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Are U.S. public schools lagging behind other countries like Singapore and South Korea in preparing teachers and students for the boom of generative artificial intelligence? Or are our educators bumbling into AI half-blind, putting students鈥� learning at risk?

Or is it, perhaps, both?

Two new reports, coincidentally released on the same day last week, offer markedly different visions of the emerging field: One argues that schools need forward-thinking policies for equitable distribution of AI across urban, suburban and rural communities. The other suggests they need something more basic: a bracing primer on what AI is and isn鈥檛, what it鈥檚 good for and how it can all go horribly wrong.

A new report by the , a non-partisan think tank at Arizona State University, advises educators to take a more active role in how AI evolves, saying they must articulate to ed tech companies in a clear, united voice what they want AI to do for students. 

The report recommends that a single organization work with school districts to tell ed tech providers what AI tools they want, warning that if 18,000 school districts send 鈥渄iffuse signals鈥� about their needs, the result will be 鈥渃rap.鈥�

It also says educators must work more closely with researchers and ed tech companies in an age of quickly evolving AI technologies.

鈥淚f districts won’t share data with researchers 鈥� ed tech developers are saying they’re having trouble 鈥� then we have a big problem in figuring out what works,鈥� CRPE Director Robin Lake said in an interview.

The report urges everyone, from teachers to governors, to treat AI as a disruptive but possibly constructive force in classrooms. It warns of already-troubling inequities in how AI is employed in schools, with suburban school districts more than as their urban and rural counterparts to train teachers about AI.

The findings, which grew out of an April convening of more than 60 public and private officials, paint AI as a development akin to extreme weather and increasing political extremism, one that will almost certainly have wide-ranging effects on schools. It urges educators to explore how other school districts, states and even other nations are tackling their huge post-pandemic educational challenges with 鈥渘ovel鈥� AI solutions.

For instance, in Gwinnett County, Ga., educators started looking at AI-enabled learning as far back as 2017. They鈥檝e since created an AI Learning Framework that aligns with the district鈥檚 鈥減ortrait of a graduate,鈥� designed a three-course AI and career and technical education curriculum pathway with the state and launched a new school that integrates AI across disciplines. 

Lake pointed to models in states like Indiana, which is offering 鈥渋ncentives for experimentation,鈥� such as a recent invitation to develop . 鈥淚t allows a structure for districts to say, ‘Yes, here’s what I want to do.’ 鈥�

You can't eliminate all risk. But we can do a much better job of creating an environment where districts can experiment and hold student interests.

Robin Lake, Center on Reinventing Public Education

But she also said states need to put guardrails on the experimentation to avoid situations such as that of Los Angeles Unified School District, which in June took its heavily hyped, $6 million AI chatbot offline after the tech firm that built it lost its CEO and shed most of its employees. 

鈥淵ou can’t eliminate all risk 鈥� that’s just impossible,鈥� Lake said. 鈥淏ut we can do a much better job of creating an environment where districts can experiment and hold student interests.鈥�

AI 鈥榓utomates cognition鈥�

By contrast, the report by , a newly formed Austin, Texas-based think tank, starts with a startling assertion: Generative AI in education is not inevitable and may actually be a passing phase.

鈥�We shouldn’t assume that it will be ubiquitous,鈥� said the group鈥檚 founder, Benjamin Riley. 鈥淲e should question whether we want it to be ubiquitous.鈥�

The report warns of the inherent hazards of using AI for bedrock tasks like lesson planning and tutoring 鈥� and questions whether it even has a place in instruction at all, given its ability to hallucinate, mislead and basically outsource student thinking.

Riley is a longtime advocate for the role of cognitive science in K-12 education 鈥� he founded , which sought to raise awareness of learning science among teachers college deans. He said that what he and his colleagues have seen of AI in education makes them skeptical it鈥檚 going to be as groundbreaking and disruptive as the participants in CRPE鈥檚 convening believe. 

鈥淚 profoundly question the premise, which is that we actually know that this technology is improving learning outcomes or other important student outcomes at this point,鈥� he said in an interview. 鈥淚 don’t think [Lake] has the evidence for that. I don’t think anybody has any evidence for that, for no other reason than this technology is hardly old enough to be able to make that determination.鈥�

By its very nature, generative AI is a tool that 鈥渁utomates cognition鈥� for those who use it. 鈥淚t makes it so you don’t have to think as much. If you don’t have to think as much, you don’t have to learn as much.鈥�

I profoundly question the premise, which is that we actually know that this technology is improving learning outcomes.

Benjamin Riley, Cognitive Resonance

Riley recently in the ed tech world by suggesting that schools should slow down their adoption of generative AI. He took Khan Academy to task for promoting its AI-powered Khanmigo chatbot, which has been known to . It also engages students in what he terms 鈥渁n illusion of a conversation.鈥�

Technology like AI displays 鈥渏ust about the worst quality I can imagine鈥� for an educator, he said, invoking the cognitive scientist Gary Marcus, who has said generative AI is 鈥渇requently wrong, never in doubt.鈥�

Co-authored by Riley and University of Illinois education policy scholar Paul Bruno, the report urges educators to, in a sense, take a deep breath and more carefully consider the capabilities of LLMs specifically and AI more generally. Its four sections are set off by four question-and-answer headings that seek to put the technology in its place: 

• Do large-language models learn the way that humans do? No.
• Can large-language models reason? Not like humans.
• Does AI make the content we teach in schools obsolete? No.
• Will large-language models become smarter than humans? No one knows.

Actually, Riley said, AI may well be inevitable in schools, but not in the way most people believe.

鈥淲ill everybody use it for something?鈥� he said. 鈥淧robably. But I just don’t know that those 鈥榮omethings鈥� are going to be all that relevant to what matters at the core of education.鈥� Instead they could help with the more mundane tasks of scheduling, grades and the like.

Notably, Riley and Bruno confront what they say is a real danger in trusting AI for tasks like tutoring, lesson planning and the like. For instance, in lesson planning, large language models may not correctly predict what sequence of lessons might effectively build student knowledge. 

And given that a lot of the online instructional materials that developers likely train their models on are of poor quality, they might not produce lesson plans that are so great. 鈥淭he more complex the topic, the more risk there is that LLMs will produce plausible but factually incorrect materials,鈥� they say.

To head that possibility off, they say, educators should feed them examples of high-quality content to emulate.

When it comes to tutoring, educators should know, quite simply, that LLMs 鈥渄o not learn from their interactions with students,鈥� but from training data, the report notes. That means LLMs may not adapt to the specific needs of the students they鈥檙e tutoring.

The two reports come as Lake and Riley emerge as key figures in the AI-in-education debate. Already this summer they鈥檝e engaged in an open discussion about the best way to approach the topic, .

In a way, CRPE鈥檚 report can be seen as both a response to the hazards that Riley and Bruno point out 鈥� and a call to action for educators and policymakers who want to exert more control over how AI actually develops. Riley and Bruno offer short-term advice and guidance for those who want to dig into how generative AI actually works, while CRPE lays out a larger strategic vision.

A key takeaway from CRPE鈥檚 April convening, Lake said, was that the 60 or so experts gathered there didn鈥檛 represent all the views needed to make coherent policy. 鈥淭here was a really strong feeling that we need to broaden this conversation out into communities: to civil rights leaders, to parents, to students.鈥�

The lone student who attended, Irhum Shafkat, a senior, told the group that growing up in Bangladesh, his educational experiences were limited. But access to , which has since invested heavily in AI, helped bolster his skills and develop an interest in math. 鈥淚t changed my life,鈥� he told the group. 鈥淭he promise of technology is that we can make learning not a chance event,鈥� he told them. 鈥淲e could create a world where everybody can rise up as high as their skills should have been.鈥� 

Lake said Shafkat鈥檚 perspective was important. 鈥淚 think it really struck all of us how essential it is to let young people lead right now: Have them tell us what they need. Have them tell us what they’re learning, what they want.鈥�

The CRPE report urges everyone from teachers to philanthropists and governors to focus on emerging problem-solving tools that work well enough to be adopted widely. Those could include better translation and text-to-voice support for English learners, better feedback for students and summaries of research for educators, for instance. In other words, practical applications.

Or as one convening participant advised, 鈥淒on鈥檛 use it for sexy things.鈥�

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