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

AI Detectors, Teacher Training, and the Real Cheating Problem

In this episode, we cover three timely AI-in-education stories that all point to the same challenge: schools are moving fast, but the systems around AI policy, teacher training, and academic integrity are still catching up. Topics Covered * Why Wake County is moving to ban AI detectors in schools * The case of Eleanor Canina and the risks of false AI accusations * Why AI detection tools may unfairly flag multilingual and neurodivergent students * What Microsoft’s 2026 AI in Education report says about teacher AI use * Why many educators are using AI without formal training * Academic integrity as a top concern for both teachers and students * New survey data on cheating among Harvard seniors * A hopeful student perspective on AI cheating Sources: https://www.wral.com/news/education/whats-in-wake-schools-new-ai-policy-draft-june-2026/ https://news.microsoft.com/source/2026/06/24/microsofts-new-ai-in-education-report-highlights-widespread-adoption-and-increasing-demand-for-support/ https://fortune.com/2026/06/23/harvard-cheating-academic-integrity-ai-detection/

What Eye-Tracking Research Shows About Spotting AI Deepfakes

In this episode we explore how students learn to spot AI-generated deepfakes, and why confidence may be one of the biggest risks in AI literacy. We look at a new study from researchers in Germany that used eye-tracking technology to examine where students look when trying to decide whether an image is real or AI-generated. Topics covered: * How students visually inspect AI-generated images * What eye-tracking research reveals about deepfake detection * The difference between gut-level pattern recognition and systematic scanning * Why AI-generated images often fail at the edges * How critical thinking instruction can change students’ attention habits * Why students may overestimate their ability to spot deepfakes * The limits of one-off AI literacy lessons Sources: https://www.sciencedirect.com/science/article/pii/S0360131525002982

Trust, AI, and the Classroom Relationship

In this episode we explore how AI is changing one of the most important relationships in school: the trust between teachers and students. We look at emerging research on AI transparency, student surveillance, false accusations, AI detection tools, and the disproportionate risks for English learners and students with disabilities. Topics covered: * How AI is changing trust between teachers and students * Why students may feel surveilled rather than supported * The problem of one-way AI transparency in schools * Why AI bans may increase suspicion instead of reducing it * The risks of AI detection tools and false accusations * How teacher AI use affects student perceptions of authenticity * Practical alternatives to detection-first classroom policies Sources: https://www.the74million.org/article/another-ai-side-effect-erosion-of-student-teacher-trust/

AI Cheating Just Got Much Harder to Catch

In this episode we explore the rise of AI cheating tools, and what they reveal about the future of writing instruction. We break down a recent New York Times investigation into “humanizers” and “autotypers,” tools designed to make AI-generated student writing harder to detect. But the episode goes beyond academic integrity panic. The real question is what teachers and school leaders can still do when digital evidence of authorship becomes unreliable. Topics covered: * What AI “humanizers” do to rewrite machine-generated text * How “autotypers” fake drafting, pauses, corrections, and revision history * Why Google Docs version history is becoming less reliable as proof of authorship * The blurred line between cheating tools and “AI help” tools * Why Grammarly, GPTZero, and similar platforms raise complicated ethical questions * What teachers can do when detection no longer works Sources: https://www.nytimes.com/2026/06/18/us/ai-apps-students-cheat.html

Practice the Way You'll Play

In this episode we explore what classrooms can learn from athletics and the performing arts about practice, performance, and real learning in the age of AI. Topics covered: * Why exams often reveal gaps between AI-assisted performance and independent learning * What music, athletics, and theater can teach academic classrooms * Transfer-appropriate processing and why practice conditions matter * The difference between practicing for a test and building independent capacity * How AI can support learning without substituting for student thinking Sources: https://www.semanticscholar.org/paper/Levels-of-processing-versus-transfer-appropriate-Morris-Bransford/838410627202df92a6cf7b45277e1368aefbe98a https://www.sarahshihuiwong.com/post/think-first-chatgpt-later-wong-qiu-2026-educational-psychology-review\ https://www.dailycal.org/news/campus/academics/failing-grades-soar-as-professors-see-greater-ai-usage-dwindling-math-skills-in-uc-berkeley/article_16fad0bf-02cb-4b8c-8d88-888ffd9f8608.html