AI in the Classroom

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

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/

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

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

In this episode we explore what happens when students appear to be participating, but may actually be outsourcing their thinking to AI.

We look at a recent UC Berkeley story about unusually high failure rates in introductory computer science courses, alongside reporting from The New York Times about students using tools like Gemini and ChatGPT during class discussion. 

Topics covered:

• How AI can create a gap between visible participation and actual thinking
• What the UC Berkeley computer science failure-rate story may reveal about AI-assisted learning
• The difference between completing work with AI and developing transferable skill
• Why “cheating” may be too narrow a frame for what schools are seeing
• What classroom teachers can look for when students appear engaged
• The risk of discovering learning gaps only when students sit for an assessment
  

Sources:

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

https://www.nytimes.com/2026/06/17/podcasts/the-daily/battle-over-ai-in-school.html

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

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

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

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

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