need to have a completely separate, you know, et cetera, et cetera. And then, like, the old migrates out to the new, and that’s not what they’re doing. So I had this thought though, as I finished. ‘Cause I didn’t know what to expect. I came in with it a little bit, like, hackles up a little bit. And then when we finished it, my reflection was this, which is when there’s another way to survive.

Disruptive innovation that we don’t talk about that often, which is because I think it’s less proven. But the way is you are so clear on the job to be done that you do your mission, what you don’t do, your priorities, what you don’t prioritize, et cetera. That when new technologies come in or new features, you can swap them out very cleanly. Because everyone in the organization understands this is what we do.

Diane Tavenner: Interesting.

Michael Horn: So let me give you an example outside of an industry, and then you can — I’m going to let you fill in the blanks from it. So there’s this retail store that came in at the quote unquote “low end” that has never gone up market and never been disrupted by online retail. And it’s called IKEA. Probably know it.

Diane Tavenner: My gosh, my children love it.

Michael Horn: Right. And IKEA, we have become convinced they do one job to be done really well, which is, I need to furnish this apartment today.

Diane Tavenner: Yeah.

Michael Horn: Like I — yeah. And they’re not selling heirloom furniture. They’re not trying to go high end. Nor are they threatened, though, by discount furniture or Wayfair or any of these folks. Because the job to be done is so distinct and clear. And they lay out the entire store like you’re walking around the apartment, and you fuel up midway through Swedish meatballs, right?

Like. And so a new technology comes along and their simple question always is, does it help us get the job to be done?

Diane Tavenner: Yeah.

Michael Horn: Accomplish it better, or is it irrelevant to us? And if so — so take China, for example.

Diane Tavenner: Do they ever ask if that job still needs to be done?

Michael Horn: That’s probably a good question, but I think you probably have to.

Diane Tavenner: I think that they probably keep finding that job keeps needing to be done.

Michael Horn: I think it’s still relevant. And they probably say the market’s pretty big.

Diane Tavenner: Right.

Michael Horn: It’s a good question. But so in China, it’s interesting. Like, in the U.S., we drive large cars, so we go. We pick out the furniture with the stickers, and we go down to the bottom thing and we pick off these flat carts, and we put them on that cart and we check out and we drive off in our cars and we assemble them really nicely. In China, they don’t have big cars. And so IKEA will deliver it to your house same day.

Diane Tavenner: Which is key.

Michael Horn: Super integrated around the job, and they can make those sorts of decisions because, like, does it help us get the job to be done or not? Context changes fine.

Diane Tavenner: Right.

Michael Horn: We just know what our true north is.

Diane Tavenner: Such clarity.

Michael Horn: Such clarity, right? And so it prevents them from going up market and specificity. Right? And you can imagine all the ways that trickles down, I think. And so my read when we were listening to Summit was like, now, you could argue your mission changed, right? When you moved away from college. But I don’t say, not really.

I would say it was more. You actually were really clarifying what it meant to educate a prepared human.

Diane Tavenner: I agree. I agree. Sharpening, clarifying.

Michael Horn: Sharpening, clarifying the metrics

Diane Tavenner: And talking about it in a way that was more resident.

Michael Horn: Yeah, to the families you served

Diane Tavenner: Yeah.

Michael Horn: So I guess my point is, like, when I heard what y’ all are or what Summit is doing.

Diane Tavenner: Not me, the team at Summit, big fan.

Refining educational processes with AI

Michael Horn: Katie and Dan and the whole team at Summit, big fan. They, like, they see, you know, Summit learning got ripped out, and they’re like, okay, we can design this new technology with AI or partner or whatever it is to accomplish what is the school that Dan walked us through very helpfully, I thought, on that episode, and, like, they could reinvent expeditions. But again, it’s not changing the DNA and the mission and clarity of the place. It’s more like, how do we sharpen the processes and better get them done right now with the tools available. I think that’s very possible

Diane Tavenner: Without being disrupted and to be very relevant. And the adoption of that and that technology.

Michael Horn: Job to be done is still, like, the prepared thing has not changed.

Diane Tavenner: No, it has. If any, it’s maybe more so, more relevant. Oh, that’s such an interesting.

Michael Horn: Here’s where we get to say we didn’t tell each other our talking points.

Diane Tavenner: We literally wait to talk about these things. That also brings me probably back to Alpha, because using that lens, I’m trying to think if I feel like Alpha has that level of specificity and clarity in a job to be done. Or if they dont?

Michael Horn: I’m not sure they do.

Diane Tavenner: I don’t think they do? Right?

Michael Horn: I’m not sure.

Diane Tavenner: Yeah. Because their model feels a little bit more like this. Like you said, there’s a little piece from here and a little piece from there and a little piece from there, and they’re kind of stuck together. But it’s not clear what they’re adding up to, necessarily.

Michael Horn: I don’t disagree with that. To me, not just from our episode, but in subsequent conversations and things of that nature, what I’ve taken away from Alpha 
 Okay, let me say it differently. I think the thing that probably bothers you the most is the disconnection between the morning academics and then the life skills learned in the afternoon, is my guess.

Diane Tavenner: For sure. Just this sort of absence of intentionality. And, like, I just don’t understand how you have.

Michael Horn: How you have been separated.

Diane Tavenner: Yeah.

Michael Horn: And so my takeaway is I don’t disagree. For my kiddos, I would agree as well. And I also think relative to the current system 
 

Diane Tavenner: Yeah.

Michael Horn: It’s still significantly better than what most families would be getting on both dimensions, I think.

Diane Tavenner: Yeah. And then you have to talk about ROI. Like, is it $75,000 better?

Michael Horn: Well, so the the price point, let’s actually talk about that. ‘Cause I agree with that.

Diane Tavenner: Yeah.

Michael Horn: And it’s not what I would price it at, but I also cynically understand pricing at that high. This is a higher education thing that I hate. Right. I’d always. You know, Western Governor’s University is not seen as a prestigious university because they charge $6,000 or whatever, but they’re amazing at their job to be done. But the game is, like, if you come in at this high price point, in the absence of metrics that people universally really can understand and agree on, they equate price to quality.

Diane Tavenner: Yeah. It’s a luxury brand. It’s a luxury good. And so they burst onto the scene and sort of signal to people with lots of money they did break.

Michael Horn: I mean, we’ve been frustrated. We haven’t been able to break into the conversation. We said that up front in the two hours. I think that’s been a way for them actually to do it. And so I’ve sort of like, I’m living in this dual world where I’m like, it’s not how I would do it. It’s not. And I get it. Like, I get what it’s accomplished for them.

And there are many parents for whom like they’re really desperate for. They’re running toward it or maybe running away from something. And that’s more I think, the truth.

Parents exploring educational alternatives

Diane Tavenner: Those are the conversations I’ve had with parents, you know, when parents are running away from something to a variety of different choices depending what’s on the table for them. Which again, I always take as feedback as someone who wants to be in the system and serving the majority of young people. I’m like, huh, you gotta pay attention to that when people are running from what you’re offering and you know, where are we? Where are we not meeting their needs? And really, to me, this is what it means to co-design and work with communities to create this public good, if you will. The other thing that came up for me in the models — and this is on Flourish, but it’s come up a lot this year. And I’m curious what your thought is. You just talked about how the conventional wisdom is that, you know, to disrupt yourself you have to sort of, you know, wall off that new innovation, which was certainly the approach I took when I was leading Summit. I’m glad to know there’s another pathway. I think I talk to a lot of people right now who view microschools as that possibility.

I think that’s what they’re thinking. So by people I mean, you know, whether it be school districts who are thinking about starting microschools, where they are going to do their innovation there, charter school networks who are thinking about doing their innovation there. Certainly Dan. Or yeah, John. John Danner is rethinking about, you know, starting as a microschool. And he talked about how he thought that the scaling there would be easier and whatnot. I’m skeptical of the microschool as the place where you can actually design a new model.

And the reason that I’m skeptical and I’m curious what you, I actually don’t think at the micro level you work through so many of the elements and principles and systems of an actual model. Like, I just can’t imagine serving 50 million kids a year in microschools. Like, I just don’t think that adds up to a system of educating the number of young people we need to educate. But I could be totally wrong.

Michael Horn: Yeah.

Diane Tavenner: And if that’s the case, like, how do you make the — ? What do you actually learn and develop in a microschool that is transferable to a larger school or a larger system or a larger model? I feel like there’s a lot of gaps there.

Michael Horn: That’s interesting. A lot of thoughts, so we’ll go through them. So, one, and then push back where you — because I don’t — so on the district and charters using microschools, that I would say is promising, but I’m unconvinced. And the reason I’m not convinced is I still don’t see the mechanism.

Let me say it this way. Districts for years have had alternative schools to serve, people who dropped out of the system. Great area of non-consumption. I’ve written a lot about how I thought it could be a disruptive thing. But the thing that they don’t do is disruption, has to grow, and people have to migrate out to it. And no one migrates out to it.

Diane Tavenner: No.

Michael Horn: Right? And I think there’s a lot of reasons for that. But I think part of it is the business model. The charters are a little different, but the business model district is the district, it’s not the schools.

Diane Tavenner: Correct.

Michael Horn: And so schools take on the thing of pilots.

Diane Tavenner: That’s a bigger system. And I’m like, how does that microschool in any way help us understand and transform the system?

Michael Horn: Yeah — no. And so I’m — I don’t know that the dual — I am convinced that the dual transformation strategy works there, even though I’ve recommended districts do this because I don’t see another avenue for them. Let’s leave aside charters for a second. I guess I would push back on the — well, maybe I would relabel it from microschools.

I’m moving away from that phrase because I think “micro” implies small. And if I look at what I think the most promising disruptive innovations in this new schooling space are, I don’t think the feature is necessarily small.

Diane Tavenner: The size.

Michael Horn: Yeah. I think the feature is that it’s a low-cost private school. And so, like, Flourish may end. It’s small at the moment, but it may end up being several hundred students. I could imagine maybe John would say no.

Diane Tavenner: Yeah.

Michael Horn: Con Lab school was originally a microschool. It’s like 300 kids or something like that.

Diane Tavenner: Right.

Michael Horn: So it’s growing. You wouldn’t call that micro anymore.

Diane Tavenner: I’m not trying to call it low cost either.

Michael Horn: Well, that’s fair. That’s fair also. Right? So — but I guess the point being, that’s a good push, by the way. But the — I guess I am somewhat optimistic still that like we’ll have hundreds and thousands of these different schools, and some of them will be chains, and some of them will be sort of your local coffee shops. Sort of.

Challenges of educational system changes

Michael Horn: If you think about the mix, and I do think families are potentially going to move out of the district system into these new things. Now I have two thoughts on what you’re saying, which is, like, how do you do all these people like the size and complexity? And I guess I have a couple thoughts. One, I think we don’t. I don’t think we can know the answer to that until they start to grow in size and complexity and solve the next problem. And there’s a tendency of people in education to want to design the system top down.

Diane Tavenner: That’s so true.

Michael Horn: And I don’t think we’re going to. I don’t think that’s how it’s going to work. So that’s one. But the second one is, I think I can imagine a lot of families in more bespoke smaller communities like these 10. I mean, if it’s truly 10 million of 50, like that’s already 20% that are DIYing. And is the future system 100%? I doubt it. But it could be 70.

Diane Tavenner: What’s interesting to me about that is the DIYing. And this was the point of that article this morning, is literally people. It felt like summer to me when I was like organizing all these summer camps and these activities. And so these parents are like assembling and essentially duct taping together. The, you know, the microschool part tending to be more of like online digitally driven learning.

Michael Horn: Or it’s like there are two days a week there, real world experience. The set of three things.

Diane Tavenner: Yeah. And so in my mind I’m like, okay, that’s a parent sort of cobbling all those things together. But if you’re a microschool, that the parents are going to you because they’re expecting you as a school to put all those things together. How do you do that? If it really is about small, how do you have all those real world experiences that we’re imagining for a small number of kids? I just don’t understand how it works.

Michael Horn: No. And this is where I think we agree, which is, I personally think, the upmarket trajectory of the sector because disruption comes at the low end. It doesn’t serve most people and has to serve more complicated use cases, which is what I think you’re describing. And so I don’t know what that looks like. But my current thinking is maybe all these microschools plug into what’s a community center and it’s actually kind of permeable between them. I think your argument would be H3 is actually the operating system underlying how these all connect to each other, which we haven’t created yet.

And then the second thought I have, also, is frankly Odyssey and Class Wallet and all these things that are trying to direct education savings accounts. I think they have to reduce the complexity of stitching this together. And it’s almost like you used to say this when you were in COVID, operating Summit. You had, like, the different flavors or — right? You go up to this — the — the sub shop, and, like, the —

Diane Tavenner: The menu item.

Michael Horn: Yeah, exactly.

Diane Tavenner: The ordering, the sandwich.

Michael Horn: But I kind of think that’s what we see start to, like, take the coordination out of it.

Diane Tavenner: Right.

Michael Horn: Because my orders aren’t going to be —

Diane Tavenner: I can order the signature sandwich where there’s no substitutions. But I know that I’m getting like the Santa Cruz that has turkey and avocado.

Michael Horn: Right. And most families are not going to be able to do. And when they do, it’s not going to taste very good, to use your analogy.

Diane Tavenner: To make their own.

Michael Horn: Right. Like that’s the other part of the innovation we don’t, haven’t seen yet. And so sort of my evolution in thinking has been it’s the coordinating infrastructure. It’s like the different reassembled parts that coordinate. Like maybe it’s hop, skip, drive as part of this ecosystem. For those that don’t know, that’s a sort of disruptive to the bus or car driving

Diane Tavenner: Uber of busing, essentially.

Michael Horn: Exactly. Right. And — and like all these things, maybe this new system and future I would think would be part of it.

Diane Tavenner: Yeah.

Michael Horn: And anyway. But I almost think the way that happens is like, we’re gonna just have to solve each next problem.

Diane Tavenner: Yeah.

Michael Horn: To try to take more like more students and more families.

Rethinking microschool education

Diane Tavenner: And I think that comes back to this idea that I guess maybe you’re helping me realize that I’ve been sort of experiencing the microschool as like people who are like, oh, we’re gonna reinvent education in the classroom. Like, like individual classroom teachers are going to reinvent education. And so microschools were starting to feel like that to me. I’m like, no, we’re not going to reinvent this system. So what you’re helping me see is like, yeah, we have to like effectively scale though, if we want them to be micro, if it is about small, or we need to understand the principles and then figure out how they all kind of coordinate and work together to scale up the system. Because the system is the actual. And if you think about it, the industrial model, most people don’t go to their school and think this is like a factory because the factory is the underlying infrastructure of how everything runs.

Michael Horn: That is the operating system.

Diane Tavenner: That is the operating system. And I think what I want and what you want is a post-industrial operating system that enables and allows the experience we had at family dinner last night, like that type of learning. And it counts and it has all. And so, will some of the features of those schools look like the ones we see today? Of course. Of course they will, because there’s amazing things that happen today, and those of course get pulled into that. But it’s that underlying infrastructure that really transforms, I think, everything.

Michael Horn: I think that’s right. I sort of want to jump into the tools. We probably have a couple things we want to pull back onto the schools on. But we can go back for a long time. We can iterate. But the — so it’s interesting, I think Timely, interestingly enough. Right?

Diane Tavenner: So this was our conversation with Paymon.

Michael Horn: Paymon. Yeah. We should say this to remind people

Diane Tavenner: Who was working on how do you make a better master schedule? And I think what I agree with him completely as someone who was fanatical about the master schedule that embodies all of your values and your resources, your master schedule does. And yeah, so he’s really trying to make that better.

Michael Horn: Yeah. And he is clearly designing in the H1. But I think the takeaway I had from it is one thing AI can be really good at is simplifying a lot of these logistics and time and getting the right experience at the right time for the kids who maybe can’t. Their family or guardians or whatever it is can’t line that up for you. And like so when you think about this operating system of, of industrial model, we move from class to class. It’s all there in the building. It’s not outside of the building to something that is much more porous and flexible and so forth. My thought from the takeaway from that conversation was wow, AI could be incredibly helpful at this coordination logistics, pulling in resources truly from the community and each kid probably having different mixes of that.

Diane Tavenner: Yeah.

Michael Horn: Right. And I’ll stay with my community center model just idea for a second, like multiple microschools, if you will, or whatever we want to call them, plugging in is you can almost imagine a parent saying like, I want to opt out of parts of this totally because I will manage this. And another parent being like — or, you know, having a conversation where like, please help.

Diane Tavenner: Right.

Michael Horn: And you being like okay, we’re going to line up three amazing outside of school experiences for you and then you’re going to jump into this learning experience here. But then like, you’re going to do this project at this other one here because like we’re hearing what your goals are and where your struggles are and, like, the questions, and we’re by the way seeing you could use some exposure to this, Steve, and understand is this something you hate or like or et cetera.

Diane Tavenner: And I think what you’re doing right now is sort of starting to illustrate the difference between Industrial model Horizon one. So Paymon and Timely are working on a master schedule that’s still within the confines of a building. You know, a five day week schedule that’s kind of eight to three, that’s classrooms, you know, I call it the egg crates. It’s like 1 to 25, like all these, you know, so that’s what he’s doing. But now you’re talking about in my mind a new operating system that would greatly sort of expand beyond those boxes, those little egg crate boxes to all the possibilities that could be practically now assembled and personalized for families and kids. And so that kind of would be the H3 version, I think of what Timely would do, don’t you think?

Michael Horn: I’m learning here already. No, I think I’m 100% and I’m getting clarity now. Okay.

Diane Tavenner: And so I think this is a perfect example of like, yes, Paymon, go please. Like there’s, like another school year is coming and please can we get off the magnetic whiteboards where people are still like by hand trying to create master schedules which we know is not going to deliver and do use AI to do that significantly better, significantly faster in a significantly more personalized way. So I thought that was an exciting tool for that reason and it provides that clarity, we just said. I think that. Yeah, I’m curious.

Michael Horn: OK, where are you gonna go?

Diane Tavenner: I can’t decide where to go.

Michael Horn: That’s OK.

Diane Tavenner: I can’t decide because — well, let’s just remind people of the other folks we talk to. So we talked to Dicia Toll, who is working on CourseMojo, which is the ELA sort of in classroom experience. And I think I would kind of bucket her, that conversation with the one we had with Matt Pasternak who is, is at Once there. He’s working on reading younger kids and how they read, starting very sort of human based and then bringing in AI to support. And if we remember, he’s doing really interesting things of how you’re using all the people resources.

Michael Horn: Giving really cool experiential opportunities potentially for high school.

Defining a school’s basic elements

Diane Tavenner: Right, right. Which takes me back as you were talking a minute ago and I’m thinking back to Dan Efflin’s description of at the most stripped down basic level, what is a school? And I love this framing and I think about it all the time now, which is like, it’s literally a set of, it’s a group of students, it’s a group of young people. It is a set of objectives or outcomes that those young people are trying to reach. And it’s a bundle of resources we have to help them do that. At the end of the day, that’s the most stripped down version. And I think Once is super interesting in how they’re thinking about the potential resources, high school kids, you know, instructed to teach young kids in the virtuous cycle that’s sort of created there.

Michael Horn: I just had this thought I hadn’t had it before, but there’s like a. Both a very cool career potential. Right. Of teaching for those kids also, frankly though, if they don’t do that, because I can imagine some people being like, oh, this is like very self determinative or whatever. And I don’t know how I feel about that. I actually think it’s also a very cool, like, parenting 101 opportunity, if you will, for kids to get experience with younger kids and learn about the science of reading and, like, some of the motivation stuff that I’m sure Matt is baking in, things not to do and so forth.

Diane Tavenner: And I would add a third is the entrepreneurial nature of this. So I talked to him about how could high school kids create summer businesses where they’re using once to tutor kids in their neighborhood and set up like little entrepreneurial ventures which he’s super excited about and open to. And so that type of creative thinking about, you know, recognizing and seeing high school kids as resources to help anyone learn but other especially younger kids to learn such a, I just, I love that kind of thinking. I wish it were just across the system in so many different ways.

Michael Horn: Totally agree.

Diane Tavenner: Yeah. One thing that we have talked about that I think is worth bringing up here is we both noted with both of those conversations how these are not, these have required significant human expertise to create these products sort of in collaboration with AI, if you will.

Michael Horn: That’s a good way to put it.

Diane Tavenner: You know, and I think Dacia did a good job of really walking us through like the level of intentionality and intervention and human expertise that has gone into creating those really cool, important, rigorous experiences in the classroom. So I’m curious like what you think about that and how you process that.

Michael Horn: So I agree. I will tell, I’m just gonna be honest. I left the Dacia conversation glowing for like 10 hours afterwards. I was so excited. And I think a large part of it was because of what you just described, with the intentionality of what is great, not just like good, great practice looks like.

Diane Tavenner: Yes.

Michael Horn: And let’s recognize that like, like 80 plus percent of teachers don’t have the training or background or can’t time or whatever.

Diane Tavenner: Like, what she described happening in that classroom is humanly impossible to do. I don’t care how good you are.

Michael Horn: Totally agree, right? But like, even if you were one on one or one on two, that’s fair. Like, a lot of teachers still don’t have that background.

Diane Tavenner: True.

Michael Horn: Right. And she’s looking at the best practitioners to think about the next best question with the text. Like really deep in the text. And that’s something I think that often comes up also right is you can’t just do this in a generalized way. Like actually really digging in is important to create this instructional tool. So impressive.

I also really appreciated how she was like, look in your framework of H1 through, like she engaged in that framework and was like, I’m on the H1, maybe H2 part of this. I found that really helpful because, like, it immediately, it’s like, this is where I’m sitting. But by the way, I can easily imagine what she’s built being part of an H3 set of offerings as well.

Rethinking traditional classroom model

Diane Tavenner: I think that’s right. And so let’s go there. Because my current favorite provocation, I’m probably driving a lot of people crazy with this is what if? And what I asked them to do is pretend for a minute that we can never ever, ever again have classrooms with one teacher, 25 kids, five days a week, 50 minute periods. I don’t even care if you have a block schedule every other day for, you know, 90 minutes. We can never do that again. Imagine we can never ever do that again. How are kids going to learn? Can we like expand our imagination how they can learn? And the reason I’m provoking this is I loved everything Dacia said and agreed with you. I left sad because of the comment where she was like, yeah, they’re doing it a couple days a week.

Michael Horn: Oh, interesting.

Diane Tavenner: And I’m like, why? Just we’re going back to the 1 in 25 egg crate to do all these other things and we’re just doing that. Why? Why aren’t we doing that more? Why aren’t we doing that really strategically or what? Like, I don’t understand, you know, this. Yeah. How we’re thinking about that. And so that felt depressing to me.

Michael Horn: Yeah, yeah, I hear that. I think it speaks to something different that we were going to hold on, but I’m going to go there now because you’ve been forwarding me pieces around the adoption of AI in the workplace.

Diane Tavenner: Yes.

Michael Horn: And Reed used the analogy that we’ve both loved for some time that Ethan Mollick at Penn uses, which is like thinking about AI like electricity. And this sort of, there was the paradox. Right. Electricity got introduced. It didn’t actually increase productivity until you redesigned the business models and factory models, models around it so you could distribute, as opposed to have the central drive shaft. And I think the same thing is playing out right now in industry, which is like AI, more of a cost ad, maybe marginally product, you know, productivity, particularly for coders, but maybe not elsewhere. And large parts of enterprises are frankly not using it to its theoretical capacity.

Diane Tavenner: Right.

Michael Horn: And I think the same thing may be true in schools right now as well, with. Well, maybe this will get us into Magic School. Unless the tool fits the workflow exactly as you’ve currently designed. And like Dacia’s thing, it does. Like, you do need to rethink use of time and space. Right. And like, what. And activities and instructional plans and coherence.

And like, it raises all these questions that are problems in the current model.

Diane Tavenner: Correct. Correct. You’re right. I have been forwarding you lots of things because, you know, I love to do this. I love to go and look out into other industries because it helps me sort of understand the principles and kind of what’s going on. And then I feel like sometimes I’m too close to it in education. And so I need that sort of distance to then come back and look at what we’re doing through that lens.

Michael Horn: Yeah.

Diane Tavenner: And so one of the things that happened to me this season is I just engaged in a whole bunch of conversations with people who are in different industries, and I’ve been really trying to deep dive into how are you using AI and how are you bringing it into your company or your work or what you’re doing? How. How is it getting integrated and whatnot. And these articles I’ve been sending you this last week was profound to me. This line of inquiry has led me to believe one. And I’ll just say this again, AI is not changing education full stop. Like, the only thing that will change education is humans.

We can use AI as the tool to do it, but AI is not changing education. There’s zero evidence that it’s changing education. One in my view. And then two, it doesn’t appear to be changing business as fast as everyone’s at. And so like, we started this season with me feeling like this is like coming. It’s coming like a tidal wave and it’s gonna hit us and we have to get ready. And it’s going fast and I’m ending this season like, oh, no, this is electricity. This is a 70-year project.

Like, calm down, we actually have time because it’s just not coming that way. And the specific thing that came this week that crystallized it for me is learning that all of the hyperscalers, so OpenAI, Anthropic and Gemini in some form or fashion, different but similar, literally are paying a lot of money to, let’s call them an intermediary or third party to force companies to adopt AI and integrate it into their workflows because it’s not happening naturally. What appears to be happening naturally is like all these companies jumped on, okay, let’s do a pilot, let’s figure this out. They’ve been running those, they’re not working. And so they’re tossing AI. They’re like, no, we’re not going to do that.

Michael Horn: Because it’s expensive. The computer power, the tokens, right? It’s expensive and people aren’t using it.

Diane Tavenner: Like, it’s not happening the way they thought. And so, you know, in OpenAI apparently is going to give their private equity companies a huge amount of money to force their companies to integrate AI, OpenAI, you know.

Michael Horn: See if any of that money materializes the business model of — I mean, that’s another shaky thing in all this is like, which companies will still even be here at the end of this? Google probably will, but like, we don’t know.

Diane Tavenner: Right. And I think it was Anthropic who’s going more of the consulting route. So MacKenzie and those folks, and they’re going to use them to try to deploy it. And Google had a — what was their strategy? Like a combo of the two or something like that. Anyway, I’m like, if it’s not happening in business, where they actually put resources to these things and efficiency is their, you know, the way they’re competing, it is not happening in education. We don’t have the resources to do what they’re doing. We don’t have the incentives they have.

And so, it just gave me sort of like in some ways a good breath of like, oh, we have more time. And then two, oh, unless we actively do this, this is not gonna be the tool that helps us.

Michael Horn: Yeah. So stay on this. This is really interesting on a few fronts. You’re crystallizing something I had been thinking about but hadn’t fully dawned on me, which is I do think what’s true in the business world, and I think it’s very true in the education world, is individuals are using it in bespoke ways.

Diane Tavenner: Yes.

Michael Horn: And like, I’ve been thinking about some danger of recording and then this won’t be out for a few weeks. But I’ve been thinking about writing something around how, like, for years, education was like the last place to adopt technology. Right now it’s actually adopting technology very quickly. And that’s the problem. And the reason I say that is, I think teachers are pulling it into workflows that exist. This is the Magic School thing that we learned what they’re already doing, but more efficiently, perhaps with less thought, et cetera, et cetera.

But to your point, from an enterprise like reworking workflows. No.

Diane Tavenner: Right. Which goes back to that idea of the operating system, right?

Michael Horn: But I think — so the other thing I want to just say on this is the other reason I’ve been skeptical. All my friends that live out where you live in Silicon Valley tell me that, like, you don’t get — it’s exponential improvement, and therefore it’s going to like, flip a switch and the world’s going to change and blah, blah, blah, blah, blah. And Reed has this view a bit as well, right? That he said.

But I think what they discount is, like, they think through a very narrow lens of how work is done, and they discount all of the human friction that you just started to allude to and organizational friction and legacy systems and stuff like that. Not just in education, but, like, I think you’re right. It’s actually in almost every part of the economy.

Diane Tavenner: Right. And I’ll just say two funny things about that, because that’s where I live, as you know, that’s like the water that I swim in. And I do think a lot of that sentiment comes from. It is moving most quickly and most effectively in software development.

Michael Horn: I mean, that’s why you see the product development. I mean, that’s why I think you see and it’s important. Amazing, right, Claude code is amazing.

Diane Tavenner: Amazing.

Michael Horn: And it’s like zeroing in on an application in a part of the economy that they understand. And it’s a narrow set of — I’ll misuse the Howard Gardner multiple intelligences. It’s a narrow set of intelligences, like, you know, what my point is, though. Yes. I don’t mean to lift that up as a valid framework, but I more mean it as an analogy.

Diane Tavenner: Yes.

Michael Horn: Let me say it this way more specifically. Right. Which is an interesting thing about these large language models is they’re largely trained on language and images, and like we have as humans, have lots of other senses that are not going into these things.

Diane Tavenner: Right, right, right, right. And it turns out we have a lot of power and control over our systems and our lives and whatnot. At the end of the day. Well, that kind of leads us into the last two tools that we explored, OK?

Michael Horn: Kira and Magic School.

Diane Tavenner: Kira and Magic School. And I mean, just a funny aside, I don’t know if people could detect this, but I think we have to come clean and be honest. Like, both of us left one episode this season pretty, like, frustrated, angry. I mean, I had a very hard, long weekend after one episode because it really, like, shook me. They were different episodes.

Michael Horn: OK, yeah.

Diane Tavenner: And I don’t think either of us predict that we would have our own emotional reaction or that the other person would. So I will come clean and say, yeah, I mean, I had a really rough weekend after we recorded the Magic School episode on a Friday, and it shook me.

Michael Horn: Yeah.

Diane Tavenner: Yeah. And I don’t know how you would describe your feelings after the Kira —

Michael Horn: After the Kira — ? Yeah. I mean, could people hear the frustration? I don’t know.

Diane Tavenner: Yeah. You were frustrated.

Michael Horn: I was frustrated. I think before that, I will say on the Magic School, one little surprised me about the conversation. And so maybe that’s why I didn’t have quite the emotional reaction. I was not surprised by the direction it went. But we can break that down. The Kira, I’ll just say, rather than hide it. I was trying to understand in more detail, like, what are you, like, what does this actually look like when you say you have a Central American country, that it’s all moving over to a new. Like, that’s a big claim.

Diane Tavenner: Yeah.

Michael Horn: What is it? Like, what does that actually mean? Look, like, how are you changing workflows, what people are doing? And when you say, like, contrast with Dacia.

Diane Tavenner: Right.

Reflecting on content personalization

Michael Horn: Because Dacia’s episode we recorded, I think, afterwards, and that’s when I had this big breath of fresh air to me was like — she’s like — we went deep in the content as humans to really understand what it’s saying. And I felt like, so maybe Kira’s able to ingest anything of content and, like, magically make it awesome and personalized and — But it took me back to a lot of stuff in like the mid 2010s that talked about personalization and data without, like a clear view of like, how are we assessing mastery? How do we understand this thing? And like, if you don’t have a viewpoint on that, I’m not sure how you’re driving these behaviors. And so I’m — I’m not castigating the tool because I just, it was more like I wanted to understand more.

Diane Tavenner: Yeah. And that was what I mean. We hung up and you were like, I don’t — I’m so frustrated. I don’t understand what it does.

Michael Horn: Yeah. It was more limitations.

Diane Tavenner: Yeah. And to be fair, as you know, I’ve spent multiple hours now sort of getting under the hood.

Michael Horn: Yeah.

Diane Tavenner: I think that’s a really interesting response though. That, and what it made me reflect on is Kira is currently in the complexity. So I always love this, you know, quote that gets, you know, you start with simplicity, you move into complexity and the goal is to get to the other side of complexity, back to simplicity, so people can understand it. And I was, I realized that I have been sucked into Kira because it is in that complex, messy place.

Michael Horn: Yeah. So it’s hard to describe.

Diane Tavenner: So much potential there though, that I can see when I’m really geeking out and nerding out. And so I see the possibility there. But I’m doing like all that mental translation, I think, having dug in. So. But I don’t think it’s a simple story yet by any stretch of the imagination.

Michael Horn: Which is fine, right? Like a startup. It doesn’t have to be until product market fit is so clear.

Diane Tavenner: Right. And there’s a lot to be figured out there and understood. But I do think it’s the closest thing I’ve seen to the potential of being kind of this operating system. Post industrial operating system. And I will just say quickly that like, part of your skepticism probably comes from like, we get a lot of people calling, emailing us, stopping us, you know, at the ASU + GSV, telling us we’re doing exactly what those people said we’re doing. And then we try to dig in and we’re like, are you really. You know, and so. And you get more of it than I do.

Diane Tavenner: And so I bet.

Michael Horn: I think that’s certainly true. And I think I’m also part of my skepticism is I kind of think you have to build for this H2, H3.

Diane Tavenner: Yeah.

Michael Horn: And purposely say we’re not building for H1 to do, if you’re really going to be the operating system for this new model. I think anything you build in H1 that’s optimizing around that is going to do two things. I think it’s going to suck you out. Like I think there will be values that are in contradiction with each other and you can’t do both. And two, I think because the most of the kids are still in H1 and most of the money is still in H1, we’re going to see what every venture backed ed tech company has done which is like start out with this value proposition that sounds great. And then move back into the traditional system. Because that’s where the money is.

Diane Tavenner: And that is huge tension. It’s a huge tension and it’s a limitation in our sector quite frankly.

Michael Horn: Yeah. And I think venture does not help it because they have very rapid expectations of growth that are incompatible..

Diane Tavenner: Incompatible with that. And so how do we get someone to build for H3? It’s a huge question.

Michael Horn: Let’s go into it next year more because I think what you did at Summit, actually doing it interdependently with the school model is. And with the AI tools available now, maybe actually easier to do parts of that. I think that’s gonna have to be the answer, but let’s hold on.

Diane Tavenner: Let’s hold, let’s hold.

Michael Horn: So let’s talk about Magic School because you had a reaction but like this is a place where I’m gonna say it. They do have product market fit. It’s taken off like gangbusters.

Diane Tavenner: Like crazy. Which is — well, let me just say the parts that I really spent that weekend grappling with and struggling with. So the first is, I mean the clarity with, of the purpose of Magic School being to make teachers’ lives easier. And so first of all I just have sort of, that is not my goal or objective ever.

Michael Horn: Period.

Diane Tavenner: And it’s not because I don’t love teachers. It’s not because I don’t think educators are amazing. Look, I am one. I do that role, whatnot. But my goal is not to make a teacher’s life easier. My goal is all about the young person. And I view all of us, me included, who are not those young people as resources to that young person’s learning journey to get them to their outcome. And so I just think when you orient around making a teacher’s life easier, you by definition are no longer focused on the student.

Critique of current education standards

Diane Tavenner: And so I just think that fundamental premise is problematic to me. Second, it just as a teacher who is really committed to the craft of teaching and learning and education. I just feel like it sets this incredibly low bar. And you know, we had a dialogue about, you know, IEP creation. And as you know, I spent a lot of time in Magic School and I was really disturbed by this, like popped out generated IEP for a variety of reasons that was terrible. And you know, the idea that that was somehow better than what is existing in school that had the kids wrong kid’s name on it is like good enough or we should be celebrating or like that’s our bar.

I just can’t. That’s just not who I am as an educator. You know, my — every morning I wake up and say like, is this a school I would want to go to as a student, teach in, send my child to. And I don’t want, that does not meet my bar in any way, shape or form. So I’d rather not be doing education than doing it at that kind of standard. And then the third thing was I’ve just seen this movie before. So first it was, you know, teachers, paid teachers where we just throw these random activities up and, and you know, you’re just doing this activity driven thing.

And then it was Pinterest where teachers are just grabbing stuff off of Pinterest. And now it is Magic School, and it just really diminishes. It makes me so sad. It’s like, really, that’s who we are?

Michael Horn: Yeah, I think I wasn’t surprised by any of that, which is why I will say I appreciated, when you talked about learning styles and stuff like that. I was. We were both pretty horrified by that given the research clarity around that learning styles is a myth Yeah, so full stop. I thought his answer on the IEP, I could appreciate it a little bit more because I do think that the baseline is not what we would hope across.

And so I get it. And I think my overriding thing is like, it illustrates to me the problems with H1, which is, and I, you know, I sent this thing to you that I did with Rick Hess. I think because existing school models struggle with, with coherence, rigor, you know, setting clear expectations for students and focusing on the student frankly, any model, any tool you sell into that model that gets immediate uptake without significant process and priority changes, as frictionless as that has, and the other tools that you named have, it’s actually amplifying those things rather than solving them.

Diane Tavenner: Yeah, I completely agree. Completely agree. And then of course the business model of we’re going to get all these teachers to use it. And then we’re going to take the data to the school or the school district and say, did you know all your teachers are using it? You should buy an enterprise license. That was deeply disturbing to me. That’s the seat that I sat in most recently. And I’m like, really? Am I going to be like, that’s how I’m making my decisions about what tools we’re adopting?

Michael Horn: Well, I’m going to say independent, though. Right. Of the. That this is my bigger point right now. I think of like, the individuals in the system are just doing stuff. And that’s where I think education actually is. I again — I think, the moniker. We’re tech backwards. Rip Van Winkle wakes up and recognizes the classroom. I don’t think that’s true anymore in this era because they are using a lot of stuff. In fact, districts are using —

Diane Tavenner: Yeah.

Michael Horn: Have licenses to 3,000 tools on it.

Diane Tavenner: That stat, you tell me that. You’ve said that like 20 times.

Michael Horn: Mind blowing, right? It’s mind blowing, and it’s a problem.

Diane Tavenner: I know.

Michael Horn: Most of them we know don’t get used.

Diane Tavenner: Correct.

Michael Horn: We know for the effective ones that it’s like a 5% problem of only, you know, students on a certain part of the curve even using them.

Diane Tavenner: Yeah.

Michael Horn: To me, though, these are all model problems at the end of the day. And that’s where I think I can get my head around. Okay, are we working on H1? Are we working on reinventing completely? I can get my head around that there’s importance and value in both. Personally, I really struggle on the first one because I just think the model is so injurious at this point.

Diane Tavenner: Well, and I think that one of the things that happened in the middle of the season was this backlash to this technology, which we haven’t even unpacked yet. Literally. We’re mid season and suddenly almost out of nowhere and overnight, I mean, this massive backlash. And you alluded to it earlier, look, we were already on this trajectory of banning phones in schools. You know, we’ve talked about that over the years. I think we’ve both sort of settled in like, you know? OK.

Michael Horn: Yeah. I mean, I’m willing to live with it. I don’t love it.

Diane Tavenner: I don’t love it either. I will, but I don’t want to live in the space of like, I wish that we could have a culture in the school where kids could learn to use them responsibly

Michael Horn: No, I agree. Yeah, I agree with that. I guess I’m more intrigued with some of these other tools that sharply limit and let the educators have control. But look, yeah, I’m not gonna do.

Diane Tavenner: That’s interesting. So that one fine we had sort of. But then suddenly overnight we are banning Chromebooks. We are putting limits on the amount of minutes that can be technology can be used in the school in a school day. We are saying that, that no one in a school can use AI, including the teacher in any way, shape or form. Which is the most baffling, mind boggling. Like how do you police that? What does that even mean? And these policies are popping up like crazy. My take on this is that there’s such a lack of understanding and such a conflation going on.

So there’s all these cases and the, the heightened sensitivity around social media and the negative impacts on kids that is mostly sitting on their phones but certainly it’s online and some kids are, you know, districts are not using the filters and whatnot. And so they’re maybe getting on their Chromebooks. So that’s getting conflated with actual useful learning tools, useful learning technologies, useful operational system technologies. And we’re just suddenly going to just ban everything.

Michael Horn: Yeah.

Diane Tavenner: Seriously.

Michael Horn: Yeah. And this is so this is the thing, right? Let’s like we live in the nuance. We like the third way. That’s our trademark, for better or worse, let’s like spell it out. We are simultaneously not thrilled with large parts of the ed tech market that is going into traditional schools. And so I understand why the backlash is emerging. I would agree that for the most part they have not been useful in some cases counterproductive.

Diane Tavenner: Correct.

Michael Horn: And these policies are limiting. So we’re not going to let someone use CourseMojo for, like what?

Diane Tavenner: That was the first example that we came to. Or we’re going to say like well if you use it and you. We’re going to count the timer, you use that for 20 minutes. So you only have 40 more minutes today for the whole day to use any sort of technology.

Michael Horn: Like what are we doing?

Diane Tavenner: What are we doing?

Michael Horn: Yeah. And so — or like Amira Learning, which Dacia also talked about, which very well studied, lots of RCTs. We’re not going to like let them use that? And then there’s been this whole meme that has gone on about a particular company i-Ready on the internet over the last few weeks about how there’s no studies behind that them. Fine. I’m not going to defend them right here except to say like that’s true of every freaking textbook and material we have ever had in school.

So let’s, like, have an honest conversation.

Diane Tavenner: Well, yes, and, and all the — the — the negatives that you just said about ed tech are 100% true of all of those industries, if you will. The other thing, like, let’s put Once in this. There’s a bunch of places banning technology use with kids under certain age, period, full stop. They can’t use Once to teach their kids to read, even though there’s human component to it. Like, this is crazy.

Michael Horn: This is crazy.

Diane Tavenner: This is literally taking a sledgehammer to —

Discussing educational model challenges

Michael Horn: What is a real problem. And I’ve been thinking a lot about it, like, what’s the role, you know, blended and all these things that we’ve sort of movements that we were part of. And I was looking back and I was like, I actually think we were pretty clear. Like a station rotation, which is all I said in elementary school, from a traditional district should even attempt. I’m partial to the flex model that I, in our typology, but, like, I didn’t think most districts had the cultures or routines or processes to do it. A station rotation model imagines a kid on a computer for like 30, 45 —

But to your point, these blunt acts policies —

Diane Tavenner: They don’t give any discretion or flexibility or professionalism to the people in the school who are having to make these choices and decisions. And the tension there, of course, is like, wow, did we lose that? Did we deservedly lose that trust?

Michael Horn: That trust, yeah.

Diane Tavenner: Because we are making some bad choices about what we’re bringing in. We are wasting money on things we’re not using. We are not thoughtfully integrating, you know

Michael Horn: So, two thoughts here, because I think you’re right. And so we’ve sort of made our. Our point of view known. I hadn’t thought about this, but I loved that Dacia said we’re doing outcomes based on contract.

Diane Tavenner: Yes, I do too.

Michael Horn: I’ve been saying this since 2009. Like districts. And districts would look at me like I was crazy. There’s now, you know, through the Southern Education Group.

Diane Tavenner: There’s a whole group.

Michael Horn: There’s a way, there’s a boilerplate language. I think every ed tech company worth its salt ought to do a contract.

Diane Tavenner: Well, let me just amend that because I’m in that space right now. That’s the exact place I wanted to go because I’ve sat on the other side, and I was like, this is brilliant. I love this, and quite frankly it’s good for me too because it doesn’t help me if someone buys me my product and never uses it. That’s actually bad for all of us.

Michael Horn: Yeah, yeah. Zombie revenue, etc.

Diane Tavenner: But the problem is even the outcomes based contracting people will tell you, oh, we only really are ready to do this with very limited number of products where there are clear measures. I think we’ve got to push on that. I’m hoping to be one of the people who will push on that because the space I’m in, they’re like, oh, we have no idea how to tackle that space.

Michael Horn: But this would be amazing. Amazing. I’m gonna go into one of your other bugaboos. Yeah, this would be amazing. Like RCTs is one way to look at the world.

Diane Tavenner: One way.

Michael Horn: If you have outcome based contracting with assessments and measures that we trust. Yes, it solves itself totally. And so, like, this is a way I think to actually develop incentives.

Diane Tavenner: I agree.

Michael Horn: To get those more grassroots driven. What’s the outcome we’re trying to drive? What’s the right assessment or measure for it? And let’s count it like conversations.

Diane Tavenner: I agree with you. And like in my case there are things I specifically know that our technology is designed to do. And so maybe those aren’t yet, you know, fully industry standard or something. But. But when you’re buying it, don’t you want to know what it’s supposed to do and then figure out if it’s —

Michael Horn: And then hold it to account for that?

Diane Tavenner: Yeah. And I think that’s good for everyone. So I agree with you. I think more there really interesting and —

Michael Horn: I think that would be a good get instead of the bands. That would be a great place for that. I will say I think it also. We may pull back into school models here for a second. I think it’s one of the reasons Alpha is attractive also to families is because they feel like they’re making this big leap, OK? And so actually I heard someone, one of my students said they profiled seven different microschools using AI in different ways. And one of their statements was like families feel like, you know, two hour uninterrupted block of time or whatever feels radical.

Is it really more radical than like seven 45 minute periods? No, but like no one ever thinks that way. But it feels like radical to do, you know, mastery based progression and then like all this time for life skill development and with what you said still true, like it’s decoupled, and that may have problems for Far transfer.

Diane Tavenner: And I’m not sure they’re doing mastery based, but keep going.

Understanding norm-referenced assessments

Michael Horn: Okay, so maybe we’ll come back to that also. But like, I guess my point being like families are opting for it, and I think one reason that they are is not just the price point signaling, but also the old metrics signaling. We might not love the old metrics. NWEA map. I think it’s like a very bizarre usage of it. And I’m just going to spell it out for those that. Because I think people still are confused around what a norm referenced assessment often shows, which is like imagine a Y axis of different score levels or achievement levels and like, okay, I’m at the 90th percentile entering in, relative to the people in my band. There’s a curve and like, you know, am I falling below or above right them.

And so when they say 2x, it’s like 2x the person in the middle at my curve is what that’s showing.

Diane Tavenner: Yeah.

Michael Horn: And in some ways it would be shocking if they weren’t hitting that because the traditional school is capping how fast you can move. We heard Reed talk about this on Dreambox and so if you’re now uncapped it, I would hope that a student would move at least that much faster.

Diane Tavenner: Well, this is why I question if they’re doing mastery based. Because that has nothing to do with mastery.

Michael Horn: Say more on that.

Diane Tavenner: I mean where you’re just comparing against the kid. That is not mastery based. Norm reference is not mastery based.

Michael Horn: Oh, fair enough. Fair enough. OK, so this is where I think they are though is because they also have a bunch of criterion references also going on that they didn’t talk about.

Diane Tavenner: Yeah. Yeah. And you know, I’m skeptical because they’re like jumping between adaptive learning programs.

Michael Horn: I think that’s a big question. Yeah, yeah. I don’t disagree. And I’m wondering where the feedback loop truly is in that some friends of ours in the industry who’ve spent a lot more time with the model tell me that there’s a lot more underneath it. Underneath. And I don’t know. I don’t know. I will say simply raising the bar from 60% to 90% was a nice start.

Diane Tavenner: Yeah. Yeah.

Michael Horn: Because it’s crazy.

Diane Tavenner: Well, and we did that as well at Summit. You know.

Michael Horn: But it’s crazy to me that they didn’t like before. But it reflects also on H1. But I guess my point being more I think that’s why they have these very traditional measures because it gives, like as a parent, it takes away anxiety.

Diane Tavenner: Yeah.

Michael Horn: I’m making this big jump. What do you mean? They’re going to be doing a Tough Mudder and a press conference with athletes.

Diane Tavenner: And like, the familiar measures that give me comfort.

Michael Horn: So I think it’s almost taking the world as it is, not as maybe we would like it to be. And I think that’s part of, like, it’s like, it’s why. I also think you’ll hear the bragging about the SAT score or the acceptance to Stanford or the, you know, and. Yeah, so I guess I’m just explaining it a little bit, but. And I think that crosswalk may be part of this process as more families gain maturity around. Oh, is that possible through this? And so I’m curious.

Diane Tavenner: Yeah. So what I think you’re speaking to is this whole, whole societal shifting sentiments. And so, and we don’t really talk about this, but this would be an interesting thing to explore next year. Like, is there a way to understand. Well, we know people who look at these things, so there is a way to understand this.

Is the public sort of sentiment moving or shifting or changing? And what is moving it and shifting and changing it? And so, you know, are we in this sort of chaotic period where we’re like, people are holding onto things of the past, but they’re like, exploring and looking at the future and they’re kind of like, you know, there just feels —

Michael Horn: Well, and here’s the relationship, right to the tech conversation also, which is like, okay, my kid’s going to be on this two hours of screen time with AI, it sounds creepy. It’s a vision model that’s looking at me, tracking my attention. But there’s a real measure that shows outcomes.

Diane Tavenner: Right.

Michael Horn: And so I think, think like now

Diane Tavenner: And this cool cocktail party thing where I just say that my kids, you know, running a Tough Mudder.

Michael Horn: Yeah, yeah, yeah, Right, right, right. Of running a Tough Mudder. And so like, but here the legislation would ban these movements to like, like this stuff as well. That’s not. I don’t think that’s what we want either.

Diane Tavenner: I mean, we’ve never liked legislation, like the type of legislation we’re seeing popping up overnight. I hope by the time next season starts, it’s kind of calmed down a little bit and people have sort of gathered their wits. I hope that educators are going and making logical cases as to why with real tangible examples like we’ve just given about why these are not useful policies. And can we actually put something more thoughtful in place? I hope the media doesn’t, you know, sort of fan those, you know, fears and instead sort of brings a little bit of a rationality to it. That’s of lot of hope. You gotta have it.

Michael Horn: But yeah, I mean, I think, I think my hope on that front continues to be the families that are opting for these new models and that it’s more grassroots driven because and I think, like, I think that’s my other piece and we’ve had this conversation offline. Your question a little bit is it really going to be a portfolio of models in the future? Is it really one?

Diane Tavenner: I’m so curious about this.

Michael HornL and I guess the reason I think it might be a. I think it depends how you define model.

Diane Tavenner: It does.

Michael Horn: And I think it also depends on like, I think it’s very hard to imagine all families lining up. I’ll say it up front, like a billion people in Alpha school. I would be shocked. Shocked. Because I don’t think that’s where we

Diane Tavenner: Just count me as a complete skeptical. There’s no way.

Michael Horn: But I think that’s the point is like some families are going to say no screen time.

Diane Tavenner: Right.

Michael Horn: Great. Some families are gonna want more than what Alpha provides OK. But like, let’s, let’s take the air out of the balloon and let a little more choice with some measures that give feedback loops to the families so that they know they’re making progress for their kids.

Diane Tavenner: We can’t help ourselves. We’re starting to foreshadow all the things we’re thinking about and what we’re starting to plan for next season. So I guess maybe it’s obvious at this point but, people might not realize that literally at the end of every season we get together and we’d reflect and we make an active choice of whether or not we’re gonna do another season and this season. I don’t even think there was a question.

Michael Horn: No. This was the most clear cut we’ve ever been I think.

Diane Tavenner: Clear cut.

Michael Horn: Clear time.

Exploring AI’s impact on education

Diane Tavenner: I feel like we are so clear. So much more to explore the impacts of AI. So I think we’re going right back in on that front. Two, I think we have more questions than we had even at the beginning of this season. More people that we want to talk to than we had at the start of this season. So like more, more, of, you know, how what is happening with AI in education? What are the tools? What are the models, you know, what’s happening on that front? So I guess this is a good time to say, like, if you guys have ideas, people you want to hear from, you know, things you think we’re not exploring.

We would love to hear from you over the, you know, this will be effectively the summer, if you will. Yeah, I know. It’s like the one sort of traditional thing we do is, like, have summer break but —

Michael Horn: We’re a little traditional.

Diane Tavenner: We’re a little bit traditional on that front. So we want to hear from people on that front. And then we’ve got these other two kind of funny passion project ideas.

Michael Horn: Are you going to talk about it? Yeah, go for it. No? OK, all right. We got a couple other ideas, which are going to be our curiosity driving it and our desire to explore. But it won’t be with guests or at least maybe. Yeah, but not.

Diane Tavenner: I don’t know what form they’re going to take. I would. Yeah, they’re interesting. They’re like things that keep coming up for us, and so I think they deserve a little bit of exploration in maybe not the exact way that we do the rest of the podcast. So we’ll see how they —

Michael Horn: We’ll see how those take shape. Probably not like season one, where we script every.

Diane Tavenner: No, no, no, no. So. So we hope folks will be excited to join us again for another season.

Michael Horn: I think that’s right. And send us your curiosity questions and, like, things that we’ve said that don’t make sense to you. And we’ve both had a lot of hot takes today and left a few dangling.

Diane Tavenner: Kind of a rambling conversation

Michael Horn: I was about to say. Did I give you room for everything you wanted to say?

Diane Tavenner: No, definitely. I mean, this is just like an ongoing dialogue for us.

Michael Horn: So people pick in where the things that we maybe had incomplete sentences or thoughts or you want to hear more.

Diane Tavenner: Definitely.

Michael Horn: We’re going to scratch those itches next season.

Diane Tavenner: Definitely. I’m very excited for it. But before we do that, should we

Michael Horn: Do what we’ve been reading or watching or what we’re going to do?

Diane Tavenner: We should. And I — I think I have to —

Michael Horn: Pull out my phone so I know what I’m reading.

Diane Tavenner: You have to know what you’ve been reading. AndI’ve got. I decided since this is the last episode of the season, I have three things, so I’m gonna go crazy and share more than one.

Michael Horn: All right, Go for it. Go for it.

Diane Tavenner: You want me to start?

Michael Horn: Yeah, you. You start.

Diane Tavenner: So this one probably won’t surprise people, I have just recently finished reading “How Countries Go Broke, the Big Cycle” by Ray Dalio. And people have heard me who’ve listened to the podcast before talk about his previous book, “The Principles for Dealing with a Changing World Order,” which was really profound for me in terms of these deep analytical approach to identifying these historical cycles that help me make sense of the world we’re living in and what sort of feels chaotic. This new one, newer one, about how countries go broke, is a companion to the first one. Obviously it’s speaking to something that I think about and don’t know a lot about, which is our debt and what it means if we’re no longer the reserve currency, et cetera. What I will say about it is it’s sort of classic Ray Dalio, if you like that kind of thing. It’s actually comforting to me in a way. So I really —

Michael Horn: It’s a critical topic.

Diane Tavenner: It is critical as like a citizen, I think, and a person in the world. So I highly recommend it. I find it fascinating. So that’s one. The second one is a podcast, you know, that’s become one of my favorite podcasts. It’s fascinating. Interesting times with Ross. I never say his last name

Michael Horn: Ross Douthat.

Diane Tavenner: Yeah.

Michael Horn: Yeah. He was my year from college, so, you know.

Diane Tavenner: Oh, I didn’t realize that.

Michael Horn: Different college, but it’s —

Diane Tavenner: Yeah, OK. And I listened to this one at the same time. I was reading the Countries Going Broke book and they go together in a really interesting way. It’s titled “A Bitcoin Evangelist Tries to Convert Me.”

Michael Horn: Oh, interesting.

Diane Tavenner: And it is like Bitcoin 101, which again, important topic. Highly related to the “Countries Going Broke.”

Michael Horn: Well, I was going to say highly related to the notion of a reserve currency.

Diane Tavenner: Exactly, exactly. So I found those two things a really interesting pair. And then the last one is, you know how much I love the “Last Invention” podcast series. I thought it’s, it’s just incredibly well done. They do continue to add these fascinating, you know, episodes here and there, but that comes out of a group called the “Reflector” podcast who actually does all of these other topics. And I just listened to a three-part series titled “Strange Bedfellows When LGB Meet T.”

Michael Horn: Oh, interesting.

Diane Tavenner: And so good. It’s this historical look at the movement. It’s incredibly well done. The style design that I love and for me, very personal. Lots of people I love are LGBTQ and living it and it just gave me a lot of incredible insights and history and understanding and provocations and yeah, really fun.

Michael Horn: Super interesting.

Diane Tavenner: Yeah.

Michael Horn: I’ll add that then to summer walk, dog walks, I guess.

What do I have? So I’m finishing up “Jump,” which is by Larry Miller. It’s the same. The story of — so he was on the Jordan Brand and Nike, and it’s his memoirs, effectively.

Diane Tavenner: OK.

Michael Horn: But he was someone who was incarcerated. He did some, you know, pretty awful things as a young man growing up in Philadelphia. Spent a couple times in jail, got his education while he was in jail, got a degree, and then ultimately an accounting degree from Temple, and then went on to have this incredible career. And it’s these reflections and frankly, also how he was dealing with this, like, ghosts in his past that no one knew about and he couldn’t talk about. And sort of. It’s a really interesting, like, reflection on human potential and how do we think about, you know, this in people.

Diane Tavenner: You were telling me about it last night, and I’m very curious.

Michael Horn: Yeah. And it’s a great — it’s a great audiobook.

Diane Tavenner: OK.

Michael Horn: So it’s very — it’s very lively.

Diane Tavenner: On my list.

Michael Horn: On your list. I’m doing a weird — so I was trying to read books out loud to my kids, because why not?

Diane Tavenner: Because they’re adorable, and it’s so fun.

Michael Horn: Yeah, well. But you don’t get the — I mean, because they’re just so racing ahead all the time, right? I don’t have a lot of opportunities. I was trying to do a couple Mark Twain books with them because I remember loving “A Connecticut Yankee in King Arthur’s Court.” Here’s the takeaway: Really hard to read it out loud.

It’s really sort of funky.

Diane Tavenner: I was like, oh, interesting choice.

Michael Horn: But as a result, I’m reading all the Mark Twain books now, so to myself. And so, like, I know you’re gonna laugh. I don’t love fiction generally.

Diane Tavenner: No, you don’t.

Michael Horn: Nope. But I’m enjoying it. So we’ll see where it goes if I keep going through it. But I had this idea that I would culminate with the Ron Chernow biography of Mark Twain.

Diane Tavenner: Oh, my gosh. You be careful before you do that, Scott read it. Do you read how long that book? It’s long.

Michael Horn: Yeah.

Diane Tavenner: He was in that book forever. Yeah.

Michael Horn: The Ulysses S. Grant book was a long one that I read it by him as well.

Diane Tavenner: I mean, the level of detail about one person’s life, it’s extraordinary.

Michael Horn: OK, well, we’ll see if I do it or not, but maybe it’ll slow down my pace in books and I’ll have to watch more TV.

Diane Tavenner: You’ll come back next season and be like, I read one book.

Michael Horn: I read one book this summer. Yeah, no, it’s entirely possible, but those are the things at the moment on my list.

Diane Tavenner: That’s awesome. Yeah, I’m not adding that one to my list.

Michael Horn: Fair enough. Fair enough. I’ll commiserate with Scott afterwards. Huge gratitude, Diane. This has been a lot of fun this season. In some ways we’ve had a lot of fun throughout, but in some ways the most fun that I think I’ve had.

Diane Tavenner: So I agree. This is. We both do kind of a lot of sort of side gigs, volunteer, you know, middle of the night, passion project. But this definitely is very special.

Michael Horn: Huge thanks for all and huge thanks to our audience again because it does really fuel our curiosity and our desire to do this. And feedback keeps us going. Reminder. Subscribe Rate us.

Diane Tavenner: Yes.

Michael Horn: We forgot to say that up front.

Diane Tavenner: I know. Because we always forget to say it. We’ve never said it, but it turns out it does matter a little. And so if you can even take just 60 seconds to put five stars there and give us some of the feedback that you give us verbally or in writing, it helps a lot.

Michael Horn: And let’s also say thank yous to Âé¶čŸ«Æ· for continuing to distribute. Let’s say thank you to LearnerStudio for sponsoring the team Danny and Lindsay, who make this come to life at the moment. Really appreciate both of them. Both last names Curtis, but not related. And just huge thanks to everyone that makes this work.

Diane Tavenner: Yeah. Awesome. And with that 
 

Michael Horn: 
 and with that, we’ll see you next time on Class Disrupted.

This episode is sponsored by LearnerStudio.

Though it was only a few years ago, the fancy gadgets have gotten a lot more advanced since then, and now Suskind is back with her next (and, she promises, her last) book, addressing the promise and perils of the technological revolution coming for every element of society, including young children. 

In “,” which will be released on July 14, Suskind acknowledges that technology is swiftly mastering endeavors that were once considered uniquely human. “Artificial intelligence is eroding our supposed superiority in each area one by one. 
 It generates language with fluency that surpasses most humans. Al turns out art, music, poetry, software. It uses existing tools and builds new ones.” 

But she zeroes in on a distinction that matters: Unlike humans, “AI does not care.”

Suskind’s new book guides parents, caregivers and educators through navigating how to raise children in the age of AI by offering the HOPE framework, which offers four principles: human connection, owning imperfections, protecting the early years and enhancing adult-child interaction. She called it HOPE, she said, because “Having a child is an act of hope for the future, and the future is not predetermined, as much as it feels like it is.” 

A pediatric surgeon and the founder and co-director of , Suskind has been a prominent voice in early learning since the publication of her 2015 book, “” which popularized the concept of linguistic “serve and return” between babies and their caregivers. 

In the conversation below, Suskind describes her nuanced stance on AI and shares her thoughts on how AI shapes child development as well as what parents and early educators should consider when deciding how and when to use technology with young children.

This interview has been edited for length and clarity.

How does “Human Raised” fit in with your other books? 

My whole journey has been about the power of talk and interaction and relationships 
 to allow children to not just learn, but to become human. A couple years ago, when AI started building momentum, I was like, “Oh my gosh, wait a second.” Suddenly we have technology that can mimic the human interaction that builds a child’s brain, which has been the focus of my entire research. I was like, “Oh, we really need to be thinking about this in a really deep and thorough way.” Because not only was it going to come so fast and furious, but parents have no guidelines or guidance. There are no policy guardrails. And so I felt I needed to write one more book — I swear this is my last one! I wrote it to think through this whole thorny issue myself. 

What particular insights do you bring as a surgeon?

I’m a cochlear implant surgeon. The way I got into this field was [by examining] differences in early language environments among children who are deaf and hard of hearing and got cochlear implants, [versus] typically developing children. I saw interaction and language 
 as modalities for building cognitive skills, language and literacy. 

I’ve always been cognizant that and nurturing interactions are important for socioemotional development, but so much of my work has been focused on those hard skills. And in writing this book, [I see] that human connection is a way to become human, to build the social brain, to build our ability to connect with other humans and navigate the human world. 

How can parents and educators tell when a technology is enhancing connection versus replacing it?

The frictionless experience is what makes it so seductive, and so different than what we’ve met before. I mean, we’ve had technologic innovation throughout all of human history, but it’s always been sort of a one-way street. Even social media and the engagement economy has been about sucking our engagement, but it hasn’t been building an intimate relationship and that is what generative AI [does].

AI is not a monolith. 
 The generative AI and that intimacy building aspect of it is what is seductive for adults, and for young children and very young children. The younger you are, the more likely you are to both anthropomorphize this technology, to project thoughts and feelings onto these entities. So they’re more at risk. 

At the same time, I am not anti-technology. 
 I believe in the power of technology that allows human flourishing. And I do believe that if we use it in the right way, it could do the things that we want. Allow opportunity gaps to close, allow people to have more presence. So let’s use it to enhance the human condition, not to replace human connection. 

I love tech, but I don’t love tech to replace the powerful role that parents and caregivers play in building children. 

In your book, you talk about the importance of owning our imperfections. How does that make us more human?

One really amazing experience in writing this book was reflecting back on the imperfections of humans and our relationships. We’ve always looked at them as bugs. How can we be better and more perfect parents? But the truth is that “good enough parenting” is an evolutionary gift that actually teaches kids how to be human. 
 Those missteps and ruptures and repairs help us learn how to be good partners with other humans. 

In your new book, you wrote, “AI has the keys to unlock the social gate,” which you define as “the biological filter built into the infant brain that evolved to allow a particular type of teacher: a human one.” What are the ramifications of this breach? 

We may not know everything about the technologies that are being built, but we know a whole heck of a lot about how children develop and how they can best develop to their full potential. In some ways, this book was about understanding the interaction between technology and children and adults, but also understanding how the human brain is built. 

In some ways, evolution gave us a mechanism to ensure that baby’s brains develop through human connection. Kids don’t learn from TV. It affects their language development and social development, but it doesn’t actually teach them. The social gate has made sure that babies only learn from human interaction. It opens it up and allows the learning to happen. And now that AI can mimic that human interaction 
 it has the passcode to the social gate, and whatever flows through from that technology is actively wiring that child’s brain. 

Patricia Kuhl [professor at the University of Washington and co-director of the university’s ] is a goddess of early brain development. Her research 
 is probably one of the most important things [for understanding] how to navigate AI in the early learning space. The basic science of human development is incredibly important not just for parents to understand, but policymakers and the people who are building this tech. They need to understand how we develop, so that they build tools that don’t inadvertently lead humanity in the wrong direction. 

And how much faith do you have in the people building these AI tools? 

I’m not going to answer that question, other than to say that I wrote this book primarily for parents and caregivers — for anyone who loves children. But I want desperately for those who are building technology 
 to read and understand it, so that they can more intentionally design with developmental science in mind. 

How could you see AI technologies supporting the early years? 

Number one, don’t displace that human connection that supports the parents or teachers in the children’s lives. We know about the administrative burden and the invisible labor that makes caring for young children so hard, so let’s build tools to make it easier for parents and teachers so that they can be more present. Let’s build tools that allow us to understand better how children are developing when [they] are exhibiting delays, so that we can more quickly ameliorate those issues. And there are scientifically driven tools that support the early learning process, but I don’t think that they should be used as replacements for teachers. 

What are some promising areas of research?

Brian Scassellati, who’s a computer scientist at Yale [and director of the ], showed that social robots can help teach children with autism spectrum disorder to learn social cues and become more connected with other humans. In the same vein, [that when] children read to social robots versus humans, they were less anxious. Using the science to help guide us in understanding the best ways to support children’s learning is great, but [we should] never forget that it must not replace human connection, it must only support it. 

I’m a physician, I come from the world of medicine. When we create new biologics, let’s say a vaccination, it’s not like we say, “Okay, we’ve created it. Let’s see how it does out in the real world and then go back and tweak and fix it.” No, we do really rigorous studies to make sure it’s safe for the population. And right now everything is being put on parents like, “Oh, you decide 
” It’d be like saying, “Here’s your car seat 
 let us know if it’s safe and we can go back and tweak.” These are really powerful technologies. We need a lot more science, not to stifle innovation, but to make sure our humans remain safe. 

We can’t wait for the research to happen to get guardrails in place so that no harm is done. We need longitudinal data in understanding children who are growing up with generative AI as a significant presence, in understanding the impacts on language, socioemotional development and attachment. We don’t have these. I want research on protective factors. What are the conditions in which AI tools generally support human connection rather than displacing it? It’s not just an academic question, it’s a design question that will shape the field. 

What are the skills that children are going to need to succeed in the future? 

Now that AI can do all the things that we were trying to optimize in our kids — like be the best at math and science — and all these hard skills now can be done by AI a million-fold better than humans, it’s those distinctly human edge skills that matter. The critical thinking, the social connection, the curiosity, creativity, resilience — those are going to be the skills that allow children to thrive in the age of AI. 

But recently I have noticed something I never expected.

Many of my more thoughtful and honest students are becoming critics of the very technologies that shaped them. They are tired of the slop. Tired of content created by code. They say they are repelled by the prospect of an AI friend or romantic partner.

When it comes to the classroom, they willingly admit they prefer a lively class discussion to an online activity. They resent teachers who use AI to grade their papers. And most powerfully of all: They admit their use of technology is a hurdle to becoming the educated Americans they know they should become.

They speak almost like addicts.

They don’t want to be on their phones eight or nine hours a day. They don’t want to use AI to complete their assignments and short-circuit their ability to learn and grow. They know their attention span is stunted.

But in so many circumstances, they simply can’t resist. These observations may sound anecdotal. Let me assure you, they are not.

Over the past year, a series of highly publicized incidents have suggested the emergence of something larger: the first widespread cultural backlash by young Americans against the digital world that shaped them.

Multiple public speakers recently referenced the AI revolution now upon us—a at the University of Central Florida, a at Middle Tennessee State University, former at the University of Arizona. In every instance, young Americans either passionately booed or, in Schmidt’s case, the remarks.

A community college in Arizona to read the names of graduates, but the system quickly malfunctioned. When the college announced what had happened, the backlash was both raw and immediate.

These incidents may appear isolated or trivial.

They are not.

Together they suggest the emergence of something larger: the first widespread cultural backlash by young Americans against the digital world that shaped them. The generation that grew up on iPhones, spending much of every waking hour online, now seems to be awakening to the perils of a digital world neither they nor their parents fully understood.

And yet these young people are increasingly lending their voices to a growing chorus of educators, parents and policymakers who have begun to realize a painful truth: There is no quick technological fix for the crises consuming the modern American classroom.

, , emotional distress and a generation-long erosion in students’ ability to concentrate all demand a fundamental reassessment of the role screens now play in the educational lives of our children.

Every other day a prominent newspaper or publication now gives voice to this fundamental truth.

Whether it’s The New York Times explaining or prominent Substack columnists offering a American educators should take advantage of this moment by defending traditional instruction rooted in foundational human relationships.

A generation ago, young Americans had access to a diverse chorus of influential adult voices that tethered them to the mighty responsibilities and possibilities of adult life. Children lived with two parents, numerous siblings and often spent considerable time with grandparents. Life exposed young minds to a faith tradition with pastors and priests on Sundays, sports activities with coaches after school and maybe Boy Scout or Girl Scout leaders as well.

Many of these voices from ages past are silent today.

But that doesn’t mean our children don’t hear voices. They do, and they often come from people and digital spaces their parents never would have chosen for them.

This is why — especially in this era — the humanity of teachers and the personal vitality of our classrooms are essential. The voices of instruction our children hear should be the voices of teachers who know and care about their students — not Alexa, not Siri, not some anonymous digital interface designed to maximize engagement rather than human flourishing.

Eye contact. Conversation. Personal relationships. No code or product required.

The kids know this. The kids want this.

Shame on us if we fail to give it to them.

But educator and author Stephanie Malia Krauss says ditching devices isn’t enough. If we want to improve young people’s academic results and well-being, we must focus on how schools can actually meet their needs. Removing devices without addressing stress and safety, among other issues, will be an empty gesture.

An expert on , Krauss began her career as a Teach For America teacher in Arizona and founded a competency-based high school in St. Louis. She has since led national youth readiness initiatives and consults widely. In her new book, “,” Krauss argues that schools have let addictive technology, stress and chronic busyness strip kids of basic human needs like sleep, play, wonder and connection.Ìę

She has coined the term to describe what schools can do to protect these needs. Âé¶čŸ«Æ·â€™s Greg Toppo talked to Krauss about how the current tech backlash offers the opening educators need to focus on a version of school with students at its core.Ìę

Their conversation is edited for length and clarity.

You begin the book by observing that wherever you go, you ask people how they’re doing. The answer is always the same: “Everyone’s overwhelmed, stressed and exhausted.” Is this just a COVID hangover, or is something else happening?

I started asking the questions, “How are your kids?” “How are your families?” and “How are you?” as a way to understand the impacts of COVID. I was doing a book tour in my basement, and I couldn’t fly to be with the schools and youth programs I was speaking to. And at that point, the overwhelm, overload, stress and exhaustion that people were reporting made sense. I felt it. We were all experiencing the existential dread of a global pandemic.

But I decided to keep asking the questions, and six years later it’s hard to still consider COVID as the consequence for what’s driving the overwhelm in our lives. Having surveyed tens of thousands of adults caring for kids personally and professionally across an incredible variation of communities and contexts, the results have shown that people are more overwhelmed and overloaded and stressed and tired than they were six years ago. There’s only been one exception, in one community. And that was when I asked a group of hundreds of early childcare educators how the kids were. And the answers were: “Full of joy, curiosity, excitement, enthusiasm, unending energy.”

Something is changing between the early childhood years and the start of school.

Well, maybe we should be talking to them! Are they doing something right, or is something happening with four-year-olds that we need to focus on?

What is interesting to me is that at the bookends of our life, the early years and the elder years, the responses are often the same. Young kids start out as curious, joyful, full of energy, contributors. They want to help. They’re playful, creative. And when researchers study centenarians, people who live to be 100 or older, you often hear the same thing: These are people who have found ways to contribute, stay playful, keep moving, be curious. And in my mind, that’s a reflection that the overwhelm we are feeling today is not a personal failure, it’s an environmental one.

Let’s talk about classroom technology. You’ve said that digital apps and algorithms “exploit students’ developmental vulnerabilities, and that schools need concrete strategies to prioritize human essentials.” It seems like schools are starting to get this message — see recent phone bans and the nascent anti-screen movement. But you think this isn’t going far enough?

Toxic tech — tech that hooks and harms our kids — is what I call one of the “dangerous weather conditions” of modern life. Addictive, manipulative tech is often designed in ways that restrict some of the best parts of being human. So young people go on addictive platforms, for example, and they’re looking to have their normal developmental needs met. They’re curious, they want to connect with somebody, they’re bored and looking for entertainment. A teacher or adult told them to, and then they end up on a platform that’s designed to keep them coming back.

I think about this as “ultra-processed content,” designed to be hyper-palatable. But it’s fake, and kids are less likely then to want to socialize, play or experience recreation outside of something designed to be available 24/7 and feel much more fun and connecting. In the classroom, computers can sometimes be brought in to address capacity issues, cost issues and burnout issues — and they are, like ultra-processed food, a convenience and a cost saver. It’s still a level of ultra-processed content. 

Any time that we have technology that is keeping kids from the very things they need to be healthy and happy, and also to learn and develop, I consider that toxic tech. And any time we have technology that actually assists or amplifies the ability to tap into the essentials that keep us healthy and happy, that’s humane tech. It’s technology that should stay. Right now, many schools are moving in the direction of seeing the antidote to addictive tech being the absence of technology. I would say the antidote to addictive tech is the abundance of healthy developmental opportunities that promote human essentials like play and creativity. We don’t have to remove all tech, we need to remove toxic tech.

So, what would be an example of tech that is not just non-toxic, but humane?

I interviewed boys at a private middle school in Richmond not too long ago, and they told me how technology is used for research, for projects, for really exploring things that young people are interested in or curious about. Kids can explore their capacities for wonder and creativity and or focus. They can learn something new, they can think about their personal interests, explore their identity — and then the technology goes away, and they’re reading books and talking about books and doing things outside. Technology is an enabler and encourager of essentials, rather than prohibiting the essentials in the first place.

How do you read the current anti-tech moment we’re in?

My worry is that the removal of devices will address symptoms that we’re seeing that sometimes relate to the consequences of harmful toxic tech, but also have roots in other places. When I was writing the book, the first question I had was, “Why are we all overwhelmed at such an intense level, and why is it getting worse? Hard lives are harder, and times of stability are still stressful.” And what I found was that there were four universal forces at play, toxic tech being only one of them. If we go into schools and remove computers and other digital devices without attending to the other “dangerous weather conditions” that I talk about — being overtapped, overworked, and overwrought, really afraid for our lives, safety issues that students feel — we’re going to see the persistence of the problems that right now are sometimes being exclusively attributed to technology use.

The idea that people are overwhelmed more broadly is not something that I hear discussed in this context. The only thing people are saying is, “My kids are overwhelmed by screens, so we need to remove the screens.” 

Yes.

You’ve been on this listening tour, and I gather that kids are really interested in talking to adults about cellphone bans. What do they want us to know?

I’m wrapping up a statewide listening tour of Virginia middle schoolers on behalf of the , which is a statewide partnership of youth development organizations, school districts and education groups. And in every conversation with middle schoolers in Virginia — a state that did statewide phone bans — they have wanted to talk about it. And the answer is almost always the same, which is nuanced, and we have to give kids credit for the nuance they bring into conversations about devices, computers and AI. 

What I’ve heard over and over is, “I like not having my phone when I’m learning. It’s easier to focus, it’s easier to pay attention. I’m not as distracted.” And then, from Appalachia to Alexandria, I have heard, “I don’t feel safe.” And what I have come to understand is that for kids, phones aren’t only communication devices, they’re comfort objects. If I have my phone and something terrible happens — a school shooting, a disaster, something scary — that is my way to get in contact with my family and to be safe. And when phones were removed from classrooms, schools either did too little or did not attend at all to the safety needs that crept in.

Aside from students saying, “OK, you took my comfort object,” what is the upshot? Are there behavioral consequences? Are there bigger mental health consequences?

I think so. In conversations with kids, it is clear that every day they come to school worried that something bad and dangerous can happen to them. Without addressing that safety concern, kids will be more dysregulated in the classroom. And we often confuse dysregulation with discipline. The signs of dysregulation look nearly identical to how we would characterize a misbehaving student or a lazy, disengaged student. Teachers receive little to no training on the science of dysregulation, but it’s possible that some of the problem behaviors that they are seeing are really dysregulation and not discipline.

You write about the behaviors that get kids into trouble at school — to your point about dysregulation — and you say, “These kids don’t need detention, they need a nap.” Are we missing a key success factor here? Do high school kids need a nap? 

The is absolute that tweens and teens should not do school or anything before 8:30 in the morning, because their body clocks shift the moment they hit puberty. They are wired to want to go to sleep later. It has ancestral origins of young people staying awake later at night to protect their families, which means we wake them up hours before they’re ready and deprive them of the types of sleep they need for learning, memory, emotional regulation, and a whole bunch of other really vital things for learning behavior and future success. As the mom of a high schooler who starts school too early and who is also learning to drive, I will say personally this feels very consequential and even dangerous. We would be amazed at the improvements in adolescent mental health, adolescent learning and motivation if kids simply felt more rested, in the same ways that we feel profound impact as adults when we feel well-rested.

You write about the importance of play and movement, even suggesting that older kids need recess too. I’m guessing without their devices?

Yes. Whether we’re talking about sleep and regulation, or we’re talking about play and movement, this is all a part of a broader move to say it’s really time to “rehuman” schools, to have a version of human-centered schools and education that really protect and prioritize our natural human capacities to learn and develop, but also to endure and enjoy life, to thrive. Across millennia, humans have relied on play, for example, to prepare, to heal and to learn. So, when we look at studies of hunter-gatherer communities, we see that young people up until the time they transitioned into adulthood spent about a third of their time playing. So when humans are kind of left to their own devices, play becomes a really primal need that we share with every other social species. And groups like the National Institute for Play have shown that we need play at every age and stage of life, from the early years through elderhood. So adults in schools also should be asking, “How can my work be more playful? How can my engagement with students be more playful?” And when play is prioritized, there are physical and psychological benefits, in addition to quality-of-learning and work benefits. Movement? Same thing: In the studies that I looked at, for “How We Thrive,” they call sitting the new smoking, saying that if you sit for six hours or longer in a day, the health harms are equivalent to a pack of cigarettes a day. And yet we tell students that a good student sits in their seat, doesn’t get up, stays seated, and then moves quickly through the hallways in a pretty controlled way. But by injecting small moments of movement or small moments of play, we improve not only the culture of our classrooms, but also, ironically, the performance and quality of work that students can do.

Speaking of play, it strikes me that what happens after school, after the bell rings, is worth paying attention to.

In doing the statewide listening tour of middle schoolers, I heard from young people every time about the vital importance of their afterschool and summer programs. And there were instances of schools that bring that kind of youth development programming into the regular school day, and in those cases that was what I heard the most about. These are activities and experiences that lead with young people’s abilities to engage socially, to engage in play, to engage creatively, to celebrate each other. In this moment where we’re talking about screens and devices and what to take away, I want us to talk about what to give back. Too many kids can’t afford or access these afterschool and summer programs, and they matter more than ever. 

To bring us to the beginning of the conversation, the antidote to addictive, toxic tech is not the absence of devices, it’s the abundance of healthy, positive developmental experiences, which are often found in afterschool and summer programs and experiential project-based learning in the classroom.

But from where I sit in Birmingham, Alabama, the focus in Washington and in many statehouses misses a crisis just as deep and consequential as the one over whether kids spend too much time on TikTok. 

The more urgent issue for millions of families is that too many children and adults lack the digital skills employers now require. That gap is not driven by overexposure to technology but by uneven access to it. Alabama is far from the only state facing this challenge. Roughly one-third of U.S. workers , even as 92% of jobs now require them. 

Concerns about technology overuse deserve attention, but they shouldn’t crowd out the work of building stronger pathways into the digital economy. That means ensuring students and adults have the tools they need, along with access to the instruction and hands-on training that lead to employment. Policymakers and state leaders should be just as focused on helping communities build the workforce pipelines the economy depends on as they are on mitigating screen time. 

As a parent, I understand concerns around screen time on a personal level. My wife and I think about it constantly with our 13-year-old son. Like many families, we have set limits. We decided early not to give him a smartphone until he turns 14. We would rather he spend time outdoors, read real books and experience the world away from a screen.

At the same time, I lead a nonprofit organization whose mission is to prepare young people for the future of work. From that vantage point, I know something else is true: My son’s comfort with technology will play a major role in the opportunities available to him as an adult. Those two realities must coexist, but only one seems to be driving magazine cover stories and congressional hearings.

For many students, school is the only place where they can reliably access a laptop, high-speed internet, or guidance from someone who understands how these tools actually work. In Alabama, lack adequate internet access at home. Nationwide, lack access. Unfortunately, even inside schools, opportunities to develop meaningful technology skills remain uneven. Only of U.S. high schools offer computer science courses at all, and of elementary students are enrolled in computer science learning experiences. As a result, many students never get the chance to learn how technology actually works.

The stakes around this gap are rising quickly. According to the World Economic Forum’s , technological skills are expected to grow in importance faster than any other skill category in the next five years. Artificial intelligence and big data top the list, followed by networks and cybersecurity, and technological literacy more broadly.

Students cannot easily gain these skills from worksheets. They cannot learn to code on paper alone. Educators cannot prepare students for careers in cybersecurity, robotics or digital design without placing technology directly in their hands. And students cannot meaningfully understand artificial intelligence without interacting with it. Yet many policy conversations treat technology in schools primarily as a distraction to be managed rather than a skill set to be developed.

Every school should have dedicated learning spaces where students can experiment with coding, explore AI and develop creative skills. These spaces should be guided by educators who can teach not only the technical skills, but also the ethics and responsibility required to use these tools wisely. Across the country, some schools are showing what that hands-on approach can look like.

At outside Birmingham, for example, a newly built learning lab provides students with access to a podcast studio, music production equipment and video editing tools. Students use the space to produce original music and record podcasts. Meanwhile, at Robert C. Hatch High School in Perry County, a tech-forward space — which was developed through a partnership with the State of Alabama and Ed Farm — combines in-person and remote instruction to expand learning opportunities for students in a rural district.

These schools remain the exception, not the norm, with many districts lacking funding, infrastructure and training. State and federal policymakers should treat that gap as an urgent priority. Digital fluency is now as foundational as reading, writing and math.

Parents, educators and policymakers all play a role in setting healthy boundaries around technology use. Students should not spend every hour of the school day staring at a screen, and devices should not replace teachers or human connection. But schools that lack meaningful access to technology leave students just as unprepared. If the national conversation continues to focus only on keeping technology out of students’ hands rather than putting it within their reach, too many young people will be locked out of the opportunities that define the modern workforce.

That experience stayed with me long after graduation. It taught me something that is becoming increasingly vital in today’s digital world: Confidence is not developed by having the perfect answer but by believing in authentic ideas. 

Many young students are losing confidence in their own ideas. It is undeniable my generation grew up behind screens, with videos and phones embedded in everyday life. As a 16-year-old high school student, I don’t consider artificial intelligence a futuristic invention but rather a simple extension of the world we already live in. That is why I believe many conversations about AI in schools are missing the bigger issue entirely. The real concern is not technology itself but what happens when student voices become overshadowed. 

Technology has not only changed the way students learn but also the way we communicate on a daily basis. Many young people have become highly skilled at digital tools, with FaceTime and Zoom becoming frequent parts of everyday life. It is a common joke that if our generation does not put their phones down, they will forget how to talk to someone on a date! There is immense truth behind that. As we become more accustomed to immediate responses, we are slowly losing the patience to sit with a thought.Ìę

About 54% of students already use AI for schoolwork, a number continuing to rise. AI can certainly help students organize their thoughts. I even used it during the brainstorming process of this piece, but the important aspect is that my writing remained a reflection of my voice. With this expanding access to knowledge, an important question remains: Where does student voice fit in? If students begin relying on AI from the moment their education begins, they risk losing the discomfort that comes from developing confidence in their own ideas. That uncomfortable part matters. 

I have personally experienced how being in an uncomfortable position can lead the mind to function in ways AI cannot replace. It was the day of a long-awaited DECA business conference, and I put on my dress and blazer, a stark contrast to the comfortable clothes I wear at school. I had recited my speech until I knew it by heart, yet during the presentation, I felt the pressure set in and my thoughts begin to blur.Ìę

For a moment it felt as though all my preparation disappeared, but after taking a moment to slow down, I looked back at my notes and continued. It was not the polished performance I had imagined, but by the end I had conveyed what I wanted to say. More importantly, the experience taught me something AI never could, which was how to recover in real time.

Through these experiences, I have seen how much students can grow when they are asked to use their own voices. Recognizing this, I am proud to lead a summer program in the Chicagoland area called First Voice Academy. 

Here, middle school students learn and practice public speaking in a low-stress, immersive environment. Designed to promote interpersonal connection, the program walks participants through the importance of communication and concludes with their delivering a speech on their own. Taught directly by high schoolers, the program gives younger students the opportunity to learn from peers who have faced many of the same obstacles.

I can envision a student struggling to present during a speech, but that is the key: The words are theirs. The goal is not to ignore AI, but to ensure younger generations have opportunities to develop confidence in their own ideas.Ìę

Artificial intelligence can polish language perfectly and respond to a question in under a second, but what it cannot do is replace a student’s voice. Confidence is gained when students believe their own thoughts are worth sharing, even when they come out wrong the first time. Students who feel they have a voice at school are seven times more motivated to learn. 

A program like First Voice Academy allows for real world experience while in a learning environment. If similar programs can be expanded into schools, more students would build interpersonal skills at a crucial point in their lives.Ìę

I think back to the moment I stepped up to give my graduation speech and how scared I was. All it took was to say the first word, and suddenly I felt connected with the audience. Student voice offers a perspective unlike any other, and simply needs the opportunity to be heard. Technology may help develop ideas, but true opportunity comes from nurturing one’s own voice.

In this episode of Class Disrupted, Netflix founder Reed Hastings joins Michael Horn and Diane Tavenner to discuss his decades-long journey through various chapters of education reform and how it’s shaped his view around artificial intelligence shaping the space. Reflecting on the slow progress and setbacks of past education initiatives, the episode dives into the potential of and urgency for harnessing AI for accelerated, mastery-based learning and global impact. Reed shared what he believes reinventing traditional classrooms means for edtech entrepreneurs.

Listen to the episode below. A full transcript follows.

Michael Horn: Remember how much fun it was to have Reed Hastings join us on Class Disrupted in the beginning of the season to dish on AI and education? So much fun that the ASU + GSV Summit said, why don’t you invite him back and let’s do it again. So that’s what we did. Six months later, Diane Tavenner and I welcomed Reed Hastings live on stage at the ASU + GSV Summit to talk AI, his different chapters in education, lessons he’s learned, and where he thinks the puck is going. Enjoy all of that on this episode of Class Disrupted live from the ASU + GSV Summit and sponsored by the Learner Studio.

Michael Horn: Welcome everyone to the Class Disrupted podcast. This is our seventh season doing it. Normally we are disembodied voices on a screen talking with each other and a guest, but tonight we’ve got a live audience and we want to thank the ASU + GSV Summit and all of the amazing staff that has put this on. Huge thanks for all of them. Please. And you are all here to make a lot of noise and make this fun, right? Diane?

Diane Tavenner: Indeed. That’s what we want, is a spirited conversation.

Michael Horn: So who do we’ve got on tap?

Diane Tavenner: Well, tonight, Michael, we have an incredible guest, someone we’ve talked to before, but it is time to do it again. We have Reed Hastings with us. And most people know Reed from Netflix. A lot of people know that he spent time in education. What you might not know is that for the last year, Reed’s been on the board of Anthropic. He’s done a deep, deep dive in AI, the recent version, and 40 years ago earned a master’s in AI.

Michael Horn: So Reed, you’ve also had a long set of experiences with education over the years. You were a Peace Corps member, teaching maths 40 some-odd years ago, I think. And 20 some-odd years ago, you were the chair of the California State Board of Education. A testing period, No Child Left Behind. You had this guy named Roy Romer in Los Angeles as the superintendent for some seven years. What did you learn from that period of time working in education?

Leadership changes in education systems

Reed Hastings: Well, that was a time of great hope. We had No Child Left Behind, Reading First, high school exit exams. We had an accountability system and I was really an administrator of that on the State Board of Education. And we worked hard on all the technical details and there was some real progress. And as you mentioned, Roy Romer was very successful as superintendent. He made it seven years in LA Unified as superintendent, set a record for that and put in a lot of great programs that really raised scores and achievement, learning. And the tragic thing was the next five years after that, I watched it all get dismantled, independent of its results. It sort of, you know, politically wasn’t in favor.

New administrations elected, they are like, get rid of the old guy stuff and let’s put in different stuff. And so that was true at the school district level and that was true at the state level. And it really woke me up to the hero syndrome we have. And whether it’s Tom Pazant’s great work in Boston now getting dismantled or Houston, Mike Miles is the hero today. And you know, watch what will happen in five or 10 years from now, because that’s where Rod Page was so great 40 years ago. Of course there was Joel Klein in New York that so many people worked hard on. So we see this cycle of rise and fall. And I have to say, for all the work that I did and all that state board, there’s very little to show for it.

Diane Tavenner: So in another chapter where we met, was in the charter world, and you’ve been on the board of KIPP national for 20 years now. You have supported countless of us who have been in the work throughout that time, you know, the City Fund. And this is a long-term strategy for you. What are you learning from charters?

Reed Hastings: Well, I would say charters haven’t failed, but they haven’t succeeded at driving up NAEP scores in the high charter states, say like Arizona, Texas, Florida. And of course unions have fought us to a draw in deep blue states and then in red states we’re able to grow and we’re investing in, again, Florida, Texas, Arizona, Georgia, so lots of states. But even, you know, after 20 years, we have a good success at the city level. So at the city level, it’s actually the only thing that’s driven citywide improvement for all kids is high charter share. So if you look, PPI did the graph, the scatter plot showing that cities with low charter share, Portland, Seattle, have had no improvement in closing the gap of achievement between poor kids and all kids over the last 25 years. And then you start walking up the cities that have 10% charter share, more improvement, 20, 30, 40, 50, like Newark, Camden. And then you get to New Orleans, which has the highest gap closure in the nation over the last 25 years. And of course that’s 100% charter.

So charter is still promising, but like grindingly hard and slow. Think trench warfare, but it hasn’t been reversed. OK, so a lot of positivity and I continue to be a huge donor in that space and continue to believe in it on maybe a half dozen boards of charter networks.

Michael Horn: So the third chapter that you then went in on an education is when I met you, 2010, the very first ASU GSV summit, you were there and you were getting involved in education technology, EdTech and DreamBox Learning, of course. And there’s been a whole wave, sort of cresting, if you will, with EdTech. What’s your take on that chapter?

Reed Hastings: Yeah, well, Rocketship was using DreamBox Learning, and I knew it through there, and I thought, OK, here’s a great opportunity to take this amazing software. And obviously computers transform everything. And so if we could just get some investment in DreamBox and get it bigger, it would surely transform both district schools and charters. And again, grindingly slow. Turns out that selling to school districts is really hard. The only thing harder is selling to charters because they’re small, so the money’s on the district side, but grindingly so. And then, you know, DreamBox was one of the early adaptive learning, you know, let kids go at their own pay systems.

But school districts kept telling us to turn that off, please, because they wanted to catch kids up to grade level, but they definitely didn’t want to get kids ahead because if the kid gets ahead, then they’re disruptive and bored in the class. So catching kids up to make the machine work better, very much valued. Letting kids get ahead, which sort of threw sand in the machine, not valued. And so it was an early lesson in sort of the depth and strength of the grammar of schooling that we have.

Diane Tavenner: So if I sum up these three chapters, state policy, district work, pop of success gets wiped away. Charters making progress haven’t failed, grindingly slow ed tech, no real discernible change yet. I know you’re not trying to depress us. I know you’re trying to help us know that you’re learning and still in the game, which we know you are. So let’s get back to AI. When’s it going to cure cancer? When is it going to figure out fusion so energy is free? When is it going to autonomously drive us all over the place so we don’t have to deal with parking lots anymore? When is it going to make our lives better?

Rapid AI advancements predictions

Reed Hastings: By the end of the summer? Predicting AI is tricky because it’s growing so fast in quality. You know, it was three years ago when ChatGPT came out and it could barely do third grade math. And now all of the major AI systems are very impressive and they’ll continue to improve. And what’s happening is we’re on one of these curves where it’s, let’s call it doubling every year in quality. So it will be twice as good as it is today a year from now, and then twice as good, and then twice as good and then twice as good. So whatever challenge you think AI is not up to, just wait a year. OK? And so that’s the amazing thing. And there’s no guarantee that the exponential will continue forever, but it has been the last several years and you know, it’s getting very, very impressive at many scenarios like the ones you talked about and many others.

So the amount of change that we’re going to see in our society, mostly positive, but there’ll be some negative, from AI getting better and better is hard to grasp because of this doubling, doubling, doubling. You know, just when you think we’ve got it like situated like how’s it going to work with society? Then it gets even better again. And so we’re in for the ride of our lives, both on the positive side. So curing cancer, energy, you know, abundance, these kinds of things and on the stress side of everything is different than it was when we grew up.

Michael Horn: Well, that’s the question I want to ask you because not only is there this anxiety and stress, as you know, people are also worried, will people get hurt as it gets better? And you know, you can imagine a myriad of ways that could play out. What’s your take on how do we prevent people from getting hurt?

Reed Hastings: Yeah, and I mean, again, that’s happened with some tragic cases of, you know, teens and suicide already. And look at the societal level, we make certain choices, sometimes explicitly, sometimes implicitly. And we tend to accept the choices that are already made for us and be scared, scared about new ones. But for example, you know, we lose 40,000 people a year to car accidents in the U.S. and about half a million globally. And if we just ban cars, you know, we wouldn’t have those deaths. OK, but we’re not willing to pay the price. So implicitly we’re making a trade off of 40,000 U.S. deaths a year. So I look at it and say, you know, is it as powerful as a car? And if it is, then I’m like, I know where society is in making those trade offs.

So I don’t want to pay that price. I don’t want to see 400,000 or 40,000 a year deaths. But I think when we get all excited about four deaths, we’re sort of losing perspective about the size of the prize and the other trade offs that we have and continue to make in society. So, you know, AI, I think will reduce deaths, and in particular with self-driving cars, that should be able to eliminate 90% of those 40,000 US deaths through self driving if we can get that adopted. OK, but then you see the story of the one Tesla death that happens. And again, that death’s tragic. I’m not trying to take away from it, of course, but in comparison to all the lives that self driving is already saving, it’s quite small.

Diane Tavenner: So let’s take that into education now, because one of the things that I love about you is that you keep learning and you stay in the work when a lot of people leave, and I know that there is a fourth chapter that is going to be written in your work and it’s going to involve AI. And so what does education look like in the age of AI? What does school look like in the age of AI? What does learning look like in the age of AI?

Improving education over the years

Reed Hastings: Yeah, but in my first 25 years, I’ve spent the time trying to do the better classroom, whether that’s from the state board level and testing and assessment, how do we make schools and classrooms better, whether that’s using ed tech like DreamBox Learning to make the classroom better. Charter schools, which have had some progress in making the classroom better. But it reminds me of the story about steam powered factories in the 1800s. So in the 1800s, all of our factories had a big steam plant that burned coal and rotated an engine. And then throughout the factory we had a rotating rod which carried power through the plant. And then we had belts and pulleys and wheels that then spun the individual looms or other machines. And these were highly developed, mechanized, and lots of belts and pulleys throughout the factory. You know, lot of productivity.

Then electricity comes and we replace the big steam engine with a big electric engine. And that saves some money. But real productivity of the factories didn’t change. And this puzzled economists for a long time. And then people started saying, hey, the power distribution system, all those pulleys and rods spinning, that’s the problem. And if we get rid of that and then go to individualized electric motors, so each loom has its own motor, then it can be designed sideways because the power is not all in one direction. Then it’s variable speed. You can turn off some motors and turn on other ones and all of these subtle effects.

Then we had a huge increase in factory productivity from basically using electricity the way it should be used in lots of small, relevant motors, rather than replace the one big motor. And I remember hearing that story and thinking, oh my gosh, that’s what’s happening in education. We’re putting tech into the classroom and the classroom, the sage on a stage, is the power distribution system. The sage on a stage is holding back technology from its natural effects and its ability to teach children directly. And we have to be brave enough to try to do school without sage on a stage at all. OK? To have all of school be learning individually, your daily lesson plan from the system executing.

Experimenting with individualized tutoring

Reed Hastings: We want to maintain the social development so the person in the classroom really becomes a social worker. They’re specializing in learning and emotional maturity and doing valor-type circles and these kinds of things. But the quote “education learning” stuff all becomes individualized where it’s mastery based learning. And the question is, how much more would kids learn? So one experimental way to get at this is to think about Bloom 40 years ago, and Bloom said two sigma improvement from individual tutoring, but it hasn’t been revalidated in a large scale way in a while. And, and so one of the projects we’re doing is funding that and you know, take 50 random kids, median kids in a median school, and give them a full year of the whole school day individual tutoring and try to figure out, OK, how much more do they learn? And so Ben Rosen, who’s here at the conference, runs Recess.gg, he’s running this project and recruiting tutors. And so let’s see, for second graders in the ideal condition, how much can they learn? What is the rate of learning of typical human 7 year olds? And I think we’re going to see it’s a whole lot faster than one grade level in one year, when again, completely individualized tutor, they can do everything moral and legal. They want to help the kid learn more in that year. All kinds of motivational things, all kinds of different teaching techniques.

But again, it’s one on one, dedicated. And you might say, well look, you know, that’s so expensive, $100,000 per kid per year. It’s ridiculous. And I would say that’s what it is now. But with AI, it gives all the AI developers a target of what they’re trying to do and how much more learning. And what we want the world to understand is, no, there really is twice as much learning that could be happening per day, per hour than today, because I suspect that we’ll find that it is twice as much, which roughly means by the time you get to eighth grade, you know as much as a typical high schooler today or by the time you get to 11th grade, you know, as much as the typical college student today. OK? Because of the time compression and the learning and the stimulation.

And that would lead to, you know, not just lifting the bottom, which of course it does, but just a tremendous revolution in the possibilities of the human brain. And there’s a positive example of this. So about 25 years ago, Deep Blue beat Garry Kasparov in chess. And from then on, AI chess has been better than human chess. And so you might think, well, everyone stopped playing chess and it’s kind of gotten irrelevant. But in fact, chess has grown. And now the typical 10 year old on Chess.com is scoring way higher than the 10 year olds of 20 years ago on a stable, vertically scored system. And what’s happening is the 10 year olds are getting tutored by AI and the 12 year olds and 14 year olds.

And so we’re seeing this rise in chess talent because they’re individually tutored by AI. And so that’s true for chess today and could be true for biology and history tomorrow.

Diane Tavenner: I know Michael has a lot of questions, but before we just move, hold, hold. Because I don’t want this to get lost. And I think people often get confused when we talk about the power of individual tutoring. And they think kids are going to be learning by themselves. And that is not what you’re saying here. I know that’s not what you’re saying.

Reed Hastings: A dark room, nothing there, locked in. We can reuse containers. No, you want all the social development that we have today. So it’s real.

Diane Tavenner: Because those chess kids are playing chess with other kids.

Reed Hastings: That’s right. And if you just take the chess, if you just take the school day and say the time that’s direct instruction, sage on the stage now becomes individualized tutoring. And all the play time and all the time that’s do a project together stays as that. And in fact you can be. The teachers can then focus on that aspect of the day. And again, social, emotional learning, we all know is important. But imagine if the teacher’s an expert in it and focuses on that because understanding and doing well on the stuff that’s tested is done by the software.

Diane Tavenner: And by the way, sage on the stage is a very lonely experience anyway, so let’s not pretend.

Michael Horn: Speaking from experience. Well, I was gonna say you’re gonna finally disrupt class, which I’m thrilled by, but yes. But I’m curious because I talk to a lot of ed tech entrepreneurs at this conference and elsewhere. What’s your advice to them? Because they do a lot of times what DreamBox did, right, which is sell to the existing system, the districts, the schools, the sage on the stage. What’s your advice to them?

Reed Hastings: Yeah, it’s a great point. The short term is if you want to make money selling to school districts, make teachers’ lives easier. OK, don’t worry about learning too much. But if you make teachers’ lives easier, you’ll sell well. If you want to change the world, focus on the homeschoolers. Focus on people who are able to go at their own pace and build systems that are individualized. And as the benefits of that are more and more clear, not meaning 5%, but meaning twice as much learning, school districts will move towards that.

Self-learning education technology

Reed Hastings: And so if you build that now, you’re skating to where the puck is going, which is this individualized education. And so think of it as trying to do the pure play where you don’t need a teacher. OK? It is the self driving car where most of the market is like the map in the car to help the human. OK? That’s where most of our ed tech is. And instead we need to build the self-driving car in terms of innovation, which is the self learning, self teaching. And again, the AI is getting better and better at the emotional motivation.

So when you, you know, the vast majority of people seeking therapy today are getting therapy from chat, not from waiting a week and going and seeing someone at 80 bucks an hour. It vastly expanded the market. And you can say, well, it’s uncertified and that’s all true, but it is satisfying to people and it’s not perfect in any way. It is getting better and better rapidly back to that doubling. OK? And so the understanding, the emotional nuance of humans is something that actually the software is, is quite good at and getting better.

Diane Tavenner: And we could talk for days and days about how this leads to agency and self direction and entrepreneurial spirit and when they’re getting what they need.

Reed Hastings: Yeah, once you learn how to learn from software and from the interaction, the world’s your oyster because then you go off and you want to do physics or you want to do history again, a lot of it is there.

Diane Tavenner: So before. Yeah, let’s take it to the world. So what does this mean to the world you are working globally? CJ is here in the audience with us. Tell us about your work in Africa.

Sharing AI education globally

Reed Hastings: Yeah, it’s one of the most exciting secondary effects of this AI revolution is it’s very shareable when we figure out good teaching practices like Success Academy or KIPP, it’s very hard to export that to a Brazilian or African context. But when you figure out tech, it’s very easy to share. So, you know, if you think of Kibera outside Nairobi, people live in, you know, hundred or thousand dollar homes, you know, a piece of corrugated tin compared to our, you know, half-million, million dollar homes. So it’s wildly different, right? But if you think of their phones, it runs basically the same operating system that we run, it’s the same apps. It’s like barely any different. And so if we can figure out software based AI teaching that really does all the work, we can share that with the entire world. And so the project that CJ’s leading is trying to figure out one tablet per child in Rwanda, which is a great test lab. If that works as we hope, we’ll do the hardware and operating system level, and various application developers in the U.S. will do amazing work there.

We’ll put those together, and we’ll see Rwanda rise to be the most successful education state, first in Africa, maybe in the world. And that will then prove at that point, which is the formula is really one tablet per child around the world.

Diane Tavenner: No pressure, CJ, no pressure. Number one in the world.

Michael Horn: We’re going to get all these people you’re working with, lots of attention out of this and so that we can multiply these efforts. Live from the ASU GSV Summit. Thank you, Reed, for joining us on Class Disrupted.

Disclosure: Reed Hastings was a founding board member of The City Fund, which provides financial support to Âé¶čŸ«Æ·.

This episode is sponsored by LearnerStudio.

In the study, , two unnamed school districts carved out dedicated time for hundreds of elementary school students to work with a well-known AI reading tutor, either during class time or after school. Researchers followed about 350 students across two randomized controlled trials. All of the students were expected to log on for at least two 30-minute sessions a week.

They found that of the students assigned to work independently with the AI, just over 60% in the first district and 53% in the second ever logged on to the platform — at all.

Among all students, average weekly usage came to just over two minutes in District A and just over five minutes in District B.

Those who did log on averaged 13.2 minutes a week in District A and 25.8 minutes in District B, using the tutoring for just four to five weeks on average in an “intervention window” that ran from 14 to 31 weeks.

For Carly Robinson, the paper’s lead author and research director for the , the gap between access and use isn’t a shock. “As we’re talking about bringing AI tools into the classroom, the challenge isn’t just building good AI tools,” she said. “It’s getting students to use them and engage with them effectively.” 

That’s going to take “intentional design” that appeals to both students and their teachers, who must choose whether to offer access.

“Having these tools available, even if they’re really good, doesn’t necessarily mean they’re going to get used if they’re not being embedded into kids’ learning experiences,” Robinson said in an interview.

But she was careful to note that the study didn’t draw conclusions about AI’s effectiveness, or the degree to which students were interested or uninterested in the bot, saying many factors could be at play. “This is not necessarily the students not engaging,” she said. In the two districts, the AI platform “was likely one of many tools available to teachers.”

For the study, researchers randomly assigned a group of students to work on the platform alongside a few classmates and a human tutor whose job was to support their engagement and motivation and to troubleshoot any problems students might encounter. In District B, the tutors were actually middle-school students who “had a free intervention block in their school day.” A typical session included a short check-in, 15 minutes on the platform and a few minutes of reflection.

Pairing students with a tutor worked, Robinson said — to a point. Usage increased by roughly one minute a week in District A and 4.4 minutes in District B. The number of stories students completed each week jumped 71% in District A and 80% in District B. 

What the human pairing didn’t do was move the needle on reading scores: Neither district saw a statistically significant improvement in end-of-year reading achievement. But Robinson said the study wasn’t primarily focused on that. Rather it was looking at the overall impact of adding a human into the equation, someone who provides “accountability, motivation and relationship building.”

Wednesday’s findings mirror recent ones from Khan Academy founder Sal Khan, who that the rollout of his in 2023 was “a non-event” for many students. “They just didn’t use it much.”

Khan said AI tutoring doesn’t necessarily make students motivated to learn, or to fill in gaps in their knowledge needed to ask questions.

The new data also raise an uncomfortable question for educators: Among students who used the platform on their own, those who logged on tended to be higher-achieving and less likely to receive special education services. So the students who stood to benefit most from extra reading practice were among the least likely to get it. 

Robinson said she sees that as a red flag for anyone considering AI tutoring as a quick fix for underserved students: “I think it should give us pause about treating AI tutoring as an equity solution.”

Alex Sarlin, founder of the newsletter and a veteran industry watcher, said the new study “shines a light on several of the most persistent challenges in ed tech implementation: low usage rates that don’t meet dosage recommendations, differential technology usage based on prior student achievement, leading to lower usage among the neediest students, and a faulty assumption that students will jump into new tools without structured guidance.” 

The researchers’ approach showcases a promising direction, he said, “as it is increasingly clear that providing access to tooling is not nearly enough to drive usage, let alone outcomes.”

Amanda Bickerstaff, co-founder and CEO of , which provides AI literacy training to teachers, said results like these aren’t all that surprising, given what we know about these tools.

All GenAI chatbots, she said, can make mistakes, lack important context about students and how they learn best, and can provide biased outputs. Her group has recommended keeping these tools out of the hands of students through second grade, “and only with significant human oversight and AI literacy training” for students in grades three through five.

“At this stage, there has been little evidence that GenAI chatbot tutors meaningfully impact learning outcomes for students,” she said, “or that they are developmentally appropriate for students in elementary schools.”

Robinson, the study’s lead author, said she sees the usage findings as part of a larger pattern playing out as schools adopt AI tools more broadly. Schools, she said, should consider offering students “different iterations of these things based on what they actually need — and that’s probably a more likely pathway to scale than just saying, ‘Let’s give everyone an AI tutor.’ ”  

Historically, personalized instruction has depended almost entirely on human teachers, with the teacher-student relationship central to the experience. But advances in technology — most recently in AI — have changed this dynamic, Robinson and her colleagues write. Now, personalized instruction exists on what they term “a spectrum of relational intensity,” from a consistent one-on-one human tutor to a computer platform that students navigate alone. 

AI tutors may approximate human interactions, Robinson said, but students may still benefit from the care and companionship that humans provide. Logging on and sticking with something that might prove to be difficult, she said, is easier with a human in the mix. “There is just this component of accountability that a human can provide, where it’s so easy to look away or check out of something when it gets hard when you’re dealing with a screen.”

A researcher at , a nonprofit focused on human connection in the age of AI, Lee asked: Why not?

In a word, the girl said: Instagram.

“If you try to dance at prom, you’re going to look stupid at some point,” Lee recalled her saying. Eventually someone will pull out a phone and you’ll end up on someone’s feed, seen by “the entire school” with mortifying results. Better just to play it safe. 

“Everybody just goes to prom to look cute,” the girl explained, “take a picture for the ‘gram, eat and leave.”

For Lee, who has spent years studying human belonging, that exchange unlocked an important, if unspoken, part of why AI holds such appeal. “We’ve created this set of conditions where young people don’t feel like they have permission to be their real, unfiltered selves,” she said in an interview. So they turn to AI, which is programmed to affirm them at every step.

from Lee and her colleagues offer this insight among others, painting a detailed portrait of how young people use AI and why. They surveyed 2,383 people ages 13 to 24 across the U.S. and found that for nearly half of them, AI has already reshaped their relationships in ways that are largely flying under the radar of parents, teachers and policymakers.

Among the findings:

• Just 15% of young people are in relationships with “personified AI” characters — but for about 45%, AI is already reshaping their real-life relationships;
• 53% of young people say they set clear boundaries with AI, using it alongside — not instead of — human support;
• 61% say parents rarely or never talk to them about AI, and 53% say the same about teachers;
• Youth from low-income households are three times less likely as others to engage with AI, but they report greater feeling: 21% feel lonely often or all the time, compared to 6% of high-income youth; 57% feel like a burden to others, compared to 42%; and only 34% feel a strong sense of belonging at school, compared to 62%.

For the study, researchers sorted respondents into four broad clusters. About 28% rarely or never use AI, often out of ethical reasons or just disinterest. The largest group, 39%, uses AI primarily as a practical tool. They turn to chatbots such as Claude, ChatGPT and Google’s Gemini for homework and research, while keeping clear boundaries between AI and their emotional lives. 

Another 18% use AI for personal and relational support, such as venting about a tough day, seeking relationship advice and processing emotions. And 15% engage with AI characters and personas in more intimate, companion-like ways.

Within the four groups, researchers found nine variations that challenge the conventional wisdom around AI use. For instance, among those who use AI for emotional support were two very different groups. Rithm calls them “Social Processors” and “Private Processors.” While they may look similar from the outside — both say they have lots of friends and use AI to work through their emotions — surveys found that the Social Processors use AI as just one tool among many. The Private Processors, by contrast, use it as a substitute for real human interactions because they feel they can’t bring problems to those around them.

That data point could hold the key to understanding problematic AI use, Lee and her colleagues said, challenging the idea that lonely teens with small social circles are most at risk of unhealthy AI dependence. The data suggest something else altogether, said Kashyap Rajesh, a rising junior at Cornell University who consulted on the report.

“The driver of risky AI use is not necessarily isolation,” he said. “It’s feeling like a burden [to others] — and that came through in the research.” 

The number of friends a young person has, the size of their social circle, how busy they are, whether they’ve got family nearby and even their feelings of loneliness barely predict whether they’ll fall into dependent AI use, he said. “What actually predicts it is specific feelings: Feeling like a burden to others, feeling like you can’t be your real self, feeling like there’s no one to turn to.”

Julia Freeland Fisher, a researcher at the Clayton Christensen Institute who advised on the study, said that finding should help start a different kind of conversation around AI. “Burdening one another is building reciprocity, which is how we maintain the social contract, how we maintain social cohesion,” she said. That young people are increasingly bypassing this step should be alarming, she said.

“AI companions wouldn’t be nearly so disruptive to human connection if we had a sturdier social fabric,” said Fisher. “It’s the weakness of our social fabric that makes these [findings] so worrisome, not necessarily the technology itself.”

‘It just keeps feeling easier than the alternative’

For Lee, the finding on being a burden reframes so much of our understanding about young people’s relationship to AI. Virtually every survey respondent reported a specific “relational rupture” or crisis that made them turn to the technology. 

One young woman’s first question to a chatbot was, “I didn’t get asked to Homecoming — am I unlovable?” Another: “I got into a huge fight with my best friend, and I don’t want to tell anybody else because I don’t want them to take sides, so I needed to ask AI.”

“Story after story after story,” Lee recalled, “of a very singular, acute, discrete moment when they really had a moment of need and needed somewhere to put it.”

Rajesh, the Cornell student, said the data reveal a steady shift in which perhaps millions of young people are quietly moving from letting AI help with homework to asking it to mediate their emotional lives.

“They start off using it to help them write an essay, or help them prepare for their interview, or to study for an exam,” he said. “And they’re like, ‘OK, damn, this is really good, this is really helpful.’ And eventually their interactions escalate.”

The drift happens gradually, he said. AI helps draft an email or respond to a text. Next it’s helping to navigate a social situation. Before long it’s processing a breakup.

Rajesh, who’s studying information science and AI policy, said his own AI use crept up on him: He went from studying with Claude to creating personalized AI study guides to wondering if even attending class mattered. 

“I found that how many times I go to class and how actively I’m paying attention in class is actually not the biggest indicator of my understanding of the content or exam performance,” he said. “It’s actually just how much time I spend with Claude dissecting the lecture slides and building study guides that work for me.”

The report notes that because even productivity-focused platforms like ChatGPT, Gemini and Claude are engineered to interact with warmth and reassurance, what starts out as homework help or playful experimentation can evolve into a substitute for human interaction.

What adults get wrong

Alongside the findings on AI use, researchers found that how adults talk about AI is also potentially problematic: Their conversations are almost always about academic integrity — cheating, plagiarism, source citation — and rarely about relationships.

Rajesh said adults should be asking directly whether young people are using AI to process emotions, to rehearse hard conversations and to get support when they’re struggling. “Those are questions that signal to a young person that the adult knows this dimension exists and isn’t going to freak out about it — which is, I think, the prerequisite for any honest conversation happening at all.”

Michelle Culver, the Rithm Project’s founder and a co-author of the report, said young people tell researchers that when the topic is AI use, they’re “navigating it alone.” She suggested that adults approach the topic with “curiosity” rather than “judgment or shaming.” That could help both sides gain insight into each others’ struggles in the face of a technology that’s constantly challenging their reality.

In the same way that educators are worried that young people aren’t engaging in the “productive struggle” of learning academic content, Culver said, “We similarly worry that young people might offload the relational work to AI and become ill-equipped to handle the very messy human friction of real relationships.”

With many students and educators already using widely available artificial intelligence tools, state lawmakers and school districts are playing catch-up on AI policies.

In Maryland, for example, AI usage policies for K-12 schools are “all over the map,” Democratic state Sen. Katie Fry Hester said.

In some school districts, she said, AI use is encouraged, while in others it is restricted, or — a worst-case scenario for Hester — there is little to no policy guidance at all.

“What we heard repeatedly is that the teachers were feeling like they had to navigate artificial intelligence entirely on their own,” Hester said.

Hester said square one for lawmakers is AI literacy, which was the aim of new legislation that she sponsored and that was signed into law in May. It requires an AI coordinator in each school system, a statewide AI professional development for teachers and AI literacy to be a component of career readiness and computer science standards for K-12 students. It also requires the state Department of Education to provide certain guidance on AI.

Many other states have also been trying to create AI policies for schools. Lawmakers filed more than 134 bills across 31 states this year related to AI in education, focusing on data privacy, usage restriction in the classroom, literacy and training, according to MultiState, a government relations firm.

A survey by the Center for Democracy & Technology showed that (85%) reported using AI in their classroom during the 2024-25 school year, while 86% of students said they’d used AI for either personal or school-related reasons. But only about half of teachers and students reported that they received some training or information about AI from someone at their school, and few received training or information on risks of AI use.

A turning point for schools came with the rollout of ChatGPT in 2022, said Noelle Ellerson Ng, chief advocacy and governance officer for the School Superintendents Association. “AI was something that could not be gatekept,” said Ellerson Ng. “It was in the classroom the minute students were able to access it.”

Her association does not take positions on state AI bills or policies. But she said districts are trying to avoid knee-jerk, reactive policies such as New York City’s brief 2022 ban of ChatGPT because of fears about cheating.

Some states have made progress in laying the groundwork for AI policy in K-12.

Ohio has set a July 1 deadline for every school district, community school and STEM school to adopt an AI use policy. The state’s model policy recommends that districts address student and staff uses, privacy, ethical use, teacher-specific uses, vendor agreements, third-party AI tools and student assessments.

A new signed in March requires local school districts and charter schools to devise local policies for AI usage in K-12 schools, requires state standards for AI literacy and education training and ensures that no AI “replaces or eliminates a human teacher.”

enacted last month requires AI tools to be age-appropriate and requires teachers to review anything AI produces before using it in the classroom. It also allows parents to opt their children out of using AI tools. The law also directs the state education department to develop AI guidance and requires local school boards to set policies before the 2027-28 school year.

Yet even as schools are being sold on AI products by numerous vendors, there’s a growing skepticism about AI in classrooms. It follows a similar backlash about social media and digital technology’s academic and mental health effects on students, which has led to more states and districts putting in place bans and rethinking their reliance on laptops.

In the Center for Democracy & Technology survey, half of students said using AI in class made them feel less connected to their teachers, and 70% of teachers said they were concerned that students’ use of AI was preventing them from learning important skills.

Schools need to weigh the benefits of adopting AI tools in the classroom against their effect on student privacy, mental health and social skills, said Sue Thotz, director of outreach for Common Sense Media, a nonprofit advocacy group focused on technology and its effect on children and families.

Schools, Thotz said, may be the “only mandated safe space” where students can learn to use and access emerging technology. But she and other education experts believe districts need to increase scrutiny of products.

Globally, the market for AI products in K-12 schools was worth around $391.2 million in 2024, and could rise to more than $9 billion by 2034, , a market research company. That includes AI products for tutoring, personalized learning, automated grading, lesson planning and administrative tasks.

“When I talk about AI literacy, it’s not how to use AI. It’s understanding how AI is built,” said Thotz. “Why is it being created? Who’s profiting off of this?”

‘Giving a tool to children’

New York Assemblymember Robert Carroll said he uses artificial intelligence in his own work and sees its value. As someone who struggled with dyslexia as a child, he also thinks technology can help students with disabilities.

But he also wants to keep AI out of most K-8 classroom instruction. Students should learn basic subject matter first — in conjunction with critical thinking — and then later use the tools that can assist them, he said.

Carroll, a Democrat, has that would prohibit the use of most AI in K-8 classrooms, with exceptions for diagnostic testing and support for students with disabilities.

“It is imperative that all children gain strong foundational skills, especially in literacy and numeracy, and it seems that AI is uniquely positioned to possibly undermine that,” he said. “There’s a difference between giving a tool to adults and giving a tool to children who have yet to master skills.”

Rather than full bans, most bills seeking to restrict AI have opted to focus on age restrictions, parental opt-outs, oversight and bans on using AI to replace teachers.

This year, Florida’s would have included a statewide restriction on student access to AI instructional tools before sixth grade, with exceptions for use supervised by school personnel, English-learner translation support and disability accommodations. It overwhelmingly passed the Senate 37-1, but died in the House.

A adds computer science to the required public school curriculum, including AI and emerging technologies. Connecticut lawmakers in 2025 failed to pass aiming to stop AI from “replacing” public school educators.

Sophia Romee, the general manager of the GenAI Studio, an initiative studying how students and educators use generative AI at the College Board, the nonprofit that administers the Advanced Placement curriculum and SAT tests for high schools, said she is concerned that only that allow students to use generative AI have a formal policy governing its use.

The College Board’s research, Romee said, shows many students are worried about becoming too reliant on AI, and that adults need to give clearer guidance about where using AI tools for brainstorming, revising and tutoring crosses the ethical line into cheating.

“Students are far more self-aware about AI’s risks than headlines suggest.”

Like aviation in 1905

Jason Coley, director of the Center for Academic Innovation at Maria College in Albany, New York, said the policy debate needs to move beyond whether schools are “for” or “against” the use of AI.

“The better question is what kinds of AI use are supervised, age appropriate, transparent, and tied to real learning,” Coley said. Schools need guardrails around privacy, student data, bias, teacher training and equity of access, he said, but also permission to “experiment responsibly.”

Ellerson Ng, of the School Superintendents Association, said superintendents see AI as part of a larger umbrella of disruptive technologies in schools that has evolved from calculators to laptops to cellphones. The lesson, she said, is that overreactive policy rarely works. She also said schools should not cover AI in a separate policy, but as part of a broader technology policy.

“I don’t have a calculator policy. Why would I have an AI policy?” she said, describing how some district leaders think about the issue. “I have a technology policy.”

With past technologies such as cellphones and laptops, adults could often control when students had access, Ellerson Ng said. With AI apps and platforms, many students accessed the tools before teachers, principals or state officials were even aware of them.

That makes bans difficult, she said. Schools can block tools on school-owned devices and networks, but “you’re only one personal device away from social media and AI being in your schools.”

Justin Reich, an associate professor of digital media at MIT, said that uncertainty around AI should make policymakers cautious about declaring best practices too soon.

Reich said states are trying to regulate classroom AI at a moment when the field is still so unstable that “writing a guide for AI in 2026 is like writing a guide for aviation in 1905” before airlines, airports or even commercial flight.

“If you were to take any of the AI literacy documents, AI readiness documents, even the moratorium documents, and put them against a checklist,” said Reich, “there would be a lot of boxes in the ‘we’re making this up’ column and not a lot in the ‘we have evidence’ column.”

State lawmakers and school districts should be honest that they don’t know what they’re doing, are relying on limited expert information and that policy is subject to change with new information, Reich said.

“Lawmakers will need to be honest that what they propose now could be completely outdated in two years.”

is part of States Newsroom, a nonprofit news network supported by grants and a coalition of donors as a 501c(3) public charity. Stateline maintains editorial independence. Contact Editor Scott S. Greenberger for questions: info@stateline.org.

I have an unusual window into what’s happening in classrooms as CEO of DonorsChoose, which provides resources in 90% of U.S. public schools. Each year, 200,000 teachers post requests on our site.

Since the 2022–23 school year, requests for AI-related tools have surged more than 200%. But what’s interesting isn’t the growth. It’s the purpose.

Teachers are asking, overwhelmingly, for AI-enabled tools to reach students who have been left behind for decades: kids with disabilities as well as those learning English. In fact, 86% of requests are aimed at meeting the needs of students who have historically been underserved. In other words, teachers are turning to AI not only to save themselves time (although it can do that); nearly 9 in 10 are using it to get essential tools to the students who need them most.

For example, a middle school teacher near Atlanta requested AI-powered translation pens. With a simple scan, students can hear text read aloud or translated into more than 100 languages. For children who are learning English, or who struggle with reading comprehension, a $90 pen transforms their school day from frustrating to fulfilling. DonorsChoose has provided hundreds of these pens to teachers, along with more than 1,500 translation devices of other types.

In Chicago, an elementary school STEM teacher looked to AI to modify classroom materials when a child isn’t understanding a lesson.

In Miami, a middle school math teacher requested software that responds to students’ answers with immediate feedback that builds confidence rather than deflating it. Meanwhile, at another Miami middle school, a computer science teacher helps students get under the hood of machine learning by training robots to recognize and react to images. The project opens up discussions about ethics, real-world applications and how AI depends on what humans feed it.

In Detroit, high school educator Carrie Russell uses AI tools to effectively give every student a personalized tutor, expanding her capacity to teach each learner. She’s also mentoring other teachers about how to ethically and confidently incorporate AI tools into student learning.

These teachers aren’t asking for anti-cheating software or ways to monitor screen time, which is where much of the public debate is focused. They are experimenting and adapting tools that work for themselves and their students, without waiting for top-down guidance.

It shouldn’t be surprising that teachers are forging ahead and deploying AI in practical ways without directives from their schools and districts. Teachers have always been first responders to children’s needs.

In 2011, when American education underwent a seismic shift with states’ introduction of new academic standards, classroom teachers sounded the alarm on poor curriculum quality and misalignment to the new standards. Instead of waiting for the market or policy to catch up, they created materials that met the higher bar — and shared them with peers. 

More recently, on DonorsChoose, educators flagged the COVID pandemic’s effects on student mental health long before they became a national concern. We saw teachers request food for hungry students when SNAP benefits were disrupted last fall. And we routinely see teachers mobilize following natural disasters to replace what’s suddenly gone from their classrooms and restore some normalcy in their communities.

AI is the latest disrupter in education. It’s an opportunity to move toward a future when technology expands human potential rather than replaces it, where fairness is built into the design and where every student can experience moments of joy, discovery and magic. Teachers are showing what that can look like — one classroom at a time.

MagicSchool has emerged as a breakout artificial intelligence tool in education, with millions of teachers rapidly adopting it. But what’s behind that growth? How exactly does it use AI? And what does its adoption mean for teaching and learning? Its founder, Adeel Khan, joined Class Disrupted hosts Michael Horn and Diane Tavenner to go beyond the hype. Tavenner and Horn pushed for answers on the MagicSchool’s quality, privacy, and whether tools like this actually improve outcomes for students.

Listen to the episode below. A full transcript follows.

Diane Tavenner: Hey, Michael.

Michael Horn: Hey, Diane. It is good to see you. As always, it’s been fun because we’ve had this arc of really digging deep on AI with school models. And then we’ve been moving into the edtech tools that are starting to define a lot of current models, the new models we might see and so forth. And we’ve got another big one. Dare I say, for today, this is going to be a good conversation.

Diane Tavenner: I am very much looking forward to it and looking forward to digging in. But before we do that, we have a quick ask for all of our listeners. Will you all rate or review Class Disrupted, wherever you’re listening to it? And of course, please subscribe. We’ve never asked anyone to do this in seven seasons. And you know, this is — it turns out, this matters. So we’d really appreciate it if you would do that.

Michael Horn: Yes, we would appreciate it. And we know we get a lot of feedback from listeners. We certainly get a lot of emails, and texts and things of that nature. So we know y’all are listening, but we need to see it in the ratings, reviews and subscriptions as well. It helps other people find the information, too. As folks know, this is a passion project for us, and we want it to actually be influencing the conversation more broadly. So, look, we know we got a lot of folks tuning in from our great partners with Âé¶čŸ«Æ·, Substack, Apple, Spotify, YouTube, you name it, we know you’re out there.

But subscribe, rate us, leave us a comment, and we’ll just be appreciative for that.

Diane Tavenner: We will. And we will continue to be grateful for the direct feedback as well, because we use that. We take that in. And in fact, this conversation today is something folks have asked for. So we will not waste any more time.

Michael Horn: Yeah, no, it’s true. Well, I’m thrilled because we get to welcome Adeel Khan to the show, known obviously for MagicSchool. But before that — we’ll talk about MagicSchool in a moment — but Adeel worked as a teacher, assistant principal, founding principal for DSST, the Conservatory Green High School in Denver in 2017, and then left a few years — I think it was like three or four years in or something like that — to coach school heads. And my sense of — Adeel can correct me if I’m wrong on this — but my sense is, this wasn’t the calling.

A year later, I think it was, ChatGPT burst on the scene into people’s consciousness in November of 2022. And Adeel says, there’s something here. This can revolutionize the teaching experience and you know, quickly sees like, hey, teachers aren’t going to figure out how to use this productively. Let’s help them do that. Builds MagicSchool and it takes off like a rock rocket. No exaggeration. I think over 7 million teachers now use it. That number’s probably out of date.

As I say. It partnered with more than 10,000 schools and districts, I believe over 160 countries. So we’ve got a lot to dig in here. The use cases are broad. But Adeel first, welcome. It is great to see you again.

Adeel Khan: Thanks Michael. It’s great to see you too. And Diane, pumped to be on the podcast today, and thanks for the kind introduction. Mostly accurate.

Michael Horn: What did I miss? Yeah, what did I miss?

Adeel Khan: You got, you got it mostly there. I think certainly when I got to coach principals, I didn’t, it wasn’t my favorite thing in the world. Not because it wasn’t a great opportunity. I worked on a great team, and principals need support. But rather, I think, when I was founding my school and building it, I thought, you know, if you’d have asked me if I had the most important job in the world, I would have answered yes. And I just missed that feeling. I didn’t have that kind of builder-chasing, really ambitious feeling when I was in that new role. And it’s sometimes good to know that about yourself, right.

I think that like sometimes trying something and knowing that you know where you are because, you know, in some sense at the end of my principal career and seeing the graduates in the first class of seniors, as I kind of built the full school out, I was exhausted. Right. And sometimes you think about, you know, what’s the other side like, you know, and you know, being an advisor sounds less intense than being in a school building every day and you know, chase this big dream and, and then, you know, knowing what the other side was like, I was like, oh no, some people are wired for this. Right? Being in the builder seat and being… I learned that about myself. And even when things get hard today, and they do, we have a lot of challenges. I know that this is what I want to be doing and, and I would be, you know, I wouldn’t be as… there’s no grasses greener thoughts anymore. Even when those seep in, I’m like, nope. I know this is the thing.

Even when it is, I’m in my most trustful habit.

Michael Horn: So I’m really, I love it because you’re willing to make the trade offs for the thing you really want. And obviously we hope all our young people start to grow that same muscle, right, in their lives.

Diane Tavenner: That’s the work I’m doing right now. So it’s an inspiring story.

Michael Horn: Well, I was going to say. Right, so like we have two, you know, Diane, Adeel, you know, founding school heads become founding ed tech company CEOs. I think the story of the founding story of MagicSchool is somewhat well known, but maybe what’s less known, Adeel, is how you think about the problem that you’re actually solving both originally and today because you all have expanded your scope quite a bit. But let’s start at the outset itself because I think your framing tells us a lot about why you grew so quickly. So, you know, how do you describe what MagicSchool first solved for teachers and why that approach was so important at the time it came out?

Challenges using general AI tools in education

Adeel Khan: Yeah, so I think that there’s a part where there’s maybe a little revision on the way you describe the problem that I found, which was not that I didn’t think teachers would be capable of figuring out how to use this and use it productively, it was that the tool itself was a general purpose tool. It wasn’t built specifically for the domain and therefore it was clunky to use. And you know, at that time even really good prompting didn’t yield a really good result for teachers. It feels like, you know, ancient history that you had to have massive prompts for like an LLM to understand what you really meant in the prompt. But at that time it really was, so if you were prompting ChatGPT, it was like you could get something quality, but your prompt had to be a page long for it to really understand your context, really understand the format of your output, just all those different things. And if teachers were going to use it right away, we had to do some of that work to get them to a magic moment with the technology. A lot of teachers would report to me when I was training them on ChatGPT, which is kind of the way the product idea started was, you know, I was prompting it so much, it wasn’t saving me time, it was costing me time.

So there was like kind of a really acute prompt of, you know, promise that this has an opportunity to assist you, save you time and also make you more efficient, augment your abilities. But it’s not working for you right now or the vast majority of teachers in that moment. But I think of it as, you know, when I started thinking about building the product, it started with I actually thought I might be training teachers on ChatGPT, and that just didn’t work. I like, experimented with it. I went to my school and teachers didn’t really adhere to the product. So I was like, there’s got to be a better answer to this. I was savvy enough to kind of be looking around to see, like, you know, products that were coming out that were generative AI products. And they were like this first set of products that were hitting the market.

There was like Harvey, which was like AI for lawyers. I remember that really vividly. And I don’t think they even had a live product, but it was like announced as a product, and they’d gotten some funding, and there were news stories about it. And I was like, oh yeah, there’s going to be an AI that is made for each kind of profession. And that makes sense because each profession has its own quirks. It has its own domain knowledge, it has its own workflows, it has all these things that are specific to that industry. And a generalized alternative language model isn’t going to be the answer for every single one. So that was kind of the clear vision from the start, is we make a really domain-specific, intimate product for teachers.

And the products evolved a ton since then, but that core of it was like, it’s going to be really domain specific. Even in those early days, I thought to myself, we’re going to make some tools in this platform that if you’ve never worked in a K12 school, you would have no idea what that even means. Like, that was kind of my bar for myself is like, I want it to be so intimate, so clearly made for teachers that they scroll through that initial dashboard of tools and they see a couple things and they say, you know what? Somebody who’s worked in schools like, built this. I still hear that today. And it’s one of the things that brings me the most pride. Teachers don’t necessarily get built products that are so clearly intimate for them and know their work. And that’s what we hope to do and continue to do with the platform.

Exploring MagicSchool’s AI features

Diane Tavenner: This is such a good place to jump in because one of the things Michael and I are doing this season is trying to help people see how AI is truly being used in schools like today. And so building off that origin story and where you started and where it is today, I mean, let’s get into like how MagicSchool actually works and what, where the AI is in it. And, you know, this is the moment we get to kind of nerd out, which is fun. It sounds like you’re, you’re in that space too. Like, just to give people some context: When you enter as a teacher, it seems like there’s basically three big categories of support, teacher tools, writing feedback, and Raina the chat bot. And so, and then when I look at the list of teacher tools, you’re right, I open that up, I’m like, oh my gosh, there’s like 75ish tools. And we’re getting everything from like a worksheet generator to report card comments, unit plan generator, standards unpacker, IEP generator, quote of the day, classroom management plans and even tongue twister, you know, so like help us unpack.

Like literally what’s going on here in this tools section? How’s AI? What is the role of AI and what, how are teachers using this?

Adeel Khan: So one thing that I think MagicSchool does exceptionally well as a product is, while there’s a breadth of tools and sometimes we get the feedback that it’s overwhelming, it’s like this tension we feel between it being overwhelming and also really familiar for teachers. Like, I’ve gotten that feedback since the very start and I always hesitate to like change the UI or make some meaningful shifts there because it has kind of become our calling card that we’re this like list of teacher terms almost. Right. The UI itself has been replicated by 20 other products at this point because of work. Right. It’s like, you know when you say AI to maybe a skeptical teacher or a teacher who’s like, technology’s not actually been a meaningful part of their story in the classroom, or a teacher who’s maybe been sold on technology being a really big opportunity for them, and they’ve been burned. And there’s all kinds of reasons why teachers and technology have not gotten along particularly well.

I will be clear. I was not somebody who like, as a principal, saw incredible value in technology. My school didn’t subscribe to any boxed curriculum-type technology tools or platforms. We built all of our own, internal. We built all of our own curriculum. We didn’t subscribe to a single ed tech product outside of the LMS that we used. And that was a kind of a requirement. And the reason was mostly because I just didn’t know that they were going to drive outcomes for our kids. And it was an outcomes-oriented principle.

I wanted to make sure our kids’ literacy and MAD scores and their SAT scores were like, that was, kind of our focus was preparing them for college. And I wasn’t sure that any of the tools out there would make a difference. And I might have been just wrong. Like, you know, maybe they just weren’t in my world. And there’s some great tools out there, I’ve learned since joining the ed tech world. But nonetheless, I think there is that skepticism amongst folks, and there are tools that have come to schools and have done just about nothing. I mean, everyone who’s worked in a school district or building will tell you, like, remember two years ago, that initiative we had? What happened?

That is just like a common story, like a new curriculum that’s going to revolutionize the way that you teach and can completely, all those kids that are behind your class, they’re going to catch up in a year. Or here’s a new tech tool that is promising to do this. So there’s all this baggage around, like products that come into schools. And the baggage often is, I invested so much time and energy in implementing this thing, and I don’t know how much value I actually got out of it, and then the district gave it up in two years. Like, you know, like, there’s like frustration here. So if you think about MagicSchool coming in, like really novice, you know, founder — me, I was just like, let’s get people to value really quickly, right? Like, let’s just get them into what this thing can do so it’s believable.

Using generative AI in education

Adeel Khan: So you jump into MagicSchool, you see the rubric generator, you click on it, it asks you some pretty simple like form fill up type questions like, you know, what we’re going to do attach the document maybe of the assignment you’re going to be assessing with this rubric. All very simple, super easy to use. And then you click the generate button, and you get a rubric that looks like a rubric. It’s what you expect it to be. It works the way you want it to work. It meets your expectations. Whereas like at the, at the, you know, in early days, ChatGPT and even still today, like it’s even the simple friction of if you went into a regular chatbot and you tried to build a rubric, of course you could, but like, you’d have to type in like four sentences.

I want a rubric. It’s gotta be six columns. It’s gotta be, you know, like making sure a boxed format. And then like, you know, you do it and then it, then the LLM will respond something like, oh, did you, you forgot this detail? Can you give me this detail? Wait, wait, what? Like I just gave you, like, it’s a frustrating experience for somebody who was told this is gonna save them time. And I have to know funky things about prompting nonetheless, like, that’s delight. A rubric generator that gives you a rubric is like a zero time-to-value, incredible experience for the actual. I see how this is going to be really helpful to me in my daily work. And the flywheel it creates is also a secondary value because if generative AI can do this for me, then what else can it do for me? And we have a chatbot on the platform Raina that is just like a ChatGPT but built for education with a couple, you know, bells and whistles and education-focused context. And if you use that, like, if you know that, you know, MagicSchool could create a rubric for you.

No, we’re not hiding the ball here. Like, you know, what’s happening is there’s a prompt, there’s inputs, it’s coming out with something. You know, you can probably prompt Reina and do something completely original or new, but you need to be able to see the value first for you to be really sucked into it. And like, I think an amazing job of getting people in the door with something really low stakes that should like, turns out to be really high quality, saves you a bunch of time and makes you want to use the platform more. It makes you want to think about, okay, what are the other tools? If it did this thing for me, what else can it do for me? And I think that’s the way that we see the flywheel start. We think about the user.

They start with these really simple tools. They might graduate into their own free form prompts and Raina and trying things. And then they might decide to use an agentic workflow like our class writing feedback tool, connect their Google Drive and have writing feedback dropped on each of their documents. Then they might want to think about, well, if I’m using this in my work and it’s really valuable, it’s helping be a thought partner to me. I wonder if it would be helpful for my students typically. So then we —

Diane Tavenner: Yeah, super helpful to understand your thinking and your flow, and it seems like that is working. Let’s go back and nerd out a little bit on the rubric generator. So where…? What happens when they type that simple thing in? Like, how are you using AI literally? What’s going on behind the scenes there? And as a person who’s written a lot of rubrics in my life, used a lot of rubrics in my life, like, how do I know that’s a good rubric and, like, what’s going on behind the scenes and under the hood?

Adeel Khan: Yeah. So great question. When MagicSchool started, it was just a prompt. It was like me with a prompt in the background. I had created rubrics as an English teacher in my life, and the best instructions I could give, and the judge was me. Like I was whether or not the quality was high enough or, you know, would meet my standards. And then it was a group of users who were doing the same things upon using it.

Diane Tavenner: And you’re prompting one of the big LLMs, essentially.

Improving model quality and selection

Adeel Khan: That’s two and a half years ago. So just know that’s like, it’s radically, radically different. So today we have a trust, safety and quality team, running our own evaluations on the platform. We have pedagogical experts on the team who are reviewing poor quality. So there’s a human part of it. Then there’s a thing called LLMs as a judge. Some of our evaluations are using models like Claude to judge the quality of the rubric. I was actually on a panel yesterday with one of the, a teammate, one of the teammates at Anthropic, his name is Nirob, and he was, he was naming that, you know, Claude itself at this point is probably the level of like a PhD in domain.

So, you know, you can have Claude, a PhD in a domain, almost, nearly, and getting better every day, judge the quality of the rubric too, and then iterate on the prompt model we’re using and change those things out. So we actually have, we have a wildly more complex version of judging for quality internally now. And it’s a combination of like LLM as a judge, human in the loop, user feedback on the platform. And then when new models come out, we are, on a regular basis, once every week or two, we’re saying, is this new model better performing against our criteria? And so each one of those essentially roll up to a score, and we have a score that determines whether or not this model can exist in the platform. And sometimes, you know, if it’s a 94 from one model and a 95 from one model, but one model is like one tenth the cost, we will choose the one that’s a little bit cheaper because we have to keep our platform affordable for schools. The vast majority of them, we’re able to say the absolute best output is the one that we go with, but they’re judged across a lot of different quality markers now to ensure that the output is something that we can stand behind.

Diane Tavenner: So just so I make sure I’m understanding. When I go in there and I pick a teacher tool, like, I think it’s report card feedback or something like that, you all have kind of established what good report card — the elements of good report card feedback. And then you’ve created prompts that are prompting the LLMs to do that. And then you’ve gone through and tested those responses to make sure that their quality. Because literally the humans aren’t testing while I’m the teacher here asking for that. You know, it comes back like that. Right? So.

So you just have confidence in those responses I’m getting?

Adeel Khan: Yeah. So, yeah, we’re essentially running out, like, hundreds of queries against them and then judging queries and then orienting around what the best kind of set of all of those variables. What’s the best prompt? What’s the best model? What’s the best output?

Diane Tavenner: Okay, got it. Now help me understand, like, that rubric. So now, like, I’m a teacher in my classroom, it’s often been standard that the teacher, sort of in their classroom, doing their thing, using their own rubric. How do you think about it from, like, your principal seat, like, the whole school? So, I mean, you know, for example, when I was leading schools, we built a longitudinal rubric that, you know, over time, the kids were — and so one of the things, as I play with MagicSchool, I’m like, oh, what’s the bigger picture here? You know, what’s the — what’s the high school arc? Or what’s the whole arc? And is this kind of more of an activity in my classroom base? Or is it the full big picture, the backward planned approach? How do you think about those things?

Adeel Khan: Yeah, it’s a really great question right now. I think that MagicSchool’s teacher side is best judged as kind of an assistant that helps you in the moment that you need it in your daily classroom activities. You can use Reina as a thought partner when you’re struggling with something or a Chatbot, you could, you know, really build out, like, a full, like, unit, then the subsets of lessons and like a generative thread, all the tools are threaded. So, for example, if you started with the unit plane generator, you could then say, build me the first lesson in this unit. It keeps context of that entire thread, and you could give it, like, input about, hey, my students performed this way on it. Can you generate the next lesson with that context? And you could do it that way.

But transparently, few people do. Right? Like, it’s really like, they come in, they kind of get what they need, and they keep going, we want to move toward more of what you’re describing, which is like, hey, the entire cycle is brought through in the platform. You give the platform your intent, and you keep these really rich threads with memory, context and knowledge of your classroom. So, we’re moving toward where you can kind of have the entire context of the classroom experience built in the platform. One of the key components of that is assessment.

Building personalized learning assessments

Adeel Khan: Like if the platform understands how your students did on their assessment, say for example today you could build an assignment in MagicSchool and you could, you could actually, you could build an assessment in MagicSchool. It could be taken by your students in MagicSchool. The results of that assessment could produce some really interesting insights about how they performed against the standard. And you could then create a material based on that assessment. So that’s where we think it’s going, is that hey, you’re going to build things based on the assessment results that take into context your students areas of strength and areas of growth based on the insights that are in aggregate across those folks who took the assessment. And you can imagine that there’s even more you can do, right? Like there’s on the student side, on an individual level, if you had assessment data about a child and you also had them taking assessments in the platform, you had them doing activities in the platform, you could trace a student’s personalized learning profile that understood kind of their academic needs. It could be continually updated with memory around the way that they interact with a platform. Not just from their academic strengths and how they’re growing, but also their stylistic preferences, their post secondary desires.

And you could really make these incredible persistent learning experiences for students that they always can tap into that’s associated with a really rich profile to serve them right in their zoning proximal development and in a style and in a way that they will engage and learn more.

Michael Horn: I’m curious off that just because that, that like becomes a very big vision, right, that shapes around the student how starting from like a teacher workflow, right to like the student life cycle almost, if you will, how much do you need to know and collect about individual students and how much do you need to know about the specific? Like take the assessment question, right, like, so I’m going to give feedback on you’re grappling with a particular poem or a book or something like that. How much does the model then need to get trained not just on the teacher rubric, but actually on the content itself as opposed to just the standard, which is probably a higher-level statement of ability to do X. But ability to do X in one context might be very different from another. So like, just help us think through the scope of that, of getting to that vision you just painted.

Adeel Khan: Yeah, it’s a really good question. I mean, we are at the early stages of understanding how this will work without student data. But as we build so one of the things that we are doing or in the planning process of getting some of these ideas off the ground, there is a really interesting process where you can kind of use LLM agents as sample students and then you can also assess the quality of their interactions with the platform based on specific profiles that you create for the AI agents. And then you can kind of get a really good data set on what you’re describing, Michael, is like, is it working? Is it actually meeting your needs? Or is that student who’s an agent who has the stylistic preference around visuals for their needs. That’s the way that they learn best. Or audio is the way they learn best. Their reading level or their reading test score diagnostic was this.

And this is what that means. You can kind of create all these profiles individually and then you can run them against the interactions they have with the platform and then you can judge how high quality the interaction is on the platform with like an external observer. So, like, almost similar to where I described that LLM as a judge, the judge can actually judge the experience.

So you can think of it as almost like a principal judge. Right? There’s a principal who’s an agent who’s watching the AI interacting with the student and determining is that a high quality interaction or not based on the child’s profile, based on what I know about what high quality instruction looks like. So you can start building these kinds of recursive loops and running them hundreds of times and get to some pretty, pretty impressive verifiable outcomes pretty quickly. And of course, you know, AI is not kids. And they’re not going to be perfect.

Michael Horn: AI is not going to act out for —

Adeel Khan: Yeah, yeah, yeah. So I don’t want to, you know, kind of anthropomorphize that this actually is the answer, that we can simulate everything through generative AI. But it’s a great way to like in a, in a basic sense feel like, okay, it works in a simulation. And then when we bring it to students and we work through the pilots and we see schools and districts who embrace this, then we can see it in action. We can combine those insights with the insights we got from our simulated experiences and make something that we think is really powerful. Some of those generative experiences, we’re going to be starting this summer in summer school with some partner districts to see how it works in practice and is it actually the needs of the kids. And we’re going to use that data as data to inform the product as it gets into more live cadence.

Diane Tavenner: You just said so many interesting things in there. Like, I am of like five minds right now. Where what thread do I want to pull? But let me pull on the one, because you started by saying, like, we’re playing with what we can do without student data in there. And best I can tell, based on my experience in MagicSchool you’re not connected to an SIS or anything like that. So you’re not pulling in any data as the teacher around your students. That said, I did create, I did use the IEP generator and I generated an IEP and I, you know, I, look, I didn’t do it on a real student, obviously. I’ve got like 25 years of experience. So I sort of built a proposed persona in my head and like input the information.

Discussing AI-generated IEPs and privacy

Diane Tavenner: And I will say I was like, kind of blown away that a fully formed, detailed, and dare I say, very confident IEP like, came out. You know, and at first I was like, oh my gosh. And it looked like an IEP that you would kind of read in a school sort of thing as I was skimming it. Then I started like really digging it, and I was like, oh, as someone with 25 years of experience, like, this is not the IEP I would have written necessarily because I, in my mind, you know, the kids I’m thinking about, like, I have intimate details and I’m like, wait, that feels a little, it felt a little AI-ish, right? Like sometimes AI gives you really, like, seems like compelling results, but they’re not very specific or personalized or whatnot. And so that was one thing I was curious about. Like, how do you think about some of the tools that are like that and, and how they get used? And like, I was thinking I’m a first-year teacher, and I use that, and I don’t have 25 years of experience. Like, can I? How do I do that? And then the second piece is you’re out with teachers, like, do they just pour a lot of information about kids into the platform? And I’m sure you get a lot of questions about privacy and security and, like, what’s going on there?

So I’m curious about those two angles.

Adeel Khan: Yeah. So I mean, we do a lot of professional development. I’ll start with the second one. We do a lot of professional development around making sure that teachers do not share information that could be sensitive into the platform. So there’s upfront training in that tool you used, actually, you’d see that like it actually actively reminds you in the tool itself to not, because that’s a tool that’s more susceptible for you to maybe submit accidentally that data. Of course, if any data is brought to our attention that was submitted to the platform, those PII, we remove it immediately.

And we have incredible data handling practices, and safe things are all available on our website. So we’ve done a lot of things to make sure that schools and districts can trust us. And so that’s certainly something that we consider. And we just think training and enabling teachers is the best way to prevent that stuff. Because even if MagicSchool has really great data handling practices and is kept safe, like they might bring it to another platform and you know, we want them to know how to use our AI, but also any generative AI and keeping student data safe is incredibly important when you’re using these tools.

Diane Tavenner: Yeah.

Adeel Khan: Second part, Diane, I think is really interesting. So I was a special education teacher too. So for me, I can think back to when I was a novice teacher, and MagicSchool would have been a godsend for me. The IEP would have prevented me from being anxious. I probably would have helped me save a lot of late nights, and it would have made me feel like I had real strategies to support kids because, you know, it’s a good point. So, so your question, I think I might challenge this. Like you presented it almost as like a fear that they’re not 25 years, so maybe they’re just taking this robotic IEP that’s not as good as the high-quality, 25-year IEP. I want you to know that like I’ve been a principal reviewing IEPs and I have seen teacher wonderful, hard-working teachers Submit IDPs with the wrong names in them because they were just copying goals and pasting goals from student to another because they were just trying to get it in by the deadline.

Right? Wonderful, hard-working, incredible teachers. So do I believe the world has gotten better because of MagicSchool’s IEP generator? Yes, it has. The floor has been raised because there are actual, because now the barrier is you understanding the student and getting some, some high-quality things and you know, even a novice teacher will see the goals and at least they have, they know how to write goals now.

I didn’t know how to write right, like, and I didn’t necessarily have someone to go to to help me write those goals. So I would say net MagicSchools raised the board dramatically for the way an IEP is written. I think a family would be much happier to see a MagicSchool-written IEP than the ones that I was editing, if I’m being totally honest. And it’s again, not because teachers aren’t wildly hardworking and talented. It’s because there is no time. And so I think that’s kind of the reality now. In an ideal circumstance, you know, they have a really great draft and they have an instructional coach like you, who they can go to and say, what do you think about these things? And like, you know, they can question and challenge them and push them and make them even higher quality.

But I do think that, like, sometimes we miss the reality of what happens in schools when we were critical of the tools that teachers are using to better their practice. And sometimes we just need to trust teachers. Like, in that case, I’m like, actually, I even trust the first- and second-year teacher to use this appropriately. And especially if they’re educated and they’re told, like, the way to use it, not to submit appropriately private information, things like that. But I always challenge people is like, yes, you know, people, we have schools and districts that hide the IEP generator because they’re so scared of it. And you know, we respect anyone’s decision on what they want to do and they’re allowed to do that and like, you know, our enterprise product and then we support them in doing that. But I, on a personal level, I always challenge them. I say, like, look, like, ask your assistant principals who are reviewing IEPs.

Yeah, they see, are they, are they higher quality because you took this tool away or are they higher quality when MagicSchool is in the loop? I think that’s like the question to ask is what’s the before and the after rather than like, what’s the ideal? Right. You want the ideal. But I think that there is like a, this go between. I think it is super strong. And I think that the tools are getting even better over time. And I think that the better the context that you share with it, the better it’ll do of course.

Diane Tavenner: Yeah, that makes sense. And I think I understand the perspective that you’re coming from and certainly your lived experience. And I do think sometimes people, you know, have an idea of what is happening in school versus a reality of what is happening in school. And so I appreciate that. I was also really tuned into you saying that as a school leader, you all developed your own curriculum. That was my reality too. You know, we ended up building curriculum, we ended up building Summit Learning, which was like a whole massive curriculum. And so I’m wondering how.

But a lot of schools have adopted curriculum, as you know, how do you recommend that teachers sort of deal with the world of like, I have an HQIM that I’m given by my school and I want to use MagicSchool. How do those two things play together? Or do they, or what does that look like?

Adeel Khan: Yeah, I think that, you know, again, lived reality of an HQIM in a district. I’ve lived in that reality as well. We didn’t box any curriculum, but sometimes we would get for a certain subject we would have like, you know, free prep lesson plans or whatever it might be. And I think the lived reality of those things are teachers are always modifying, changing, supplementing, making those things work for their classes. And that’s good. So I think that that’s what we’ve encouraged them to do is like, yeah, keep the spirit of what your school wants you to do. And obviously there’s a research base behind the curriculum that you’re using, hopefully, and we want to be a great supplement to that. We want to make sure that you’re able to build the supplementary materials that you need to make sure your class functions and works.

Integrating curriculum with MagicSchool

Adeel Khan: And MagicSchool could be used alongside those things. There’s a world where we build knowledge into our platform so the schools and districts can upload things like standards, curriculum they’ve built internally that are not like, you know, copyright by the publisher. And there’s a world where we partner with publishers too, and we bring those knowledge bases into our platform and call them as well. And as you’d imagine, those publishers aren’t super excited to work with large language models because this is like their proprietary IP. And nonetheless, I think that like, you know, we think that there’s a future where there’s kind of a win-win for both of us in a world where teachers are finding that they’re starting their day with MagicSchool, and it’d be really convenient if they can pull in some of that information and make those curricula more flexible with generative language models. But yeah, I support that. I’m also, I think a lot about curriculum and I make spicy posts on curriculum on my LinkedIn, if you haven’t seen them. There are a couple curriculums that are incredible and obviously they’ve driven meaningful outcomes for kids.

And I don’t think they’re all that way. To be clear. I don’t think they’re all super research based. I think that a lot of them are not particularly valuable and I think, I don’t think. I know a lot of teachers hate being put in a box. They hate having to be told that you can’t be autonomous in your classroom. You must follow the script because the script is better than anything you would create.

Which is like the, not the intended message, but the unintended message that a teacher might receive when receiving certain curriculum. And of course they’re the teacher’s love. Right? Like they tell you, no, this thing’s amazing. It’s changed my classroom. So not painting with a broad brush. There’s also like really amazing ones. I will say that my lived experience has shared that like if you trust teachers to go find the right resources and give them the tools to do that and know their kids and coach them really well, you can get really extraordinary outcomes. Mind you, most of my experience is secondary, but we have incredible results for our kids.

We served a highness population and had dramatic growth. And so that’s my own experience. So I don’t know. I think that there’s a little bit of like, I would not call myself as a personal. Like on a personal level. I do not ascribe to the Church of Box curriculum. That is not my ministry as we used to call it in Atlanta.

So do I think there’s really good stuff out there? Absolutely. It’s not my ministry. I just teach it.

Diane Tavenner: That makes sense. Clearly you are outcomes driven. I know that based on the school that you started and all the language you’re using here. How do you think about — how do you now at MagicSchool think about what outcomes your — how do you hold — what’s your bar? What are your goals? Like what are your outcomes that you want MagicSchool to drive to and how do you measure them?

Adeel Khan: So there are a couple ways that we’re thinking about this. So outcomes are at the heart of our mission. We have named goals in our company about how we are going to drive student outcomes in classrooms. Right now, the way that looks in our platform is the amount of what we would call feedback delivered to students. So generally what we’ve seen in the first two years of the product is that like the things that we, that teachers have said have driven growth, the experiments we’ve set up or have not set up we’ve just been reported are that when students get things like feedback on their writing that they’ve done on their own aligned with rubric in the platform, that is powerful. That is something that drives an alchemy classroom.

Measuring feedback and student outcomes

Adeel Khan: We have actual classrooms that have shown state exam scores change over those things. We have enough data to say that like when MagicSchool gives student feedback that teachers is controlling and aligning to a rubric or the way that they’ll assess students, that’s a great thing. So we measure right now how many instances of feedback is MagicSchool giving to a child under the supervision of their teacher through either our assessment platform in the product which gives students just in time feedback after they take an assessment, or in their feedback portion of the platform where they tune in. So those to us are pretty hard. Like we don’t kind of look at it as like hey, you talk to a chatbot, so you learned. Like, that’s not enough for us to kind of count and like our something that we think is definitely going to drive an outcome. It certainly might drive AI literacy and we certainly think about that measure as well. But in terms of outcomes, that’s the way we think about it now.

Well, where we want to go is we want to be able to probably say that students have learned and judge it in the platform itself. So one of the ways we might do that is through having a diagnostic assessment students taking the beginning of the year and then at the end of the year or tracking timing platform as we have more persistent student profiles in the platform and simply asking the district to themselves compare their users data which students spent the most time in platform and how much did they grow and then just give us a report back. There’s ways that we can kind of say it’s not a perfect correlation, but if students are spending more time in the platform and they’re getting better results at the end of the year, they’re growing more than their peers. That’s usually a pretty good indicator to us. There’s an experiment that was run unbeknownst to us in our first year. I actually shared this on a panel yesterday. I was at South by Southwest.

But I think it’s a powerful example and I think what a lot of organizations are doing around generative AI around the world and I actually think this is fun because a school district was ahead of enterprises, they’re ahead of companies in the way that they’re thinking about generative AI and our first year that we had a real enterprise product was 2024 to 2025 or 2023. Our first partner, which you imagine our very first partner at MagicSchool would be pretty innovative. And they certainly were as Aurora Public Schools, one of our very first ones. At the end of their first year using MagicSchool, they got a printout of their MGP scores at the district level. MGP in Colorado is basically like a growth score that is associated with each teacher in the district based on their state exam scores. Their grade has a high stakes exam. So the district quite literally gets a list of all the teachers who had the highest growth in order of like this calculated growth score. So they kind of have like which teachers are having the best results in their classroom.

It’s pretty sophisticated calculation. It tracks like based what was their expected growth based on their prior year’s performance to this year’s performance. It’s a pretty solid number. Like at the end of the day it’s like the kids actually grew and, and it’s based on some pretty hard metrics. What they did was that they liked the way the story is told, is the academic, one of the assistant superintendents called the technology director and said, I have all the teachers who have the highest scores or the highest MGP scores in the district. Can you pull up MagicSchools our user dashboard which shows which teachers are using it the most? And they said like one for one. It was like best growth scores were in the top 20 users, best growth scores, best users.

Using AI to boost engineering productivity

Adeel Khan: So the way industry is doing this now, what we’re doing at MagicSchool is we, we have an enterprise version of Claude that our employees use and where we have a real big focus on like agent decoding for our engineers to move faster and ship with more velocity, build a lot of really amazing things for our users. And one way we’re measuring the efficacy of generative AI right now is we’re looking at our leaderboard, like, which engineers are using the most tokens in our version of that enterprise dashboard? And then we’re asking the managers, would you call that your highest performer? Like, is that engineer who also is using the most tokens in Claude performing better? Are they shipping more? Are they meeting the goals that you’ve set for your team in a better way because they’re using AI more? And if the answer is no, then we need to rethink about is generative AI actually helpful? But if the answer is yes, then we need to spotlight that engineer and we need to have that engineer teach the other engineers how they’re using it and how it’s making them more productive. And Aurora was doing the exact same thing two years ago. So I think it’s a really, really cool way to think about how this is actually amplifying productivity in a meaningful way.

Michael Horn: Adeel, I think that’s a good place to leave the conversation for now. I appreciate how much you’ve geeked out with us, and also I appreciate that you’ve told us where it is now and where your sort of vision for it is. As you know, a lot of entrepreneurs, they sell the future/present as one package as opposed to distinguishing the two. So I appreciate that in this conversation as well. Before we move to our last segment, as listeners know


And with that we’ll move to our last conversation, which is it’s just a fun segment we’ve had and people track this and so forth, Adeel on LinkedIn and believe it or not, about things that we’ve been watching, listening or viewing and would love to hear something that you’ve been tuning into what’s either on your bedside table or on your playlist.

Adeel Khan: I think I’ll go with a Netflix show. So it took me quite a long time to get to it, but I finally watched just finished the last season of Stranger Things, which I was late to the party yard in the first place but kind of binged it a few years ago. And so I was eagerly anticipating the final season. None of it got spoiled for me and I got to watch the whole thing and it was delightful. So I felt like I wrapped the bow on a really special I feel Stranger Things is so awesome if you guys have seen it, but I feel like it’s such a special cultural phenomenon that everyone kind of experienced together and watched together. So that was mine. Speaking of a new show. So excited to hear from you guys.

Diane Tavenner: Well, I’m not going to give you a show today. I apologize. I’m gonna give you a book. It’s a little bit ironic to have AI books, I think, but this one feels special to me. So it’s called Co-Intelligence Living and Working with AI by Ethan Mollick. And I will say, when it arrived, my kiddo who works on the models said, oh, that’s perfect for people like you. I was like, what do you mean? He’s like, you know, for, for regular people who don’t understand what we’re actually doing, but, like, have some sense of it. And it’s really useful for kind of who are really trying to make sense of AI and world and what that looks like.

And that’s what it feels like to me. And so it’s, you know, I’m not telling you anything new by sharing this book with you, you know, a bestseller. But it is, it’s short and it’s thoughtful and I think useful for anyone who’s really trying to grapple in this space. I would recommend it.

Michael Horn: The one question Diane, I have, I love Ethan’s Substack as well. And when the book came out, I bought it as well and read it. But I’m just curious, like, does it still feel current given, like, the race?

Diane Tavenner: You know… yes?

Michael Horn: Interesting.

Diane Tavenner: Yeah, I think so.

Michael Horn: OK.

Diane Tavenner: I think so.

Michael Horn: Cool.

All right. I’ve got a book as well. So, Adeel, I’m also striking out on the show, watching, I think, at the moment, but I still haven’t done Stranger Things. Diane, I think this was on her list. I can’t remember how many episodes ago

Diane Tavenner: I started early, but I haven’t finished off the season. So you’ve been —

Michael Horn: I was about to say. You just inspired her to finish it. Yeah. The book I’ve been reading is A Heart of a Stranger by Angela Buchdahl. She’s a rabbi at the Central Synagogue in New York City, which I think is the largest synagogue in the U.S. or, or top two, I guess. And it’s terrific, she’s a Korean American rabbi. And so it’s like a very interesting story about where she, the circle she has not belonged in, and then making sort of this, I guess, momentous movement into being a rabbi and sort of what that’s been like and her life story through it.

So it’s been a very good read. As Diane knows, my wife’s Korean American, so, like, and I’m Jewish, so it’s like hitting on multiple levels in our household. TBD if anyone else in the household reads it beside me. But I’m enjoying it quite a bit. And we’ll leave it there. Adeel, huge thanks for coming on, joining us, geeking out with us, and for all of you, keep the questions, comments, notes coming after this episode and in general, and we’ll see you all next time on Class Disrupted.

This episode is sponsored by LearnerStudio.

The middle schoolers at Philly’s Marian Anderson Neighborhood Academy have a lot of questions about artificial intelligence.

They want to know how the government is using AI and what impact the technology has on the environment. They’re curious about how it’s being used for creativity, and whether it will be with us forever — or if it’s an economic bubble waiting to burst.

The sixth through eighth graders have been researching these topics and grappling with how it makes them feel about themselves, their education, and the world around them. On Friday, they presented their findings to their parents, teachers, and some state and local officials in their school cafeteria. Overall, they said there’s a lot they still don’t know.

Sixth grader Azizah Simmons said she’s weighed the pros and cons and she’s pretty confident that AI’s overall effect on our society is negative. If used correctly, Simmons said language models like ChatGPT could help kids her age improve their writing. More often than not, she said students use it to cheat on homework or cut corners on writing assignments.

But it’s the ubiquity of the technology that worries her most.

“You can’t really escape AI,” Simmons said.

Conversations about AI have permeated every aspect of education since the arrival of models like ChatGPT. Familiar debates about cheating have given way to Marketing pitches from companies promising “transformative” AI tools are now . In Philly, educators are working with students to build their own curriculum to and that can be embedded deep in the internal code.

And students say they feel like they have as much knowledge — or sometimes more — than the adults in their lives.

Sixth graders Thomas Mapp and Tyshaan Anderson’s research project focused on how video game designers use AI for level design, character creation, and visuals. Outside of school, they’ve been using AI to help them code games in Roblox and edit videos.

Anderson said he thinks the technology has helped kids like him experiment with creative fields like game design without needing to know the ins and outs of specific coding languages.

Marian Anderson Principal Nicole Patterson said she’s been inspired by her students’ civic inquiries and has learned a lot from them.

Patterson said she sees her school as a trailblazer in leading challenging conversations about AI. But she cautioned that “this is unfinished work.” She said students will continue their research and keep talking about these issues.

Marian Anderson computer science and technology teacher Trey Smith said the goal of Friday’s event was to help students and parents discuss how AI is now part of society, culture, politics, and everyday life, not just about how AI works.

“We’re all still trying to figure this out together,” Smith said. “For students to be in dialogue, not just with themselves and each other and me, but also with their families and with legislators and with school district officials and professors — I think it’s so important for them to learn together.”

That learning process can be tricky. Simmons said she ends up using AI involuntarily because search engines like Google now frontload AI overviews. That makes it difficult for young users to differentiate between what is a primary source link and what is AI generated.

“You use it without meaning to. It’s everywhere implanted in our lives,” Simmons said.

Chalkbeat is a nonprofit news site covering educational change in public schools. This story was originally published by Chalkbeat. Sign up for their newsletters at .

Yet just freely letting students use AI to generate ideas, explain difficult concepts and produce/revise writing ” upon which learning depends and . 

So I have been attempting the “third option” recommended by both the and the : teaching AI literacy.

This year my 10^th and 11^th grade English students used AI itself as a text to advance their critical thinking skills. We still read novels and short stories, still engaged in discussions and wrote essays, but AI was now a regular part of our work together.

As we read, we examined how large language models’ recycled novel “analyses” mis-read and oversimplified complex literature, producing distillations that often lacked nuance compared with the creative, discursive yet defensible readings that the students themselves generated. They learned to discern actual analysis from simplistic summaries, and to suspect the allure of AI’s instant “correct answers.”

As we engaged in literary discussions, we sometimes invited chatbots into the conversation; many students described these interactions as “bizarre” and “disjointed,” adequate for reviewing plot but too circular or directionless for genuinely provocative dialogue. ChatGPT’s sycophancy in particular tended to kill the necessary tension for true debate. One student “started purposely saying dumb things just to see how GPT would still find a way to say `great idea.’ It just felt so fake.”

As we wrote, we compared LLM-generated essays with human-generated ones, teasing out how AI’s “sophisticated-sounding” yet “generic” prose differed from the “messier” but ultimately, in the students’ judgment, more engaging language they themselves created. In a world where everyone has access to LLMs, these students were discovering the value of developing genuine voice. I hope at least some emerged thinking ChatGPT was best reserved for inter-office memos and letters to one’s utility company.

As we researched, we studied how AI search summaries — which users are now — don’t actually represent internet searches, but instead reflect word proximity within a static corpus of text, a corpus lacking access to paywalled scholarly research and therefore drawing disproportionately on unregulated chat forums. 

Students examined whether LLMs accurately reported their sources and to what extent AI drew from ideologically extreme sites. They saw how wording a query — e.g., “is abortion safe” vs. “is abortion murder” — could lead to politically-slanted results based on what the AI thought they wanted to see, and how sources often said something very different than AI summaries claimed they did. 

As we took and organized notes, students compared their manual note-taking process to the output of AI note-taking tools, learning how what we choose to include or exclude in summarizing notes, how we use emphasis and phrasing — did Africa under colonialism “fuel worldwide industrial production” or were African resources and peoples “exploited for the benefit of Western industrial profit” — create and propagate different narratives.

These narratives do not ; “what is ranked at the top” of AI searches “is ultimately influenced by the priorities of LLMs’ shareholders,” so we studied studying the politics of AI magnates like Sam Altman and Peter Thiel, learning how Gemini’s responses to political questions, and studying algorithmic bias (e.g., image generation requests for “doctor” returning mainly white males), all helped my students re-think their understanding that AI tools were neutral and simply utilitarian.

When we studied AI, we simultaneously studied neurological research about how humans, unlike LLMs, don’t just rely on pattern recognition, but also make intuitive leaps, and used Edward De Bono’s activities as practice. Students did something else that AI couldn’t: related classroom content to personal experiences. 

One multilingual student recalled attending a business meeting with her father where he faltered, because he “[knew] that someone who has the ability to speak English better [me] sat right next to him… ‘It makes me want to depend on you’ he told me, ‘when I’m totally capable of doing so by myself.’ He did much better after I left.” The student then made the leap to consider how, even if AI help is readily available, perhaps we gain something by refusing to rely on it.

When I abandoned AI bans, I instituted AI audits. Students had to demonstrate their thoughtful, detailed evaluation of each AI tool they used, including knowledge of how it operated, what they felt they gained and lost by using it, how they verified accuracy of information, and how they had not relinquished their own thinking. The students didn’t necessarily conclude “AI is always bad,” but they did see that using it always requires vigilance. Best of all, they didn’t have to take my moralizing word for any of this; they discovered it for themselves. 

Yes, I had to teach fewer novels in order to make room for AI literacy, but ultimately my job is not to teach novels; it’s to teach students. Their insights — how Grammarly’s “correcting” language altered integral parts of people’s unique voices, how personal evolution often comes from struggle and discomfort, how our desire for ease can hold us back from achieving our potential, how dangerous it is to invest authority in words just because they emerge from a machine — were equally valuable as any takeaway they gleaned from novels. And this time I knew those takeaways were theirs, not ChatGPT’s.

I teach an affluent population, but are with more economically and linguistically diverse learners. To be sure, my experience was often fraught. Some of my less-confident students never stopped considering LLMs’ “clear” and “well organized” writing superior to their own, and still hesitated to trust their own readings of literature over “the answers” ChatGPT offered. 

I struggle with asking students to critically evaluate AI while their own linguistic and analytic skills are still developing, but I also know I cannot create the conditions that allow teenagers to become master writers and thinkers before they are exposed to AI; they will soon arrive at my classroom having been using it since childhood. 

Post-pandemic suggests that, when teaching anything, we cannot wait for students operating well-below grade level to “catch up” before introducing higher order thinking skills; we have to figure out how to teach both simultaneously.   

That requires creativity, and creativity is what makes humans superior to AI, which can only regurgitate already-created ideas. Teachers excel at creativity; every day we come up with new ways to meet the ever-changing needs of our students, and right now AI literacy is one of those needs. 

that this training is crucial for keeping AI users — a population swiftly becoming synonymous with “humans beings” — from engaging in “cognitive surrender, marked by passive trust and uncritical evaluation of external information,” as opposed to “cognitive offloading, which involves strategic delegation of cognition during deliberation” when using AI.

about AI rendering English classes obsolete forget that the humanities are about studying what is human about us — including both our criticality and our adaptability.

Note: This is an abridged, non-scholarly version of a peer-reviewed article slated for publication in Issue 115.6 of NCTE’s .

AI assistants like Claude, ChatGPT and Gemini won’t change these realities. But when you’re overwhelmed, they can help you streamline some of the most tedious aspects of your work. They can free up your energy for what only a human teacher can do. And AI assistants can actually push us to be more creative. They can help us overcome teaching ruts, nudging us to revitalize aspects of our teaching that are growing stale.  

AI assistants have arrived at a time when teachers need support to do their best work. In a national by the RAND Corporation, just 24% of teachers reported being satisfied with their total weekly hours worked, and 66% said their base salary was inadequate.

AI tools won’t make up for unfair compensation. But they can help us save time and create a better work/life balance. They can also help us do better work. 

A 60-second guide to prompting

The first step to making the most of AI is understanding how to use prompts. A prompt is a natural language instruction to an AI assistant. It doesn’t have to include technical or formal language. You don’t even have to use full sentences. 

Prompts are tool agnostic, so you can use them with whichever AI assistant you have access to. I recommend the free or paid versions of Claude, Gemini and ChatGPT, but you can also use free AI tools that run privately on your own laptop, like or . 

As the teacher, you guide an AI assistant like Claude or Gemini with relevant context. For prompts to work well they have to be detailed, including specifics and context. Generic prompts yield generic responses. 

It helps to iterate on AI output with follow-up prompts. Ask for more specificity or detail. Adapt the response for your students, don’t use it as is. Part of retaining your agency in the process is making sure you build on whatever outputs an assistant generates. You’re the director. It’s much like you were adopting an . The advantage, though, is that this material will be more tailored to your students and teaching approach.

Knowing how to use prompts effectively can mean the difference between AI that’s actually helpful and AI that’s gimmicky. The prompts below are designed to provide you with a creative boost. They each illustrate a practical way to use AI in support of thoughtful, pedagogically-sound teaching. You don’t need any special technical skills or subscriptions. You can copy, paste, and customize them to suit your subject matter.

Setting up a project

If you want to use prompts more efficiently, create a Claude or ChatGPT Project, or a . That’s a folder where you provide a summary of context about your students that the AI assistant can reference whenever you ask for support. You can also upload past materials, syllabi, lesson plans, curriculum guidelines, Common Core State Standards, or whatever else would be helpful context for the AI assistant. 

You can also provide detailed instructions in the project for how you’d like the AI to assist you. You’re training the AI assistant and teaching it your preferences. Once you set up a project,  you won’t have to repeatedly type in the same context. I set up projects for each of the classes and workshops I teach.

The Prompt Collection

The Bell Ringer 

Start class with a spark

The first five minutes of class set the tone for everything that follows. A short, well-designed opening activity can draw students in. Engaging openers are especially valuable on Mondays, after vacations or when you’re pivoting to a new topic. The challenge is coming up with fresh ones regularly. 

Goal: Generate a bunch of quick activities you can use at the start of a class session, adapted for your subject and your students. 

Prompts: “I teach [subject x] to students in [grade level x]. We’re studying [specific topic xyz. Be as detailed as possible about your subject and context. Include a sentence or series of phrases of context about your particular class and teaching style, or any special needs or context for your students. No need to make it formal].”

“Generate five bell-ringer activities I can adapt to open a [xx] minute class. Each should take no more than [x] minutes, require no materials, and either activate prior knowledge or help students reflect on what they’ve just learned. Include one that’s discussion-based, one that’s written and one that’s a game or visual/creative task. [Or adapt these examples to reflect your subject matter. For example, one of the options could be a logic puzzle or an artistic challenge].”

Prompt Example

The Real-World Hook 

Answer “Why does this matter?” before students even ask

When you’re juggling administrative meetings, multiple preps and paperwork, it can be hard to give extra attention to helping students relate to a given learning unit. This prompt helps you brainstorm connections to contemporary music, art, film, TV, cultural trends or other subjects of interest to students. 

Goal: To generate five ways to show your students how the topic you’re teaching is relevant to their lives, each with a two-sentence hook you can use to open discussion.

Prompt: “I’m about to begin a unit on [x topic] with [x grade level] students. [Provide a sentence of additional context and a few additional details about your students’ interests]. Generate five ways to connect this material to something students at [x] grade level may likely be able to relate to. This can include sports, the arts, social media trends, pop culture, music or other contemporary issues. For each connection, suggest a two-sentence hook I can adapt to help jumpstart a class discussion.”

Prompt Example

The Bad Example Generator 

Turn common mistakes into teachable moments

Showing students examples of common mistakes can help them avoid those pitfalls. But we can’t embarrass students by showing examples of their weakest work. Fortunately, AI assistants are excellent example generators. They can come up with nearly any kind of error you specify, saving you hours you might otherwise have spent creating intentionally bad work.

You can adapt this prompt to include any kind of error you want your students to avoid. These can include experimental design mishaps in science or mangled math formulas. If you’re teaching essay writing, showcase logical fallacies or ad hominem arguments. 

Here’s an example of a I generated with the help of an AI assistant.

Goal: Produce five realistic examples of a specific error type, unlabeled, so students can identify, discuss and learn from the flaws. 

Prompt: “I’m teaching [x subject/topic] to [grade level x] students. [Provide an additional sentence of specific context about your class, the learning goals you’re focusing on, and/or the lesson you’re preparing.] Generate five examples of paragraphs with [ad hominem arguments / circular reasoning / weak thesis statements / misleading use of statistics / or pick any other weakness] related to [x topic]. Make sure each example is realistic and plausible. These should be the kinds of errors students at this grade level might actually make. Don’t label what’s wrong. I’ll use these for a class activity where students identify and explain the flaws themselves. [You can also task the AI with annotating or explaining these errors to help you walk students methodically through these common flaws.]”

Prompt Example

The Scaffolding Prompt 

Make instructions clear for every student

Complex instructions often trip up students. Simplifying language can help, along with breaking guidance into smaller steps. This prompt helps you clarify instructions for an existing assignment, handout or any other activity. It’s particularly useful if you have students with learning differences or if your class has a wide range of readiness levels.

Goal: Reframe an existing handout or assignment so it’s clearer and more accessible, especially for students who need extra support. 

Prompt: “Here is a [handout / assignment / resource] I give students: [paste or upload the handout]. Help me reframe this for students who face [specific challenges or context that impact some of your students]. I particularly want this to be more accessible for students who need extra support. Break the instructions into smaller, numbered steps. Replace any abstract language with concrete, specific directions. Point out any parts I should clarify. Suggest a brief example for each major step and any illustrations or images that might help me make this more visually engaging. Maintain the academic expectations I have for the work. The goal is clarity, not simplification.”

Prompt Example

The Review Game Generator 

Create engaging questions efficiently for learning games

Coming up with a long list of review questions can take hours and designing multiple plausible wrong answers for every question can be exhausting. An AI assistant can help, quickly turning existing handouts, lesson plans or fact sheets into engaging questions. It can help you customize questions for your subject matter and student level. 

Goal: Generative 15 multiple-choice review questions, tiered by difficulty, formatted for whatever learning game you prefer. 

Prompt: “I’m finishing a unit on [x topic] with my [grade level x] students. I’m preparing an end of term review session, so I’m trying to come up with some good questions to help students practice [a particular skill or area of knowledge].  Generate 15 trivia questions based on the following key concepts: [list concepts or paste notes or upload a handout]. Suggest a series of multiple-choice questions, each with a correct answer and three plausible wrong answers. Vary the difficulty—five easy, five medium, five challenging. Flag the correct answer for each. Also suggest some true/false, fill-in-the-blank, and open-ended questions for variety.”

Prompt Example

The Fresh Angle Search 

Bring new life to familiar content

Some topics get stale, especially when you’ve taught them the same way for years. To liven up an old lesson, it can be helpful to gather new sources, examples, statistics, case studies or unexpected angles. 

Use , a free, AI-powered search engine that provides citations alongside its results. The links it provides ensure you have an evidence trail you can use to verify its responses and to dive deeper. Digging into Perplexity’s concise search summary is more efficient than sorting through hundreds of blue Google links.

Goal: Find five recent or unexpected real-world examples of a concept you’re teaching,  including perspectives from outside the U.S. and connections to students’ current interests. 

Prompt: “I teach [x topic] to [grade level x] students. [Provide additional context here about the topic or learning outcomes you’re focused on]. I’m looking for interesting material [or whatever other description you prefer] to make this subject more engaging for students. [Include any additional context about your students’ interests]. Find me five recent, unexpected or counterintuitive real-world examples of [x concept] that might surprise or intrigue students. Include also several real-world details to help add nuance for students who think they already understand the concept. And suggest several new analogies I can use for students who don’t yet understand this concept. Include international examples, and at least one that has an element of humor.”

Prompt Example

The Skeptical Student Prompt 

Prepare for the hardest questions before class starts

You never know what odd questions might arise when you teach a new topic. AI assistants can help by generating all sorts of potential questions. That prep can help you avoid unpleasant surprises in class, so you’re ready for nearly anything students might toss at you.

Goal: Generate 10 challenging questions a skeptical student might ask me about this lesson. 

Prompt: “Here is a [lesson plan / reading / concept] I’m teaching: [paste or upload material, mentioning the grade level and any other relevant context]. Give me a list of potential student questions about the relevance of this new topic and about real-world applications. Include also a mix of other unusual or surprising questions curious students might ask. If these high school students doubt this material is relevant, what might they ask, and what aspects in particular might they question. Generate 10 challenging questions students might ask. Include questions that challenge the relevance of the topic, the reliability of my sources and the assumptions behind my explanations.”

Prompt Example

The Blind Spot Audit 

Find your own blind spots before students do

During a typical week, we don’t always have time to trade peer feedback on lesson plans or syllabi. But we can still benefit from getting input on our materials. AI assistants can critically evaluate your materials for clarity, accessibility, inclusivity or other blind spots. You always have the option of ignoring the observations. I find that many of the weaknesses the AI assistant points out are ones that benefit from a fix.   

Goal: Identify specific places in your lesson plan or syllabus where I might have an unconscious bias, where my instructions may be unclear, my examples may not reflect student diversity or my assessment criteria might be confusing. Or point out unnecessary jargon.

Prompt: “Here is my [lesson plan / syllabus / unit overview]: [paste or upload document]. Take the perspective of a critic with expertise in inclusive pedagogy and student-centered design. Identify parts of my plan that may not work for someone with physical differences such as a vision, hearing or mobility impairment. Also point out places where an unconscious bias might be influencing the way I’m presenting this topic. Point out places where examples or explanations I’ve included might not make sense to my diverse students. Show me places where my assessment criteria could be made more clear. Note any other sections of the material that might not be inclusive, accessible or relatable for students. Be direct. Include the location of each issue so I can explore potential fixes. I want specific critique, not general praise, and I want you to explain each observation in detail.”

Prompt Example

The Differentiation Prompt 

Adapt one assignment for three distinct student levels without tripling your prep time

In many classrooms, students arrive at varying levels of readiness. Creating three versions of the same material is one of those things that turns a 40-hour week into a 53-hour one. Tasking an AI assistant with suggesting adaptations of your material ensures that your newly differentiated materials will remain anchored in your own ideas and teaching goals. 

Goal: Produce two alternative versions of an existing assignment: one with additional scaffolding, and one with stretch challenges for advanced students.

Prompt: “Here is an [assignment / assessment] I give students: [paste or upload material]. Generate two versions of this: one for students who need additional scaffolding and more explicit guidance, and one that adds stretch challenges for advanced students. Preserve the core learning objectives. Summarize the suggested changes and explain their rationale, so I can decide how to adapt these alternatives for my students.”

Prompt Example

The Rubric Builder 

Help students understand how you’ll assess them.

A well-designed rubric does two things: it clarifies your expectations before students start working, and it gives them a roadmap for revising. Developing rubrics from scratch is tedious. It requires formatting small batches of text into boxes in complex tables. This prompt generates a structured first draft in table format. You can then refine it before sharing it with students. To start, specify the elements of the student work you’ll be evaluating, and describe your criteria. 

You don’t have to use full sentences or formal language. Just describe what constitutes excellence for this assignment, what satisfactory work looks like, and what evidence signals to you that a student may need more skill practice. Developing these rubrics with AI assistance is an iterative process. Revise initial outputs by adding your own details and refinements.

Goal: Generate a rubric with three performance levels and five criteria you’ve specified, written in specific, concrete language, without vague phrases like “good use of sources.”

Prompt: “I’m assigning [describe assignment] to [grade level x] students. [Provide any additional relevant context]. Generate a rubric with three performance levels: Excellent, Proficient and Developing. Include five criteria relevant to this assignment: [list criteria, e.g., argument clarity, use of evidence, originality, structure, mechanics]. For each criterion and each level, write two specific sentences describing what that performance actually looks like. Avoid vague language like ‘good use of sources.’ Be concrete. Put this rubric into a table, then await my input for potential edits”

Prompt Example

The Case Study Collaborator 

Generate fictional scenarios to spice up discussions

Case studies help spark lively discussions. They’re useful whether you’re introducing students to ethical questions or trying to help students relate to a historical situation. They can also be useful for bringing a business decision or a scientific discovery to life.  Creating cases from scratch can be exhausting. So this prompt helps you build fictional but realistic scenarios customized to your subject matter and student context. 

Goal: Create a fictional case study to illustrate a tension relevant to your subject, set in a context students can relate to, ending with three discussion questions.

Prompt: “I teach [x subject] to [grade level x] students. [Provide an additional sentence of context or specifics to ensure the case studies are relevant and useful.] We’re exploring [x concept or issue. Include as much detail as possible about what and how you’re approaching the topic and your learning goals]. Create a fictional but realistic case study involving [type of character, institution, or situation relevant to your subject] that illustrates the tension between [value A] and [value B]. Set it in [context relevant to your students—a school, a local community, a specific industry]. The scenario should be complex enough that reasonable people could disagree about the right response. End with three discussion questions that I can adapt to push students to apply the concepts we’ve been studying.”

Prompt Example

Disclosure: Two kinds of prompts appear in this piece. I developed the templates with brackets based on my teaching experience. The filled-in examples showing how teachers might customize each template were drafted with help from Claude, an AI assistant. Using AI to help generate these examples let me stress-test and customize each template across different subjects and grade levels and confirm that the prompts produce useful results. I reviewed and edited every example.