AI in the Classroom - Daily

In this episode we explore why Gen Z students are growing more skeptical of AI even as they continue using it at high rates. Drawing on new Gallup survey findings and recent conversations about “cognitive surrender,” we look at what student attitudes toward AI may be telling educators right now: not that students want AI banned, but that they want real help using it thoughtfully. We also examine w...

In this episode we explore what happens when AI writing feedback improves a paper without necessarily improving the writer. Using new research from the University of Pennsylvania on graduate students using an AI teaching assistant, we look at the difference between revision and real learning, why group feedback can mask individual understanding, and what teachers can do to make AI feedback more in...

In this episode we explore the idea of “cognitive surrender” in classrooms: what happens when students or teachers rely on AI so quickly that they stop fully engaging in their own thinking. Drawing on a new preprint from researchers at Wharton, we examine how access to AI can improve accuracy when the tool is right, but worsen performance when it is wrong, especially under time pressure. Topics co...

In this episode we explore a powerful question for educators: what happens when AI doesn’t just support thinking, but starts to replace it? Drawing on Ezra Klein’s reflections after spending time with people on the frontier of AI, we examine the difference between using AI as a tool for cognitive offloading and slipping into what researchers call cognitive surrender . Topics covered: Ezra Klein’s...

In this episode we explore the rise of AI agents in education through the lens of Grammarly’s expanding vision for AI in the workplace and the classroom. We examine the idea that teachers might one day “build” agents that encode their expertise, then ask a harder question: what gets lost when teaching is reduced to a set of rules? We look at what this means for teacher expertise, student equity, a...

In this episode we explore new research on bias in AI writing feedback and what it means for teachers, school leaders, and anyone evaluating AI-powered writing tools in K–12 education. We look at a Stanford preprint on how large language models responded differently to the same student essay when only the attached demographic profile changed, and we connect that to earlier MIT research showing tha...

In this episode we explore what AI can and cannot do when it comes to helping young people prepare for hard conversations. Starting with a story about Gen Z workers using ChatGPT to rehearse salary negotiations and workplace conflict, we contrast that with the “spirit of the game” in ultimate Frisbee, where players are expected to resolve disagreements themselves in real time. From there, we ask a...

We explore the question, what should schools protect and prioritize that AI can never replace? We focus on the idea of the human core , including emotional intelligence, interpersonal connection, ethical judgment, and wellbeing. We also examine what happens when AI takes on more instructional and cognitive tasks, and whether schools are at risk of sidelining the very relationships that matter most...

We explore one of the more promising classroom uses of AI: helping teachers create custom decodable texts for early readers. We look at how AI can make it much faster to generate phonics-aligned reading passages connected to students’ interests and classroom experiences, while also examining why teacher expertise still matters at every step. Topics: How AI can help generate custom decodable texts...

We explore what a publishing controversy reveals about AI, authorship, and trust — and why educators should pay close attention. Using the case of a novel reportedly found to be largely AI-generated, we examine how the same challenges facing publishers are also showing up in schools. Topics covered: • The controversy around an AI-generated novel making it deep into the publishing pipeline • Why AI...

Drawing on the work of Lee Shulman and a recent interview with researcher Andrew Lan, we look at why AI can generate content and imitate pedagogy, yet still struggle to understand how real students misunderstand, make mistakes, and learn. We also consider what this means for evaluating EdTech tools, teaching AI literacy, and the future potential of simulated student agents in instructional plannin...

We explore what kinds of work should teachers actually offload to AI, and what kinds of work should stay fully human? We examine the argument that so-called “busy work” is often not busy work at all, but part of the real craft of teaching. We push on that idea by asking where AI can genuinely reduce friction and where it risks weakening judgment, reflection, and professional voice. Topics: Why som...

We explore how “AI slop” is flooding children’s media, and why that matters for classrooms, not just screens at home. We look at a new story from The 74 on the rise of AI-generated kids’ videos that appear educational but are often inaccurate, incoherent, or even unsafe. Topics covered: How AI-generated children’s videos can look educational while teaching nonsense Why “educational-looking” is no...

We cover a core tension in AI and writing instruction: just because AI can take feedback and grading work off a teacher’s plate, does that mean it should? We explore what’s gained when teachers stay deeply involved in reading and responding to student writing, and where AI may help without replacing the professional judgment that strong writing instruction depends on. Topics covered: Why writing i...

We explore a deceptively simple but increasingly urgent question: How should schools teach students to use AI? We dig into a major report arguing that the risks of generative AI in children’s education may outweigh the benefits. From there, we examine the difference between AI-enriched learning and AI-diminished learning , and we ask what happens when schools put the tools ahead of the thinking. W...

We explore what it would mean to move beyond using AI to simply speed up traditional tests and instead use it to rethink assessment itself.  We look at how AI could help schools design more authentic, responsive, and instructionally useful assessments. We also explore a central tension: will AI merely help schools build a faster version of the assessment systems they already have, or can it help e...

We take a closer look at one of the most repeated claims in AI and education: that AI is saving teachers hours every week. But instead of accepting the headline at face value, I ask a more important question: what should schools actually be optimizing for? Topics covered in this episode: Why claims about AI “saving teachers time” deserve more scrutiny The limits of self-reported productivity data...

We explore a provocative question at the center of today’s AI conversation in education: when AI boosts productivity, what actually happens to the time it saves? Drawing on a recent workplace study discussed in Harvard Business Review , we examine whether AI reduces workload or quietly expands it, and what that could mean for teachers, school leaders, and the future of work in schools. Topics cove...

We explore a central tension in AI and writing instruction: if educators want authentic student work, is handwriting really the answer? Using research on handwriting versus typing, plus reflections on Chris Lehman’s argument for transparency in schools, we examine why a “return to the blue book” may create new problems instead of solving the old ones. Topics covered in this episode: Why concerns a...

We reflect on Chris Lehman’s recent writing about the state of AI in education and explore a practical middle ground between AI hype and total rejection. We look at why schools are still wrestling with cheating, misaligned expectations, and the real limits of “innovative” lesson design, while also asking where AI may genuinely help, especially in assessment and writing. Topics covered: Chris Lehma...

We talk about what happens when district AI guidance arrives after schools have already spent years figuring things out on their own. We look at the newly released New York City Department of Education AI guidance and compare it with how individual schools across the city have already been responding to AI in very different ways. Topics covered: NYC DOE’s newly released AI guidance How schools res...

In this episode we unpack New York City Public Schools’ new red, yellow, green AI guidance framework and explore what it signals for teachers, school leaders, and district administrators across the country. We look at why this policy matters not just because NYC is the largest school district in the U.S., but because it offers one of the clearest examples yet of how a district is trying to separat...

Who is classroom tech really built for? We reflect on a long-running tension in K–12 EdTech: the gap between what districts buy, what teachers are expected to use, and what students actually experience. We explore what happens when software is designed more for dashboards, monitoring, and accountability than for genuine teaching and learning. Topics covered in this episode: Why critiques of EdTech...

We examine the hidden costs of “free” AI tools in schools. The real issue is not just convenience or productivity. It’s what happens when teachers upload student writing, IEP notes, and other sensitive information into tools that districts have not approved and may not fully understand. Topics covered in this episode: • Why “free” AI tools are often paid for with user data • How student writing ca...

In this episode, we examine a troubling MIT study showing that AI chatbots can respond differently based on who they think they’re talking to, sometimes giving less accurate, more patronizing, or even mocking responses to users described as less educated or non-native English speakers. This episode looks at: what AI bias can look like in practice  why district leaders need better ways to evaluate...