AI in the Classroom

In this episode we explore a new kind of objection emerging in classrooms: students and teachers questioning whether AI tools belong in school because of their environmental costs.

We look at real examples from classrooms, professional learning sessions, and student writing to unpack why this concern is gaining traction. We examine what educators should say when students raise ethical objections to AI, how past classroom opt-outs offer useful precedent, and why this issue may soon move from individual assignments to district procurement and policy decisions.

Topics covered:

• Why students and teachers are raising environmental objections to AI
• The energy, pollution, and health costs connected to data centers
• How AI’s environmental footprint is becoming a classroom conversation
• What teachers should do when students object to using AI
• Why district leaders may need to consider sustainability in AI procurement
• The limits of vendor sustainability claims and offsets
• How schools can take student ethical concerns seriously without overreacting

Sources:

https://www.edsurge.com/news/2025-08-14-as-data-centers-expand-should-that-concern-schools

https://marketbrief.edweek.org/education-market/ais-hidden-costs-what-k-12-needs-to-know-about-energy-and-infrastructure/2026/04

In this episode we explore how AI could reshape state assessment, and why that shift matters for teachers, coaches, and district leaders.W

We look at two recent pieces: Jane Swift’s argument that AI-enabled assessment can turn testing from an “autopsy” into a “compass,” and Transcend’s policy paper calling for a more careful bridge to the future of state assessment.

Topics covered:

• Why state assessment data often arrives too late to help instruction
• How AI could support more responsive classroom assessment
• The difference between state tests and formative classroom tools
• What teachers should expect from AI-enabled feedback systems
• Why PLCs matter in making assessment data useful
• The risk of adding more assessments instead of removing ineffective ones
• Why trust, student data privacy, and parent communication are central to AI assessment policy
• Why the future of assessment should be built at the classroom and PLC level, not just the policy level

Sources:

https://www.the74million.org/article/time-for-schools-to-stop-testing-kids-like-its-the-1990s-ai-can-show-the-way/

https://transcendeducation.org/wp-content/uploads/2023/04/Future-of-Assessment-Report_042726.pdf

In this episode we explore a question schools are only beginning to confront: how should students speak to AI?

As AI chatbots become more common in classrooms, the conversation often focuses on cheating, plagiarism, citation, and academic integrity. But there is another layer worth paying attention to: the interaction itself. When students talk to AI, are they respectful, dismissive, abusive, curious, aggressive, or overly dependent? And what might those interactions reveal about student behavior, classroom culture, and ethical development?

Topics covered:

• Why schools talk so much about AI cheating, but much less about AI interaction
• What student language toward chatbots may reveal about classroom behavior and emotional patterns
• Whether speaking abusively to AI can reinforce habits that carry into human relationships
• How AI interactions may become part of character formation in small, everyday moments
• Why teachers may need guidance for responding when students mistreat AI tools
• What districts should consider when creating AI use norms and classroom policies
• Why this issue belongs in broader conversations about AI literacy and digital citizenship

Source:

https://hbr.org/2022/04/how-to-manage-your-anger-at-work

In this episode we explore the rise of AI-powered coaching tools for teachers and what happens when feedback on classroom instruction starts coming from software instead of a human observer.

We look at the promise and risk of AI tools that analyze classroom video, generate feedback, and shape how teachers talk about their own practice.

Topics covered:

• How AI coaching tools are being used to analyze classroom instruction
• Why automated feedback for teachers parallels AI feedback for student writing
• The role of context in judging classroom decisions
• Why trust matters more than efficiency in teacher coaching
• The risk of AI narrowing the definition of “good teaching”
• What classroom teachers should ask when reviewing AI-generated feedback
• How instructional coaches and district leaders can use AI without replacing human judgment

Source:

https://www.tandfonline.com/doi/abs/10.1080/00405841.2025.2607939

In this episode we explore what happens when AI changes the basic conditions of student writing, and why some teachers are bringing pencils, notebooks, and in-class writing back into the center of English instruction.

Drawing on a recent New York Times piece about AI and student writing, we look at the growing move away from take-home essays and toward handwritten, in-class work. The real question may not be paper or AI. It may be something more.

Topics covered:

• Why teachers are rethinking take-home writing assignments in the age of AI
• What student AI use reveals about homework, drafting, and critical thinking
• How handwritten assessments can unintentionally measure the wrong things
• The difference between drafting, revising, and composing under time pressure
• Why AI should support the writing process without replacing student authorship
• What district leaders should consider before making “back to paper” policies
• A practical writing workflow that combines notebooks, rubrics, AI feedback, and student reflection
  

Sources:

https://www.nytimes.com/2026/04/30/us/ai-students-cheating-homework-classrooms.html

In this episode we explore a new Stanford study showing that AI writing feedback tools can respond differently to the same student work depending on demographic cues like race, gender, motivation level, English learner status, or learning disability status.

We break down why this matters for classrooms and districts, especially as schools adopt AI feedback tools that promise personalization. The central question: when does “personalized” feedback actually support students, and when does it quietly reinforce stereotypes?

Topics covered:

• What Stanford researchers found when they tested AI feedback on middle school writing
• Why some students received more praise while others received more substantive revision guidance
• The difference between encouragement and feedback that actually helps students grow
• Why demographic data can both help districts monitor equity and introduce new forms of bias
• How this differs from culturally responsive teaching
• What teachers can do to help students reflect critically on AI-generated feedback
• What instructional coaches can test inside their own schools
• What district leaders should ask vendors before approving AI writing tools

Sources:

https://arxiv.org/pdf/2603.12471

https://hechingerreport.org/proof-points-ai-bias-feedback/

In this episode we explore the promise, and the limits, of “closed loops” in AI-enhanced classrooms.

We talk about whether AI can really give teachers and school systems a complete picture of a student’s learning journey: where they are, what they need next, and how best to support them.

Topics covered:

• What “closed loop” means in AI-supported teaching and learning
• The appeal of real-time insight into student progress
• Why AI may work well for highly motivated students but miss many others
• The danger of mistaking niche AI learning models for universal solutions
• What technology can measure — and what only teachers can understand
• Why student motivation, context, and meaning-making still matter
• What district leaders should watch for when evaluating AI-driven personalization
• Why classroom relationships remain central, even in data-rich environments

In this episode we explore what Khanmigo’s uneven rollout reveals about AI tutoring, student motivation, and the limits of chatbot-based learning tools.

When students do not use an AI tutor, is that a student motivation problem, a product design problem, or a teaching design problem?

Topics covered:

• What Khanmigo’s rollout tells us about AI tutoring in real classrooms
• Why low student usage matters more than edtech hype
• The difference between a teaching problem and an interface problem
• Why students may not know how to ask AI tools for help
• How chatbot interfaces can increase cognitive load
• What teachers already understand about motivation, confidence, and relationships
• Why Socratic AI tools may not work for students who are already confused
• What district leaders should ask vendors about AI usability and evidence
  

Sources: 

https://danmeyer.substack.com/p/rip-khanmigo-and-edtech-industry

https://www.chalkbeat.org/2026/04/09/sal-khan-reflects-on-ai-in-schools-and-khanmigo/

https://www.oneusefulthing.org/p/claude-dispatch-and-the-power-of

In this episode we explore what happens when school districts begin using “vibe coding” to build their own software tools with generative AI.

We look at Peninsula School District in Washington State, where administrators are using AI coding tools to create internal apps and potentially save hundreds of thousands of dollars in EdTech contracts. We examine why this is promising, why it is risky, and what district leaders need to consider before AI-built tools touch student or staff data.

Topics covered:

• What “vibe coding” means in a K–12 school district context
• How Peninsula School District is using AI to build internal software tools
• Why districts may see AI-built apps as a way to reduce EdTech costs
• The cybersecurity risks of AI-generated code
• The risks of in-house tools that outlive the people who built them
• Questions district leaders should ask before deploying AI-built applications
• What teachers should ask when a district introduces an internally built AI tool

Source: 

https://www.k12dive.com/news/vibe-coding-helped-a-washington-district-save-250k-in-ed-tech-costs/816993/

In this first Teacher Tuesday episode of AI in the Classroom – Daily, we explore what AI looks like from inside a real elementary classroom.

Dan Cogan-Drew talks with educator Diana Betancourt of Neabsco Elementary School in Prince William County, Virginia, about how AI-supported writing tools are helping her students—especially multilingual learners and students with disabilities—build confidence, organize their ideas, and take greater ownership of the writing process.

Rather than treating AI as a shortcut or replacement for teaching, this conversation looks at how carefully designed AI feedback can support student thinking, reduce teacher bottlenecks, and create more opportunities for one-on-one instruction.

Topics covered:

• Why Diana became an educator and how her work with multilingual learners shapes her teaching
• What writing challenges look like for her students
• How AI feedback can help students move from ideas to organized writing
• Why confidence matters so much for English learners and students with disabilities
• How AI can support metacognition without giving students the answer
• What teachers should, and should not, expect AI to do in the classroom
• Why AI should amplify teacher judgment, not replace it
• What district leaders can learn from teachers experimenting thoughtfully with AI
• 
  

Source: 

https://wtop.com/prince-william-county/2026/03/how-a-prince-william-co-teacher-is-using-ai-to-offer-students-immediate-feedback/