AI in the Classroom - Daily

In this episode we explore what can get lost when asking a person turns into asking a machine, and why the ability to ask other people for help may be a skill schools need to deliberately protect and practice.Drawing on recent work from Pamela Cantor and Julia Freeland Fisher, we examine why young people may avoid reaching out, and what happens when AI becomes an easier, frictionless alternative.Topics covered:Why students may choose AI over asking another person for helpThe misconception that asking for help is a burdenJulia Freeland Fisher’s concept of “pro-social AI”What human relationships provide that AI interactions cannotWhy “weak ties” and social networks matter for studentsHow AI could affect students’ ability to seek help and build relationshipsA classroom routine for practicing how to identify and approach human expertsSources:https://biologyofbecoming.substack.com/p/trauma-dumping-is-what-friendshiphttps://substack.com/inbox/post/214866089https://www.the74million.org/article/survey-young-people-turn-to-ai-to-be-their-real-unfiltered-selves/

In this episode we explore what authentic student engagement can look like in the age of AI.Stories of extraordinary project-based learning can be inspiring but can also feel out of reach for the average teacher trying to make tomorrow’s lesson more meaningful.We look at a different model from Notre Dame’s God and the Good Life course and think about how we design learning experiences students actually need to inhabit for themselves.Topics covered:What AI is exposing about some schoolworkThe limits of using project-based learning as a model for everyday classroomsThe difference between redesigning an assignment and redesigning an entire schoolHow teachers can increase engagement within the constraints of their existing classroomsWhat authentic student work might look like in the age of AISources:https://www.the74million.org/article/schools-have-a-motivation-problem-ai-is-making-it-impossible-to-ignore/https://philosophy.nd.edu/courses/1st-courses-in-philosophy/god-and-the-good-life-an-introduction-to-philosophy/

In this episode, we explore three stories that look encouraging on the surface, but get much more complicated when you look closely.A school district says AI helped students become better critical thinkers.SchoolAI is making new commitments around student privacy. And a new national framework could change how future teachers are prepared to use AI before they ever enter the classroom.In this episode, we explore:The surprising results of a two-year classroom AI studyWhy “AI improved critical thinking” may not mean what it sounds likeA new development in the fight over student data and AIThe privacy problem that contracts alone may not be able to solveWhy teacher preparation programs are suddenly rethinking AIThe skills future teachers may need that weren’t part of teacher training just a few years agoSources:https://www.eschoolnews.com/digital-learning/2026/09/09/aacte-releases-national-framework-on-ai-in-educator-preparation/https://www.edweek.org/technology/microsoft-agrees-to-new-student-privacy-protections-for-ai-how-ironclad-are-they/2026/09https://www.k12dive.com/news/utah-district-reports-critical-thinking-boost-amid-ai-deployment/829919/

In this episode we explore the question, what if AI were designed to strengthen human relationships rather than replace them?We look at Prosocial Possibilities, an early-stage study from the Clayton Christensen Institute examining how AI and technology might help students identify, reach out to, and build stronger relationships with mentors, counselors, alumni, and other people in their networks.Topics covered:The Clayton Christensen Institute’s Prosocial Possibilities researchHow AI affects students’ habits of asking other people for helpThe difference between AI that replaces relationships and AI that facilitates themWhat the student experience with Khanmigo can teach us about AI tutoringHow Uprooted Academy used technology to help students activate their support networksHow Overgrad explored reducing the anxiety and friction of professional networkingWhy some kinds of “friction” in learning are productive—and others may not beSource:https://www.christenseninstitute.org/publication/prosocial-possibilities/

In this episode we look at a randomized study of students in England that compared three approaches to reading: traditional note-taking, using a large language model (LLM), and combining an LLM with note-taking. Three days later, researchers tested students on retention, comprehension, and free recall.Topics covered:What the study found about AI, note-taking, and reading comprehensionThe gap between what students prefer and what actually improves learningThe case for having students think and take notes first, then turn to AIHow student interactions with an LLM could give teachers insight into misconceptions and areas of difficultyWhat the study tells us about AI’s role in reading and learningSource:https://www.sciencedirect.com/science/article/pii/S0360131525002829

In this episode we explore what it’s like to go through college with AI always available.Dan Cogan-Drew sits down with his son Leo, a 21-year-old college senior who began college in 2023, making him part of the first generation of undergraduates whose entire college experience has unfolded alongside widely available generative AI.Topics covered:How a college student actually uses AIUsing AI as “damage prevention”What Leo is comfortable using AI forThe difference between using AI in humanities and science coursesCognitive offloadingHow AI changes the choices students have to make about reading, writing, and studyingWhat Leo would tell high school students preparing to enter college in the age of AI

In this episode we explore a consequential week for AI in education, and the growing tension between how quickly technology companies are putting AI into schools and how quickly school systems can decide what responsible adoption should look like.We examine new AI restrictions from New York City Public Schools and Los Angeles Unified School District, including why two of the nation’s largest school systems are taking such cautious approaches. We also look at the broader environment behind those decisions: Google making Gemini available to students by default, Anthropic expanding Claude into education, and the challenge schools face when powerful new technologies arrive before districts have had the opportunity to evaluate or intentionally adopt them.Topics covered:New York City’s restrictions on student AI useLAUSD’s decision to block AI on district devicesGoogle making Gemini available to students by defaultWhy “opt-out” AI adoption creates challenges for schoolsThe importance of keeping educators and administrators in the loop when new AI features launchAnthropic’s Claude offerings for teachers and administratorsStudent privacy concerns when educators upload information to AI toolsThe American Psychological Association’s guidance on children, adolescents, and education technologyThe launch of Responsible Technology and EducationHave a story we should be following, or are you experimenting with AI in your classroom, school, or district? Reach us at [email protected]:https://apnews.com/article/zohran-mamdani-ai-ban-nyc-schools-647f6a968eea0399521b7934418b1affhttps://www.k12dive.com/news/lausd-restricts-all-students-from-using-ai-tools/829612/https://www.edweek.org/technology/schools-caught-flat-footed-after-google-makes-gemini-chatbot-available-to-all-students/2026/09

New York City Public Schools has announced a one-year moratorium on student-facing generative AI for grades K–8, a policy affecting roughly 600,000 students and one of the most significant moves yet by a major school district to put limits on AI in the classroom.In this episode we explore what NYC’s new AI policy gets right, where it may go too far, and the questions educators and district leaders should be asking as schools decide when and how students should use generative AI.Topics covered:• What NYC’s one-year K–8 generative AI moratorium actually does• NYC’s new Technology in Schools Coalition• The district’s limited high school AI pilots• Why AI literacy matters• Whether middle schools should have more flexibility to pilot AI tools• What “evidence of instructional impact” should actually mean• The role politics and the technology industry may play in shaping school AI policy• Why NYC’s decision could influence how other major districts approach AISources:https://www.nyc.gov/mayors-office/news/2026/09/mayor-mamdani-and-chancellor-samuels-put-students-first-with-nathttps://laist.com/brief/news/education/lausd-students-barred-artificial-intelligence-toolshttps://www.edsafeai.org/

In this episode we explore the relationship between AI use, belonging, and social connection among young people, and what two recent studies suggest educators should understand as students return to school.Drawing on research from the Rhythm Project and the California Partners Project, we look at why some young people are turning to AI for more than help with tasks.Topics covered:• The connection between students’ sense of belonging and their AI use• Why emotional vulnerability may predict higher-risk AI use• The difference between purposeful AI use and dependency on AI for emotional support• Why students who feel unwelcome in physical spaces may turn to AI chatbots more often• The lack of conversations between young people and adults about AI• Why teachers need to understand students’ out-of-school AI experiences• What schools can do to create stronger opportunities for belonging and human connectionSources:https://www.calpartnersproject.org/_files/ugd/2ceb15_fbca2279eb654ca1a91f2c10e756e6ab.pdfhttps://drive.google.com/file/d/1oGI3uxseNdfIc2Pms19TsKBQOB7wnJhV/view

In this episode we explore what thoughtful AI use looks like through the eyes of a first-year teacher preparing to enter the classroom.We speak with Stefanie Stoj, a recent Wesleyan University graduate and Teach For America corps member who is beginning her career teaching seventh-grade ELA in the South Bronx. Stefanie shares how she used tools like ChatGPT and Claude during summer teaching and lesson planning.Topics covered:How a first-year teacher is preparing for her seventh-grade ELA classroomUsing AI to anticipate student misconceptionsUsing ChatGPT and Claude as lesson-planning toolsWhere AI can save teachers time without replacing professional judgmentThe risk of gradually outsourcing more thinking to AIWhat Stefanie learned about AI use as a college studentBalancing efficiency with the individual needs of studentsHow new teachers can develop their own judgment while working with AISource:https://www.sbecacs.org/

In this episode we explore when AI can actually improve cognition, and when it risks doing the thinking for students.Building on Dr. Philippa Hardman’s work on cognitive offloading, we examine what separates productive AI use from simply handing cognitive work over to a machine. Topics covered:When AI use can improve cognitionThe difference between using AI as a collaborator and using it as a substitute for thinkingWhy AI should “critique, never complete”The debate over which tasks can safely be offloadedHow learner motivation changes the risks of cognitive offloadingPractical design principles for using AI without undermining the learning itselfSource:https://drphilippahardman.substack.com/p/how-to-design-around-cognitive-offloading

In this episode we explore three stories shaping the conversation around AI in education, and a common thread running through all three: AI alone isn’t enough.We look at how Khan Academy and Google are evolving Khanmigo beyond the original vision of a student-facing AI tutor. We also examine the idea of AI as an “arrival technology,” technology that entered schools before districts had time to build the policies.Finally, we dig into new research from OpenAI examining what happens when students use ChatGPT alongside explicit critical-thinking instruction.Topics covered:• How Khanmigo is evolving away from a student-facing AI tutor• Google and Khan Academy’s new AI-supported classroom capabilities• The challenge districts face when students already use AI outside approved school systems• OpenAI research on ChatGPT combined with critical-thinking instruction• What these developments mean for teachers and district leadersSources:https://openai.com/index/what-students-gain-from-chatgpt-critical-thinking-training/https://www.marketscale.com/industries/education-technology/k-12-ai-spending-is-moving-from-classroom-apps-to-vetting-policy-and-proofhttps://blog.google/products-and-platforms/products/education/khan-academy-back-to-school/

In this episode we explore the growing concern around cognitive offloading, what happens when students hand too much of the thinking over to AI.Drawing on recent research highlighted by Dr. Philippa Hardman, we examine an important distinction: AI itself isn’t necessarily the problem. What matters is when and how it enters the learning process.Topics covered:What cognitive offloading actually meansHow AI-generated feedback can narrow thinking when it arrives too earlyWhy deadlines, grades, and output-focused assignments can encourage students to outsource thinkingHow unstructured AI use can increase cognitive load rather than reduce itSource:https://drphilippahardman.substack.com/p/how-to-design-around-cognitive-offloading

In this episode we explore how schools can embrace AI without losing sight of what education is actually for.We sit down with Chris Lehmann, founder and principal of Philadelphia’s Science Leadership Academy (SLA), an inquiry-driven, project-based school built around five core values: inquiry, research, collaboration, presentation, and reflection. Our conversation examines how those values are shaping SLA’s approach to AI—and why decisions about what students can outsource to AI ultimately require schools to be much clearer about what they want students to learn and become.Topics covered:How SLA’s inquiry-driven, project-based, caring model shapes its approach to AISLA’s approach to AI, citation, and plagiarismHow AI can support students during long-term projects and senior capstonesWhat students should—and shouldn’t—outsource to AIHow technology, algorithms, and information overload are changing what students need from schoolsWhy AI forces educators to reconsider the deeper purpose of schoolHow schools can preserve student thinking, agency, and human relationships as AI becomes more capableSource:https://2027.educon.org/

What does the latest research actually tell us about how generative AI affects student learning?In this episode we explore four different patterns of AI use in K–12 classrooms, and why the way AI is designed and implemented may matter more than the tool itself.Topics covered:The difference between AI-assisted performance and durable learningWhat can happen when students have unrestricted access to tools like ChatGPTWhy guardrails and guiding questions may help preserve student thinkingHow purpose-built AI can encourage reflection and metacognitionHow teachers can test whether learning transfers once AI is removedWhy schools should focus on how AI is configured, not simply which AI tool they adoptSource:https://barker.institute/documents/188/Young_People_learning_and_generative_AI.pdf

In this episode we explore why asking “Does AI tutoring work?” may be the wrong question.A new Stanford University research brief argues that AI tutoring isn’t a single model. Instead, it exists along a spectrum—from fully automated AI self-study to traditional human tutoring—with very different levels of research support behind each approach.We break down six models of AI tutoring, examine what the evidence currently tells us about each, and consider what districts should look for before investing in AI-powered tutoring. Topics covered:The six different models of AI tutoringWhy “AI tutoring” is too broad a category for evaluating effectivenessWhich tutoring approaches have the strongest research supportWhy fully automated AI tutoring still has significant evidence gapsThe role human tutors can play alongside AIThree questions district leaders should ask before adopting an AI tutoring modelWhy implementation may matter as much as the technology itselfSources:https://scale.stanford.edu/sites/default/files/AI%20Tutoring%20is%20Not%20a%20Monolith%20What%20We%20Actually%20Know.pdf

In this episode we look at the growing arms race around AI watermarking and why new methods for identifying AI-generated text may not provide the reliable detection solution schools are hoping for. We also unpack new survey data from Common Sense Media showing how high school students are actually using AI.Topics covered:The emerging arms race between AI watermarks and watermark-removal toolsWhy watermarking may not solve schools’ AI detection problemWhat new survey data reveals about high school students’ AI useThe gap between student AI adoption and teacher guidanceStudents’ concerns about AI, critical thinking, and losing opportunities to learnNew U.S. Department of Education guidance on education technologyThe shift from measuring EdTech adoption to measuring learning outcomesWhy districts still face major variation in AI policy, privacy, accessibility, and data protectionSources:https://www.edsurge.com/news/department-of-education-issues-long-awaited-edtech-guidance-for-states-and-districtshttps://www.commonsensemedia.org/research/teens-in-the-ai-era-schoolwork-and-skills-that-matterhttps://watermark.sabrina.dev/

In this episode we explore why AI policy alone may not be enough for schools navigating a technology that is changing faster than the traditional policy cycle.We reflect on a conversation with a veteran high school English teacher who feels a growing gap between district AI guidance and the realities teachers are encountering in their classrooms. Topics covered:The growing gap between district AI policy and classroom realityWhy static policies may struggle to keep pace with rapidly changing AITeacher concerns about trust, student AI use, and academic workThe isolation educators can feel when there’s no venue to discuss AI openlyWhat schools can learn from bringing AI skeptics and advocates into the same conversationThe case for standing AI councils that include teachers, students, administrators, families, and board membersSources:https://www.brookings.edu/articles/enhancing-student-agency-through-school-ai-councils/https://www.edutopia.org/article/talking-technology-school-staffhttps://theimportantwork.substack.com/p/what-does-it-mean-if-your-ai-position

In this episode we explore new research on AI-supported tutoring that suggests that giving students access to an AI tutor, even during structured school time, doesn’t necessarily mean they’ll use it as intended. Topics covered:What a two-year study of Khan Academy’s Khanmigo found about student achievement and engagementWhy structured practice may matter more than conversational AI tutoringWhat another study tells us about pairing human support with AI learning platformsThree qualities that distinguish effective human tutorsThe “reach-out problem”Why student motivation, confidence, and relationships matter for tutoringHow AI tutors may need to evolve if they’re going to approach the impact of high-dosage human tutoringSources:https://edworkingpapers.com/sites/default/files/ai26-1451.pdfhttps://www.linkedin.com/events/7487956708225433600/https://www.nber.org/papers/w35620#fromrss

In this episode we explore how AI can help school leaders reclaim time, reduce administrative burden, and focus more of their attention on the work that matters most: supporting teachers, students, and learning.For this Teacher Tuesday conversation, we sit down with Carl Johnson, Associate Director at the New England Association of Schools and Colleges (NEASC) and a former school principal. We explore when should students use AI, and what happens when educators don’t have enough expertise to evaluate an AI-generated answer. Topics covered:How AI can help school leaders reduce administrative workloadWhy educators may already have many of the skills needed to work effectively with AIPractical AI workflows for meetings, brainstorming, classroom observations, and follow-upWhy human judgment and “critical distance” still matter when working with AIThe importance of transparency when school leaders use AIWhat schools can learn from the adoption of smartphones and social media

In this episode we explore how competitive academic environments may be shaping the way students use AI, and why some students may feel they have little choice but to use it.Can the way we grade, rank, and define success unintentionally turn AI from a useful option into a perceived necessity?Topics covered:How social comparison can influence students’ AI useWhy competitive students may experience AI-specific FOMOThe difference between strategic AI use and anxiety-driven relianceHow grading curves, class rank, and college admissions pressure can affect AI adoptionWhy students may use AI even when they have negative feelings about itHow teachers can make expectations around AI use more explicitThe role of AI-free assignments, transparency, citation, and reflectionSource:https://arxiv.org/abs/2606.03560

In this episode we explore three AI-in-education stories that point to a growing challenge for schools: districts are investing in AI faster than researchers, policymakers, and educators can determine what actually works.Topics covered:Why districts still lack clear guidance for evaluating AI toolsWhat current research suggests about AI, cognitive offloading, and student learningWhat San Antonio ISD’s mixed results tell us about pilots, causation, and implementationWhy positive student outcomes alone may not be enough to prove an AI product worksAnthropic’s introduction of machine-readable watermarks for Claude-generated textHow transparency about AI involvement could change assessment and academic integritySources:https://stateline.org/2026/08/12/schools-spend-billions-on-ai-but-struggle-to-figure-out-whats-worth-it/https://ies.ed.gov/learn/blog/ai-k-12-education-good-bad-and-guardrails-considerhttps://www.yahoo.com/news/us/articles/saisd-put-ai-middle-schools-100000048.html

In this episode we explore whether school districts should be more willing to say no to new education technology, especially generative AI.We are joined by Claude for a conversation about the research paper Refusing Educational Technology: Artificial Intelligence, Inequity, and the Problem of Critical Optimism. The paper challenges a familiar stance in education: acknowledging the risks of new technology while assuming that, with the right guardrails, schools should ultimately find a way to adopt it.Topics covered:Why “critical optimism” may still tilt schools toward technology adoptionWhen saying no to an AI or EdTech product can be a legitimate district decisionThe difference between technology that supports teacher agency and technology that asks educators to step asideHow districts can define success, failure, and acceptable outcomes before launching a pilotWhy vendor usage data shouldn’t become the default measure of whether an implementation is workingHow districts can prevent optional tools from slowly becoming prescribed implementation modelsSource:https://journals.sagepub.com/doi/abs/10.3102/0013189X261466234

In this episode, we explore how one school district is responding to AI not as a passing trend, but as a rapidly evolving reality for students, teachers, and school leaders.We are joined by Alicia Kersten, Principal of Somerville High School, and Jason Behrens, Innovation Project Specialist for Somerville Public Schools, for a conversation about how Somerville is approaching AI from both the classroom and district perspectives.Topics covered include:Why Somerville believes schools can’t simply avoid AIHow student AI use is changing teaching, writing, and assessmentProtecting critical thinking while allowing students to use new toolsPreparing students to use AI in college, careers, and technical fieldsHow teachers can use AI without adding another overwhelming initiativeBringing students, teachers, administrators, and families into the conversationThe practical tension between innovation, budgets, priorities, and implementationSource:https://www.aasa.org/resources/resource/students-first-act

In this episode we explore what happens when AI becomes more than a classroom tool, and starts competing with classroom learning itself.Topics covered:How AI may change students’ expectations about what learning should feel likeWhy immediate AI responses could make classroom dialogue feel slower by comparisonThe connection between AI use, boredom, and delayed gratificationHow students are already using AI for entertainment, advice, creativity, and personal questionsWhy students may reach for AI before attempting difficult work themselvesCognitive offloading and the question: “Why should I do this if AI can?”Why impatience with confusion may be an important classroom signalThis is Part 2 of a two-part series on student attention.Source:https://www.aft.org/ae/spring2026/willingham

In this episode we explore a question many educators are asking: Have students actually lost the ability to pay attention, or has something else changed?Topics covered:Why teachers report declining student reading staminaWhat research actually says about screens and attention capacityThe difference between attention ability and willingness to pay attentionHow “delay discounting” can influence persistence and schoolworkWhy phones may make ordinary classroom tasks feel more boringHow the availability of distractions changes students’ experience of learningWhy briefly checking a phone may actually increase boredom afterwardWhat cell phone restrictions can—and cannot—solveThis is Part 1 of a two-part series on student attention.Source:https://www.aft.org/ae/spring2026/willingham

In this episode we explore four stories that reveal how quickly AI is accelerating across education, and why schools should remain skeptical of claims that move faster than the evidence.We examine ByteDance’s plan to add AI-generated video lessons to the study app Gauth, the rise of low-quality AI-created resources on Teachers Pay Teachers, Alpha School’s rapid expansion of its AI-centered private school model, and a student-written framework for K–12 AI policy.Topics covered:• Whether high-production AI videos improve learning, or simply increase engagement• How “AI slop” is entering teacher marketplaces and exploiting educators’ limited time• Why platforms must take greater responsibility for vetting AI-generated instructional materials• Alpha School’s two-hour AI learning model and its decision to replace licensed teachers with “guides”• The unproven claim that AI tutoring can reproduce the effects of intensive human tutoring• Student proposals for AI opt-outs, fact-checking requirements, bias disclosures, and approved school AI toolsSources:https://www.aasa.org/docs/default-source/resources/reports/17_s-2026-the-students-first-act-final-bill-passed-84-14-july-19-2026.pdf?sfvrsn=ef93d4d2_1https://www.axios.com/2026/08/02/alpha-schools-ai-expansion-50-campuseshttps://www.businessinsider.com/seedance-bytedance-education-push-study-app-gauth-ai-animations-2026-7https://www.aasa.org/resources/resource/students-first-act 

In this episode we explore why teacher AI literacy—not the latest tool—is the most important investment schools can make.Topics covered:• Why the same AI tool can produce very different classroom outcomes• The six dimensions of teacher AI literacy• Technical fluency and understanding how generative AI works• Evaluating AI outputs for accuracy, bias, and missing perspectives• Keeping teachers in control of human–AI collaboration• Deciding when AI supports learning—and when it replaces necessary thinking• Ethical reasoning, academic integrity, and student ownership• The importance of teacher agency in school AI policy• Why tool-specific training is not enoughSource:https://arxiv.org/pdf/2608.01705

In this episode we explore how AI chatbots may shape students’ understanding of what a real conversation—and real thinking—sounds like.We examine the idea of “critical distance”: a student’s ability to recognize what is missing from an AI-generated response. We also consider why some schools and districts are limiting younger students’ access to conversational AI.Topics covered:How the chatbot interface creates a false model of conversationWhy speed and confidence are not the same as thoughtful reasoningWhat authentic pedagogical dialogue sounds likeHow students develop critical distance from AI-generated answersWhy age-based AI policies and access gates may matterSources:https://www.nytimes.com/2026/02/23/business/ai-literacy-faq.htmlhttps://www.aft.org/news/afts-weingarten-unveils-10-point-plan-boost-student-learning-ai-erahttps://www.wired.com/story/some-kids-will-never-think-ai-is-cool/https://www.oecd.org/en/about/projects/pisa-2029-media-and-artificial-intelligence-literacy.html

In this episode we explore why AI tutors and chatbots often praise students instead of giving them the honest, challenging feedback that learning requires.We examine “psychofancy,” the tendency of AI systems to flatter, validate, or agree with users when correction would be more useful. Topics covered:Why excessive AI praise can interfere with productive struggleHow AI feedback differs from feedback provided by teachers and peersWhy students need to question whether feedback is truthful, useful, or simply flatteringHow speed, confidence, and isolation can make AI responses feel authoritativeWhy teachers should introduce AI feedback in a group setting before students use it independentlyHow comparing AI, peer, and teacher feedback can strengthen student judgmentSource:https://www.the74million.org/article/ai-tutors-are-praising-instead-of-teaching-heres-why-thats-hurting-students/

In this episode we explore what it means to be literate in a world where algorithms increasingly decide what information students see, and what they never encounter.We examine the OECD’s new Media and Artificial Intelligence Literacy framework, known as MAIL, and its argument that AI literacy must extend beyond evaluating whether a piece of content is accurate.Topics covered:The shift from using AI tools to living inside AI-mediated environmentsThe OECD’s Media and Artificial Intelligence Literacy frameworkPISA’s three foundational ideas: authors and audiences, messages and meanings, and representations and realitiesHow platforms are designed to capture and monetize attentionThe effects of algorithmic personalization, echo chambers, and filter bubblesWhy students cannot always see which perspectives have been filtered outThe values and biases embedded in AI training data and system designWhy AI literacy should include asking, “Why did I see this?”What the planned 2029 PISA assessment could mean for teachers, schools, and education systemsSource:https://www.oecd.org/content/dam/oecd/en/about/projects/edu/the-pisa-2029-media-and-artificial-intelligence-(mail)-assessment-will-shed-light-on-whether-young-students-have-had-opportunities-to-learn-and-to-engage-proactively-and-critically-in-a-world-where-production,-participation,-and-social-networking-are-increasingly-mediated-by-digital-and-ai-tools-/PISA%202029%20MAIL%20Assessment%20Framework%20First%20Draft.pdf

In this episode we explore a growing problem for school districts: how do you govern AI use when you cannot clearly see where or how it is already being used?Looking at recent policy moves in New York City, Fairfax County Public Schools, and Katy ISD, we examine the gap between official AI rules and the reality inside schools. Topics covered:Why districts often lack a complete inventory of AI toolsNew AI restrictions in New York City, Fairfax County, and Katy ISDWhy defining “AI” is harder than it soundsThe limits of bans, firewalls, and approved-tool listsThe trade-off between surveillance and teacher autonomyThe risks of policies that districts cannot consistently enforceWhy AI governance is now more urgent than AI adoptionWhat the forthcoming student-led AI Bill of Rights for Schools could add to the debateSources:https://www.houstonchronicle.com/neighborhood/katy/article/katy-isd-ai-policy-generative-ai-students-22357245.phphttps://www.the74million.org/article/students-convene-to-hammer-out-ai-bill-of-rights-for-schools/https://www.ffxnow.com/2026/07/23/fairfax-county-school-board-approves-restrictions-on-generative-ai-digital-devices/

In this episode we explore why AI tutors may be improving student performance without producing the deeper, transferable learning schools actually want.Topics covered:• Why strong tutoring results may not translate beyond the tutoring environment• The difference between task completion and genuine transfer• What brain-training research can teach us about AI tutors• Why current assessment systems shape what AI tools optimize for• What teachers and district leaders want from next-generation assessment• How multimodal AI could capture student reasoning and problem-solving• Why changing assessment may be the key to improving AI tutoringSources:https://thedigitaldelusion.substack.com/p/are-ai-tutors-really-teaching-studentshttps://www.gettingsmart.com/2026/07/17/weve-been-judging-symphony-rehearsals-by-the-last-note-why-multimodal-ai-can-finally-measure-mathematical-practice/https://marketbrief.edweek.org/meeting-district-needs/whats-next-for-k-12-assessment-the-demand-for-innovation-in-k-12-testing/2026/06

In this episode we explore whether schools are actually falling behind on AI—or whether educators are adopting the technology faster than headlines suggest.Topics covered:• How teacher AI adoption compares with other professions• Why experimentation does not always lead to regular, confident use• The training and access barriers facing educators• What students misunderstand about AI and truthfulness• Why AI literacy matters for student health and safety decisions• The difference between moving fast and building sustainable infrastructureSources:https://www.gallup.com/699797/indicator-artificial-intelligence.aspxhttps://www.sciencedirect.com/science/article/pii/S2666920X25001043https://www.k12dive.com/news/ai-is-moving-quickly-how-can-districts-keep-up/816265/

In this episode we explore how teachers are moving beyond restrictive AI policies and building classroom approaches rooted in trust, transparency, and student learning.Topics covered:Why classroom AI policies are shifting from restriction to transparencyHow disclosure can create opportunities for teaching and reflectionThe value of including student voices in policy designWhy AI use should be understood as a spectrum rather than a yes-or-no questionHow teachers can explicitly teach responsible AI decision-makingWhy classroom policies need to evolve throughout the school yearHow educators can model the transparency they expect from studentsThe difference between designing for compliance and designing for integritySources:https://www.edutopia.org/article/8-classroom-ai-policies-developed-by-teachers/https://www.edutopia.org/article/ai-transparency-surveys-tool-navigating-student-choices-togetherhttps://wpvip.edutopia.org/wp-content/uploads/2026/07/Class-AI-Agreement_Stacy-Kratochvil.pdf

In this episode we explore why two recent studies—one showing AI weakening students’ math performance and another showing AI-enhanced feedback improving writing—may actually point to the same conclusion: the impact of AI depends on how learning is designed.Topics covered:What research reveals about AI use and declining math performanceHow combining peer and AI feedback can strengthen student writingThe difference between receiving an answer and exercising judgmentWhy AI exposes assignments built around task completionUnderstanding by Design and the role of backward planningHow to identify meaningful transfer goalsDesigning writing tasks around audience, purpose, and lived experienceCreating math assessments that measure genuine problem-solvingWhy stronger assignment design may be more effective than banning AISources:https://arxiv.org/pdf/2605.21629https://hechingerreport.org/proof-points-ai-eroding-math-skills/https://www.sciencedirect.com/science/article/pii/S8755461525000088

In this episode we explore three stories that reveal where AI in education actually stands, not where the industry says it is headed, but what schools are dealing with right now.Topics covered:• Why Claude for Teachers raised student-data and FERPA concerns• Why district governance must come before teacher adoption• What the collapse of InBloom can teach today’s AI companies• How AI data centers are affecting laptop and Chromebook prices• Why some schools are pulling back from one-to-one devices• Y Combinator’s vision for AI tutors• The influence of The Diamond Age and “The Primer”Sources:https://drjoephillips.substack.com/p/anthropic-launched-claude-for-teachers?r=5qyhthttps://www.idahoednews.org/top-news/schools-face-soaring-laptop-costs-as-ai-data-centers-squeeze-supply/https://www.nytimes.com/2026/03/29/technology/chromebook-remorse-kansas-school-laptops.html 

In this episode we speak with Jim Bentley, a fifth- and sixth-grade teacher in Elk Grove, California, who is entering his 31st year in the classroom. We examine how Jim has begun using AI with students. Topics covered:The classroom as a laboratory for curiosity, experimentation, and productive mistakesHow project-based learning shaped Jim’s teaching philosophyAn AI image-generation project connected to Raymie Nightingale by Kate DiCamilloPrompt writing, revision, and healthy skepticism toward AI-generated resultsMedia literacy, lateral reading, and fact-checking AI responsesThe difference between using AI as a productivity tool and using it as a learning toolWhat it means for students to do “real work,” not merely “schoolwork”Rubric design, teacher judgment, and the risk of overvaluing polished final products

In this episode, part 4 of our series on the productive struggle for teachers, we explore a growing challenge in teacher preparation: how can supervisors tell whether a teacher candidate used AI as a thinking partner, or simply submitted a polished AI-generated lesson plan?In this episode we explore:Why AI can make a teacher candidate’s thinking invisibleThe difference between thoughtful AI-assisted planning and minimal revisionWhy teacher preparation programs should grade decision logs, not just lesson plansHow supervisors can identify genuine pedagogical reasoningWhich parts of lesson design should remain productive strugglesHow AI can remove unnecessary friction without replacing professional judgment

In this episode, part 3 of our series on the productive struggle for teachers, we explore how AI is shaping the habits of early-career teachers, and why the choices teachers make in their first five years could influence the rest of their careers.Topics covered:• Why early-career teachers may be especially vulnerable to passive AI use• How generic AI-generated materials can affect student engagement• The connection between teacher confidence, burnout, and retention• Why productive struggle matters when learning to teach• The importance of revising and personalizing AI-generated lessons• Why protected reflection time should be part of AI implementation• How early-career teacher support affects equity and the long-term teacher pipeline

In this episode we explore how teachers can use AI during lesson planning without outsourcing the thinking that helps them grow as educators.We look at why teachers should do the difficult thinking first, how AI can be used to pressure-test a lesson, and why revision and reflection are essential when evaluating AI suggestions. Topics covered:• Why lesson planning is a learning process for teachers• The difference between using AI as a thinking partner and outsourcing the work• Productive struggle and its role in developing teacher expertise• How pedagogical content knowledge is built through planning• Why AI-generated lesson plans require substantial revision• Metacognitive reflection as an alternative to cognitive surrenderSources:https://www.nature.com/articles/s41539-025-00311-8#citeashttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646

In this episode we explore two major developments shaping AI in K-12 education: Anthropic’s launch of Claude for Teachers and the fragmented policy environment surrounding AI adoption in schools.Claude for Teachers promises to reduce teacher workload, but the product’s integrations raise important questions about student data, classroom recordings, vendor agreements, and FERPA compliance.Topics covered:• What Claude for Teachers offers K–12 educators• How AI could support lesson planning and instructional coaching• The privacy risks of sharing classroom audio and student data• Why vendor integrations require district-level review• The difference between “FERPA-aligned” and FERPA-compliant use• New state laws governing student data and automated decisionsSources:https://www.chalkbeat.org/2026/07/14/anthropic-launches-claude-for-teachers-as-ai-companies-battle-for-classrooms/https://www.youtube.com/watch?v=V-OOEC5RNaQ

In this episode we explore what happens when teachers use AI to save time, and whether those shortcuts can come at a cost for students.Drawing on a statewide survey of more than 13,000 Georgia teachers, we examine a troubling tension: Teachers often believe AI improves their work, while emerging evidence suggests that certain patterns of AI use may reduce student motivation, confidence, and, in some classrooms, academic performance.Topics covered:• Why teacher AI adoption is accelerating• The gap between perceived productivity and student outcomes• Findings from Georgia’s statewide teacher survey• What a randomized controlled trial found about student motivation and confidence• Cognitive offloading and the “skill substitution” problem• How generic AI-generated materials can weaken a teacher’s personal voice• The difference between using AI as a shortcut and using it as a thinking partnerSources:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7007339https://www.the74million.org/article/more-than-half-of-georgia-teachers-now-use-artificial-intelligence-to-prepare-for-class/https://www.anthropic.com/news/claude-for-teachers

In this episode we explore what happens when teachers use AI to prepare lessons, assessments, discussion questions, and other classroom materials, and whether working faster actually leads to better learning.As OpenAI, Google, Microsoft, and Anthropic race to integrate AI into educators’ daily workflows, the promise is straightforward: save teachers time. But emerging research suggests that when teachers rely too heavily on AI-generated materials, students may find classes less interesting, feel less motivated, and, in some cases, perform worse.Topics covered:• The rapid growth of AI-assisted lesson preparation• The difference between faster content and better instruction• Research linking passive AI use to lower student motivation and engagement• Why students notice when classroom materials feel generic or impersonal• The launch of Claude for Education and integrations with teacher productivity tools• How time pressure and limited training influence teacher AI use• Why revising and personalizing AI-generated materials mattersSources:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7007339https://www.the74million.org/article/more-than-half-of-georgia-teachers-now-use-artificial-intelligence-to-prepare-for-class/https://www.anthropic.com/news/claude-for-teachers

In this episode we explore how AI can support student writing without bypassing the difficult, necessary work of learning to write.We speak with literacy expert Maggie Roberts about the foundational skills beneath strong writing, from handwriting and oral language to working memory and executive function. Together, we examine where AI can serve as a useful scaffold, where it may short-circuit learning, and how teachers can make thoughtful decisions based on a student’s age, needs, and instructional goals.Topics covered:• The foundational skills that support writing development• Why “thin” student writing may reflect barriers rather than disengagement• How AI can help teachers identify and respond to those barriers• The risks of using AI to generate writing for younger students• Using AI to support brainstorming, planning, and executive function• How teachers can decide when AI is a scaffold and when it becomes a shortcut• The importance of preserving productive struggle in the writing process• What students may gain or lose by using, or avoiding AI• Why AI literacy should include ethics, metacognition, and student agency• How schools can balance technology use with screen-free learningMaggie Beattie Roberts is a national literacy consultant, author, professional learning facilitator, and writing development specialist. She is the co-author of Foundational Skills for Writing with Melanie Meehan, which explores the cognitive, linguistic, motor, and executive-function demands of writing. Her forthcoming book, Unboxing the Curriculum, helps educators and school leaders navigate prepackaged curriculum and tailor it to their students’ needs. Learn more about Maggie's work at kateandmaggie.com

In this episode we sit down with Carey Swanson, Chief Program Officer for Literacy at Student Achievement Partners, to explore what strong literacy instruction should look like in an age of rapidly advancing AI.In this episode we discuss why new technology should not distract schools from what research tells us about reading, writing, knowledge-building, and meaningful engagement with text.Topics covered:• Connecting education research with real classroom practice• The role of literacy in helping students understand themselves and others• Why reading and writing remain essential in the age of AI• Keeping texts at the center of instruction• The importance of knowledge-rich curriculum and background knowledge• Productive struggle, cognitive friction, and meaningful learning• The difference between AI feedback and AI-generated student work• What educators should look for when evaluating AI-powered instructional tools

In this episode we explore why schools, districts, governments, and families are beginning to slow down AI adoption in education.From Norway’s restrictions on generative AI for younger students to policy delays in New York City, governance concerns in Portland, safety questions in Broward County, and parent-led calls for a pause across the Washington, D.C. region, a broader pattern is emerging: education leaders are asking whether schools are moving faster than they can responsibly govern.Topics covered:• Norway’s ban on generative AI for elementary-age students• New York City’s delayed AI guidance and calls for a moratorium• Portland’s decision to pause AI expansion until oversight is established• Broward County’s concerns about privacy, cybersecurity, and student safety• Parent-led efforts to pause student-facing AI in the Washington, D.C. area• The tension between innovation and responsible governance• What district leaders should consider before approving new AI tools• The growing gap between school policy and students’ everyday AI useSources:https://www.reuters.com/technology/norway-imposes-near-ban-ai-elementary-school-2026-06-19/https://www.chalkbeat.org/newyork/2026/06/24/nyc-education-department-delays-ai-guidance-after-backlash/https://www.oregonlive.com/education/2026/06/amid-concerns-that-ai-is-infiltrating-its-schools-portland-school-board-hits-pause.htmlhttps://www.cbsnews.com/miami/news/broward-school-board-meeting-ai-in-classrooms-june-2026/

In this episode we explore “the magenta line of learning” — a powerful metaphor borrowed from aviation to understand what happens when students become too dependent on AI tools before they have built real expertise.Topics covered:The “Children of the Magenta Line” aviation metaphorWhat Air France Flight 447 can teach us about automation dependencyWhy students may feel confident without actually understandingThe difference between performance and real learningCognitive load theory and why struggle mattersHow chatbots can give students fluency without schemaHow teachers can test whether students truly [email protected]:https://carlhendrick.substack.com/p/children-of-the-magenta-line

In this episode we explore what happens when teachers ask students directly whether AI belongs in the classroom.We look at a classroom conversation led by English teacher Marcus Luther, who asked his students whether he should integrate AI tools into his teaching. Their responses were striking.Topics covered include:Why student voices are often missing from AI-in-education debatesWhat one teacher learned by asking students whether AI belonged in his classroomHow students think about accomplishment, effort, and authentic learningThe difference between using AI in school and learning about AI for future workWhy teachers’ own perspectives on AI may shift after listening to [email protected]:https://thebrokencopier.substack.com/p/i-asked-my-students-about-ai-again

In this episode we explore what AI-generated fiction can teach us about human storytelling, student writing, and the value of literary messiness.We look at a new study on StoryScope from researchers at the University of Maryland and Google DeepMind, which compares the narrative structure of human-written short stories with stories generated by several major AI models.Topics covered:What the StoryScope study reveals about AI-generated fictionHow human stories differ in ambiguity, time, and unresolved meaningTim Requarth’s concern that AI may homogenize imagination before writing beginsHow English teachers can help students notice narrative complexityThe risk of “closed-loop” thinking in both storytelling and education technologySources:https://arxiv.org/pdf/2604.03136https://substack.com/home/post/p-200818672