In this episode we explore California State University’s large-scale AI licensing deal with OpenAI, and what it reveals about the tension between institutional policy, classroom practice, and pedagogical purpose.

CSU set out to become the nation’s first “AI-powered university system,” offering ChatGPT enterprise access across its campuses. But a year later, many licenses went unused, faculty pushed back, and the deeper question became clear: providing access to AI is not the same as knowing what AI is for in teaching and learning.

Topics covered:

• CSU’s $17 million OpenAI agreement and what happened after rollout
• Why enterprise AI access can be an administrative decision, but classroom AI use is a pedagogical one
• Faculty resistance, unused licenses, and the limits of top-down implementation
• The difference between AI as institutional branding and AI as a teaching tool
• What K–12 leaders can learn from CSU’s rollout
• How districts should think about AI licenses, adoption, teacher agency, and purpose
• The risk of confusing workforce preparation with meaningful learning

Source:

https://www.nytimes.com/2026/06/01/magazine/ai-university-college-california.html

In this episode we explore what effective feedback looks like in AI-assisted classrooms, and why the design of that feedback matters more than the novelty of the tool.

Topics covered:

• Why many teachers are still receiving little guidance on classroom AI use
• What new research suggests about metacognitive feedback and student learning
• The difference between feedback that supports transfer and feedback that simply encourages
• How teachers can design AI feedback that prompts student reflection
• Why custom teacher-built chatbots need clearer instructional guardrails
• What instructional coaches should ask when evaluating AI feedback tools
• Why district leaders should make feedback behavior part of AI procurement and professional development
• The risk of AI tools optimizing for engagement instead of learning

Sources:

https://news.gallup.com/poll/710534/teachers-receive-no-formal-guidance.aspx

https://www.nature.com/articles/s41539-025-00311-8

In this episode we explore the debate around a Georgia school marketed as the nation’s first AI-themed high school, and what its story reveals about the gap between AI branding and actual classroom practice.

Topics covered:

• The Seckinger High School debate and the risks of branding a school around AI
• Larry Cuban’s “oversold and underused” pattern in education technology
• Why AI may create a harder accountability problem than earlier classroom technologies
• The difference between durable skills and actual AI integration
• When AI speeds up learning versus when it short-circuits important thinking
• The limits of student self-awareness when using AI tools
• What district leaders should ask before making AI part of a school’s identity
• Why good teaching may matter more than the AI label attached to a school

Sources:

https://www.nytimes.com/2026/05/30/opinion/ai-high-school.html

In this episode we explore a live “AI usage teardown” of a reflective tool created by educator Amelia King, designed to help students think more clearly about how they are using AI in academic work.

Topics covered:

• How students can reflect on whether their work is genuinely their own
• The difference between AI assistance and cognitive surrender
• Why “how much AI did you use?” is often harder to answer than it seems
• What teachers should notice about student ownership, comprehension, and verification
• Why the timing of AI use matters in the writing and thinking process
• How reflective tools can help restore student agency

Sources:

https://poe.com/AI_Usage_Review_2.0

https://www.amelia-king.com/about 

In this episode, we explore a fascinating (and counterintuitive) finding from a large-scale study of AI writing tools in Brazilian high schools: adding human graders to AI feedback didn’t improve student outcomes—but that’s not the real story.

We challenge the common narrative about AI “doing the teaching” and instead ask a more important question: what happens when AI handles the routine work so teachers can focus on what matters most?

In this episode, we explore:

• How an AI writing tool improved student outcomes—even without human graders
• Why the biggest gains showed up in complex thinking, not surface-level writing
• The concept of “task reallocation” and why it matters more than automation
• How teachers used AI to shift toward more meaningful one-on-one conversations
• Why more writing + more feedback didn’t require more teacher time
• The critical role of sequencing
• Why the lowest-performing students saw no benefit
• The limits of AI as a “last mile” solution for feedback
• What educators should really ask before adopting any AI tool

Source:

https://www.povertyactionlab.org/sites/default/files/research-paper/working-paper_5806_Artificial-Intelligence-Teacher-Tasks-Pedagogy_Brazil_Feb2021_0.pdf

In this episode we explore the idea of cognitive surrender in AI education: what happens when students, teachers, or professionals begin relying on AI not just for support, but for the thinking itself.

How do we use AI as a thinking partner without gradually losing our own voice, judgment, and generative ability?

Topics covered:

• What “cognitive surrender” means in the context of AI and education
• Why AI dependence is harder to detect than plagiarism
• The difference between recognizing your thinking and producing it independently
• How AI can subtly reshape voice, judgment, and creative work
• What teachers should consider before using AI for lesson plans, feedback, or writing support
• Why districts should pay attention to the defaults being set by AI tools
• How schools can encourage intentional AI use rather than reflexive dependence

Source:

https://www.oneusefulthing.org/p/choosing-to-stay-human

In this episode we explore a sharper way to think about AI in literacy instruction: not as a shortcut that makes texts easier for students, but as a teacher’s assistant that helps educators identify where complex texts may challenge readers.

Topics covered:

• Tim Shanahan’s argument for using AI as a teacher-facing tool
• How AI can identify complex sentences and vocabulary in classroom texts
• The difference between useful AI analysis and noisy or overly technical output
• Why teacher judgment still matters when interpreting AI suggestions
• What AI handles well in text analysis — and where it struggles
• Why text complexity is more than a single readability score
• What this means for teachers choosing and teaching complex texts
  

Sources:

https://www.shanahanonliteracy.com/blog/ai-and-the-teaching-of-reading-comprehension-part-2

https://app.newsela.com/view/cmlldktks00083b6qi19cz2vu/?levelId=cmlldktor0kt708ncdns10m6r&activityPanel=annotations

In this episode we explore what eighth-grade students in California are asking about AI. We reflect on a recent classroom visit and use students’ own questions to examine what young people already understand about AI, what they are unsure about, and why their concerns about fairness, cheating, fake work, and feedback matter for educators and district leaders.

Topics covered:

• How students are experiencing AI feedback in real classrooms
• The limits and risks of AI detection tools
• What student questions reveal about AI literacy
• Why transparency matters when AI gives feedback
• How AI use can affect students’ sense of fairness
• What teachers and districts should consider before adopting AI tools
• Why process, not just final writing products, matters in assessment

In this episode we explore the debate around Khanmigo, Khan Academy’s AI tutor, and what its struggles reveal about the difference between explaining content and actually supporting learning.

Topics covered:

• Why Khanmigo was described as “a non-event for most students”
• The difference between a great explainer and a true tutor
• How Sal Khan learns
• Why students often need more than prompts and explanations
• What AI tutoring tools may misunderstand about motivation and learning
• How teachers can evaluate whether an AI tool supports real student thinking
• What instructional coaches should help teachers notice about AI products
• What district leaders should ask vendors before buying AI tutoring tools

Sources:

https://punyamishra.com/2026/04/16/why-sal-khant-on-learning-by-making-but-teaching-by-telling/

https://substack.com/inbox/post/197857852

In this episode we examine a published analysis comparing civilian casualties at Gettysburg with civilian casualties in Gaza in 2024, and explain why the comparison is historically incoherent. 

Topics covered:

• Why AI-generated comparisons can sound plausible while being misleading
• The difference between factual accuracy and content-area coherence
• What the Gettysburg/Gaza analogy gets wrong
• Why teacher subject-matter knowledge is essential in an AI-rich classroom
• How AI-generated “slop” can pass through editorial and instructional review
• What classroom teachers should look for before using AI-generated materials
• Why instructional coaches need content-area reviewers
• What district leaders should ask vendors about educator review processes

Sources:

https://www.techlearning.com/technology/ai/in-an-ai-classroom-content-knowledge-matters-more-than-ever

https://www.edsurge.com/news/2026-01-12-ai-is-changing-classrooms-teacher-expertise-still-sets-the-direction