AI in the Classroom

In this episode we explore whether AI translation tools are making language learning obsolete, and why the answer is more complicated than it first appears.

Using a recent essay from The Conversation as a starting point, we look at the difference between using AI for quick transactional communication and using language to build identity, relationships, and cultural understanding.

Topics covered:

• AI translation tools and the future of language learning
• The difference between transactional communication and identity-based communication
• Why learning another language is about more than exchanging information
• What this means for world language classrooms
• How teachers can design assignments that students are less likely to offload to AI
• Why personal investment, identity, and ownership matter in AI-era learning
• How educators can decide which parts of learning should stay human

Source:

https://theconversation.com/what-ai-earbuds-cant-replace-the-value-of-learning-another-language-264965

In this episode we explore the increasingly heated debate over technology in schools — from state-level efforts to restrict devices to the growing pressure on districts to adopt AI tools. Using recent arguments about EdTech, screen bans, handwriting, typing, and classroom technology, we look at why the real question is not simply “screens or no screens,” but what specific learning task students are being asked to do.

Topics covered:

• Why some policymakers are pushing broader school device bans
• The debate over whether EdTech harms student learning
• What handwriting vs. typing research actually suggests
• Why learning decisions should be task-specific, not technology-specific
• How blanket screen bans can affect students with learning differences
• The difference between banning cell phones and banning instructional tools
• What teachers should ask when device policies reach the classroom
• What district leaders should consider before adopting or restricting technology
• Why educators, not blanket policies, are best positioned to judge what learning requires
  

Source:

https://www.the74million.org/article/some-states-are-banning-much-more-than-phones-in-schools-thats-a-huge-mistake/

https://www.chalkbeat.org/2026/03/17/jared-cooney-horvath-says-ed-tech-hurts-learning-a-look-at-the-evidence/

In this episode we explore why Gen Z students are growing more skeptical of AI even as they continue using it at high rates. Drawing on new Gallup survey findings and recent conversations about “cognitive surrender,” we look at what student attitudes toward AI may be telling educators right now: not that students want AI banned, but that they want real help using it thoughtfully.

We also examine what this means for teachers, instructional coaches, and district leaders as schools try to respond to a generation that is both highly engaged with AI and increasingly uneasy about its effects on thinking, originality, and learning.

Topics covered:

• Gen Z’s rising anger and declining excitement about AI
• Why AI use is holding steady even as trust falls
• The connection between student skepticism and “cognitive surrender”
• What students are worried AI may be doing to their thinking
• Why schools should treat student attitudes toward AI as a teaching topic
• How teachers can surface skepticism productively in the classroom
• What instructional coaches should look for in AI-integrated lessons
• Why district messaging about AI may need to shift
• The gap between student need and school response
• What it would look like to help students use AI without hype

Source:

https://www.the74million.org/article/gen-z-increasingly-skeptical-of-and-angry-about-artificial-intelligence/

In this episode we explore what happens when AI writing feedback improves a paper without necessarily improving the writer. Using new research from the University of Pennsylvania on graduate students using an AI teaching assistant, we look at the difference between revision and real learning, why group feedback can mask individual understanding, and what teachers can do to make AI feedback more instructionally meaningful.

Topics covered:

• How AI feedback can lead to revision without deep learning
• What a University of Pennsylvania study reveals about student writing improvement
• Why group work can hide whether individual students actually learned
• The difference between fixing writing and understanding writing
• Why structured reflection should be part of any AI feedback workflow
• How teachers can help students process feedback more thoughtfully

• What district leaders should look for in AI writing-feedback tools
• The risk of overwhelming students with too much unprioritized feedback
• Why revision is evidence of compliance, not always evidence of understanding

Source:

https://learninganalytics.upenn.edu/ryanbaker/20_U%20Penn%20team%20-%20Do%20Students%20Learn%20from%20Writing%20Feedback%20from%20an%20AI%20Teaching%20Assistant.pdf

In this episode we explore the idea of “cognitive surrender” in classrooms: what happens when students or teachers rely on AI so quickly that they stop fully engaging in their own thinking. Drawing on a new preprint from researchers at Wharton, we examine how access to AI can improve accuracy when the tool is right, but worsen performance when it is wrong, especially under time pressure.

Topics covered:

• Cognitive surrender and what the research suggests
• Why time pressure increases reliance on AI
• Which students may be most vulnerable to overtrusting AI
• Why AI can make people feel more confident even when it is wrong
• The connection between AI use, motivation, and equity

• What teachers can do to design assignments that preserve student thinking

• Why district leaders should connect AI use to workload and staffing conditions
• The role of feedback and meaningful stakes in reducing overreliance on AI

Sources:

https://www.nytimes.com/2026/03/29/opinion/ai-claude-chatgpt-gemini-mcluhan.html

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646

In this episode we explore a powerful question for educators: what happens when AI doesn’t just support thinking, but starts to replace it?

Drawing on Ezra Klein’s reflections after spending time with people on the frontier of AI, we examine the difference between using AI as a tool for cognitive offloading and slipping into what researchers call cognitive surrender.

Topics covered:

• Ezra Klein’s argument that AI may be changing people, not just technology
• The difference between cognitive offloading and cognitive surrender
• Why polished AI output can conceal shallow or incomplete thinking
• What this means for student writing, reasoning, and identity formation
• How teachers can protect the moments where real thinking needs to happen
• Why assignment design matters more in an AI-rich environment
• How instructional coaches can use this conversation with faculty
• What district leaders should consider about the working conditions that make overreliance on AI more likely

Sources:

https://www.nytimes.com/2026/03/29/opinion/ai-claude-chatgpt-gemini-mcluhan.html

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646

In this episode we explore the rise of AI agents in education through the lens of Grammarly’s expanding vision for AI in the workplace and the classroom. We examine the idea that teachers might one day “build” agents that encode their expertise, then ask a harder question: what gets lost when teaching is reduced to a set of rules? We look at what this means for teacher expertise, student equity, and the future of classroom decision-making.

Topics covered:

• What AI agents are, and how they differ from chatbots
• Grammarly’s broader vision for agent-based work and learning
• Why the idea of “scaling teacher expertise” is both appealing and problematic
• The limits of rule-based systems in real classroom instruction
• Why pedagogical content knowledge cannot be easily encoded into an agent
• What Teachers Pay Teachers can teach us about quality and scalability in EdTech
• Why grammar instruction and history instruction are not the same kind of teaching problem
• Why equity concerns should be central when schools evaluate AI agents
• Three key questions educators and district leaders should ask before adopting agent-based tools

Sources:

https://www.theverge.com/podcast/898715/superhuman-grammarly-expert-review-shishir-mehrotra-interview-ai-impersonation

https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025

In this episode we explore new research on bias in AI writing feedback and what it means for teachers, school leaders, and anyone evaluating AI-powered writing tools in K–12 education. We look at a Stanford preprint on how large language models responded differently to the same student essay when only the attached demographic profile changed, and we connect that to earlier MIT research showing that AI systems can shift tone and quality based on who they think they are talking to. The bigger question: when AI claims to “personalize” feedback, is it actually supporting equity, or quietly automating lower expectations?

Topics covered:

• How a Stanford research team tested AI writing feedback using 600 real eighth-grade persuasive essays
• Why changing only demographic labels changed the feedback students received
• What “positive feedback bias” and “feedback withholding bias” can look like in classroom practice
• How AI can give more praise but less useful critique to some students
• What the earlier MIT chatbot study revealed about tone, condescension, and perceived vulnerability
• Why AI “personalization” can slip into profiling
• What teachers should ask before trusting AI-generated writing feedback
• How students can be taught to question, audit, and respond critically to AI feedback
• What curriculum leaders and district leaders should demand from vendors about inputs, transparency, and equity testing

Source:

https://arxiv.org/pdf/2603.12471

In this episode we explore what AI can and cannot do when it comes to helping young people prepare for hard conversations. Starting with a story about Gen Z workers using ChatGPT to rehearse salary negotiations and workplace conflict, we contrast that with the “spirit of the game” in ultimate Frisbee, where players are expected to resolve disagreements themselves in real time.

From there, we ask a bigger question for schools: if students need AI to simulate difficult conversations, what does that reveal about the kinds of learning experiences we are — and are not — designing for them?

Topics covered:

• How Gen Z is using ChatGPT to rehearse difficult conversations
• What ultimate Frisbee’s “spirit of the game” can teach us about school design
• Why schools may not be giving students enough practice with real disagreement
• The difference between AI role-play and real interpersonal fluency
• What classroom teachers should ask before using AI conversation tools
• What district leaders should consider when evaluating these tools
• Why civility and conflict resolution have to be designed into learning environments
• The risks of relying on AI before students have authentic practice in real human situations

We explore the question, what should schools protect and prioritize that AI can never replace?

We focus on the idea of the human core, including emotional intelligence, interpersonal connection, ethical judgment, and wellbeing. We also examine what happens when AI takes on more instructional and cognitive tasks, and whether schools are at risk of sidelining the very relationships that matter most for learning.

Topics:

• The Transcend Education framework for holistic student outcomes
• What the report means by the “human core”
• Why relationships are central to learning, not separate from it
• How AI could either strengthen or weaken teacher-student connection
• The risk of personalized learning becoming isolating
• Why schools need to make empathy, ethical judgment, and wellbeing explicit goals
• A practical question school leaders should ask when evaluating AI tools
• Why time saved by AI should be redirected toward deeper human work

Source:

https://transcendeducation.org/wp-content/uploads/2026/01/Overdeck-AI-Resource_FINAL_012026.pdf