When the University Goes All In

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

When AI Reads First

In this episode we explore why sequencing matters when AI enters reading instruction. Drawing on Student Achievement Partners’ argument to “keep text at the center,” we look at a practical question for teachers and district leaders: does AI arrive before students have wrestled with a complex text, or after they have made their own first attempt? Topics covered: * Why “keeping text at the center” depends on instructional sequence * The difference between AI as a reading scaffold and AI as a reading substitute * How pre-reading summaries can short-circuit student comprehension work * What productive failure has to do with complex text instruction * Why “access support” and “pre-reading substitution” are not the same thing * The risk of designing away the productive friction that reading requires Source: https://learnwithsap.org/literacy/keep-text-at-the-center-of-a-changing-world/

What Happens to Students When the Tool Is Gone

In this episode we explore what happens when students learn with AI or other cognitive supports, and then suddenly have to work without them. We look at a recent learning science study highlighted by Carl Hendrick in The Learning Dispatch, where students who had been using a simple planning tool saw their performance drop when that tool was removed.  Topics covered: * Why introducing an AI tool also means planning for its removal * The difference between scaffolds, accommodations, and persistent AI tools * What learning science suggests about tool dependence * Why “taking the tool away” can reveal whether students have actually built skill * Why teachers need control over when AI feedback is on, limited, or turned off * Why AI implementation should be judged not only by access and efficiency, but by whether students can still think and perform when the support is gone Sources: https://carlhendrick.substack.com/p/the-monthly-dispatch-whats-new-in-a9a https://link.springer.com/article/10.1186/s41235-026-00722-0