When the University Goes All In

An ML Engineer and Professor on AI, Bias, and What's Really Happening in the Classroom

In this episode we explore what AI looks like from the inside: not as a shiny classroom tool, but as something real teams have to build, test, constrain, and question before it ever reaches teachers or students. We talk with Jon Landrigan, about what he is seeing from today’s college students as they learn, work, and think alongside AI. Topics covered: * How college students are using AI today * How AI tools move from prototype to production in education * Why a tool that works once may not work safely or fairly at scale * The risks of “vibe coding” for schools and district leaders * Prompt injection, edge cases, and other safeguards behind AI products * AI feedback, writing assessment, and the limits of automated grading * Automation bias and why teachers may over-trust AI recommendations * Personalization, learner profiles, and the danger of biased metadata * Cognitive offloading, student pressure, and what schools still need students to internalize * Resources for educators who want to better understand AI systems

Was This AI Tool Built for Learning or Just for Work?

In this episode we explore the question: were the AI tools entering classrooms actually designed for learning? We look at a May 2026 preprint from Hassan Khosravi, Ryan Baker, and colleagues that argues many AI tools now being used in schools were originally built for work, not education. Topics covered: * How does the tool handle cognitive effort? * What does the tool do when students make errors? * What does the tool measure as success? * Does the tool remember anything meaningful about the learner over time? * Why tools that look effective during practice may fail when students must work without them * What Khanmigo’s redesign can teach us about building AI around actual learner needs * Why procurement conversations should focus less on marketing claims and more on learning design Source: https://arxiv.org/pdf/2605.04816

Are Students Cheating With AI, or Are Assignments Unclear?

In this episode we explore why student perceptions of AI cheating are far less settled than many school policies assume. We look at new survey data from Oxford University Press showing that students draw very different lines around AI use, from seeing any AI support as off-limits to not viewing full AI completion of homework as cheating.  Topics covered: * Why students disagree about what counts as AI cheating * The connection between AI use, assignment purpose, and “fidelity” * Why AI detection tools are unreliable for high-stakes decisions * How teachers can clarify when independent thinking is the goal * What instructional coaches should look for when reviewing assignments * Why district AI policies need to explain the instructional “why,” not just the rule * How schools can shift from catching misuse to designing clearer learning conditions Sources: https://fdslive.oup.com/www.oup.com/oxed/research-reports/Navigating_AI_in_Education_Research_Report_June2026.pdf?region=uk https://www.sciencedirect.com/science/article/abs/pii/S0360131526000540

The Hidden Work That Makes EdTech Work

In this episode we explore the hidden human work that makes educational technology actually work. We look at a working paper on Khan Academy use in government boarding schools in Uttar Pradesh, India, where schools with dedicated “lab-in-charges” saw much stronger student learning gains than schools that had access to the same technology without that support. Topics covered: * What the Khan Academy “lab-in-charge” study found * Why the same technology produced very different outcomes across schools * The organizational and relational work behind successful EdTech use * Lessons from a 2012 personalized reading platform study at Achievement First * What the Khanmigo adoption story reveals about student engagement * How sophisticated AI can make old implementation gaps harder to see Sources: https://www.nber.org/papers/w34683 https://carlhendrick.substack.com/p/the-monthly-dispatch-whats-new-in-a9a

The Group Thinks the Same Thing

In this episode we explore whether AI should be used as a starting point for student writing, especially in ELA classrooms. Topics covered: * The NCTE AI ELA Framework for grades 6–12 * Why the “blank page” is such a real challenge in writing instruction * The difference between AI as a starting point and AI as a thinking partner * What research suggests about AI, brainstorming, and idea diversity * How AI can function as a mirror for student thinking rather than a replacement for it Sources: https://ncte.org/working-ela-ai-framework/ https://www.nature.com/articles/s41562-025-02173-x