With the rapid advancement of generative artificial intelligence, teachers and faculty leaders are searching for answers to complicated questions on successfully integrating technology into lessons, while also ensuring students actually learn what they’re attempting to teach.
Justin Reich, an associate professor in MIT’s Comparative Media Studies/Writing program, hopes a brand new guidebook published by the MIT Teaching Systems Lab can support K-12 educators as they determine what AI policies or guidelines to craft.
“Throughout my profession, I’ve tried to be a one who researches education and technology and translates findings for individuals who work in the sphere,” says Reich. “When tricky things come along I attempt to jump in and be helpful.”
“A Guide to AI in Schools: Perspectives for the Perplexed,” published this fall, was developed with the support of an authority advisory panel and other researchers. The project includes input from greater than 100 students and teachers from around the US, sharing their experiences teaching and learning with latest generative AI tools.
“We’re attempting to advocate for an ethos of humility as we examine AI in schools,” Reich says. “We’re sharing some examples from educators about how they’re using AI in interesting ways, a few of which could prove sturdy and a few of which could prove faulty. And we won’t know which is which for a very long time.”
Finding answers to AI and education questions
The guidebook attempts to assist K-12 educators, students, school leaders, policymakers, and others collect and share information, experiences, and resources. AI’s arrival has left schools scrambling to reply to multiple challenges, like tips on how to ensure academic integrity and maintain data privacy.
Reich cautions that the guidebook will not be meant to be prescriptive or definitive, but something that can help spark thought and discussion.
“Writing a guidebook on generative AI in schools in 2025 is a bit of bit like writing a guidebook of aviation in 1905,” the guidebook’s authors note. “Nobody in 2025 can say how best to administer AI in schools.”
Schools are also struggling to measure how student learning loss looks within the age of AI. “How does bypassing productive considering with AI look in practice?” Reich asks. “If we expect teachers provide content and context to support learning and students now not perform the exercises housing the content and providing the context, that’s a major problem.”
Reich invites people directly impacted by AI to assist develop solutions to the challenges its ubiquity presents. “It’s like observing a conversation within the teacher’s lounge and alluring students, parents, and other people to participate about how teachers take into consideration AI,” he says, “what they’re seeing of their classrooms, and what they’ve tried and the way it went.”
The guidebook, in Reich’s view, is ultimately a set of hypotheses expressed in interviews with teachers: well-informed, initial guesses in regards to the paths that schools could follow within the years ahead.
Producing educator resources in a podcast
Along with the guidebook, the Teaching Systems Lab also recently produced “The Homework Machine,” a seven-part series from the Teachlab podcast that explores how AI is reshaping K-12 education.
Reich produced the podcast in collaboration with journalist Jesse Dukes. Each episode tackles a selected area, asking necessary questions on challenges related to issues like AI adoption, poetry as a tool for student engagement, post-Covid learning loss, pedagogy, and book bans. The podcast allows Reich to share timely details about education-related updates and collaborate with people keen on helping further the work.
“The tutorial publishing cycle doesn’t lend itself to helping individuals with near-term challenges like those AI presents,” Reich says. “Peer review takes an extended time, and the research produced isn’t all the time in a form that’s helpful to educators.” Schools and districts are grappling with AI in real time, bypassing time-tested quality control measures.
The podcast will help reduce the time it takes to share, test, and evaluate AI-related solutions to latest challenges, which could prove useful in creating training and resources.
“We hope the podcast will spark thought and discussion, allowing people to attract from others’ experiences,” Reich says.
The podcast was also produced into an hour-long radio special, which was broadcast by public radio stations across the country.
“We’re fumbling around at midnight”
Reich is direct in his assessment of where we’re with understanding AI and its impacts on education. “We’re fumbling around at midnight,” he says, recalling past attempts to quickly integrate latest tech into classrooms. These failures, Reich suggests, highlight the importance of patience and humility as AI research continues. “AI bypassed normal procurement processes in education; it just showed up on kids’ phones,” he notes.
“We’ve been really incorrect about tech up to now,” Reich says. Despite districts’ spending on tools like smartboards, for instance, research indicates there’s no evidence that they improve learning or outcomes. In a brand new article for article for , he argues that early teacher guidance in areas like web literacy has produced bad advice that also exists in our instructional system. “We taught students and educators to not trust Wikipedia,” he recalls, “and to look for website credibility markers, each of which turned out to be incorrect.” Reich desires to avoid an analogous rush to judgment on AI, recommending that we avoid guessing at AI-enabled instructional strategies.
These challenges, coupled with potential and observed student impacts, significantly raise the stakes for schools and students’ families within the AI race. “Education technology all the time provokes teacher anxiety,” Reich notes, “however the breadth of AI-related concerns is far greater than in other tech-related areas.”
The dawn of the AI age is different from how we’ve previously received tech into our classrooms, Reich says. AI wasn’t adopted like other tech. It simply arrived. It’s now upending educational models and, in some cases, complicating efforts to enhance student outcomes.
Reich is quick to indicate that there aren’t any clear, definitive answers on effective AI implementation and use in classrooms; those answers don’t currently exist. Each of the resources Reich helped develop invite engagement from the audiences they aim, aggregating useful responses others might find useful.
“We are able to develop long-term solutions to varsities’ AI challenges, but it would take time and work,” he says. “AI isn’t like learning to tie knots; we don’t know what AI is, or goes to be, yet.”
Reich also recommends learning more about AI implementation from a wide range of sources. “Decentralized pockets of learning will help us test ideas, seek for themes, and collect evidence on what works,” he says. “We want to know if learning is definitely higher with AI.”
While teachers don’t get to decide on regarding AI’s existence, Reich believes it’s necessary that we solicit their input and involve students and other stakeholders to assist develop solutions that improve learning and outcomes.
“Let’s race to answers which are right, not first,” Reich says.
