When AI's Plausible Comparisons Reach the Classroom

What a New Study Tells Us About AI and Learning

In this episode we explore a simple question for classrooms using AI: Should students think first and use ChatGPT later? We connect research to a seventh-grade poetry classroom where students drafted on paper before using AI-supported tools. Topics covered: * Why “better work with AI” is not the same as learning * The difference between performance and transferable skill * What happens when students go straight to ChatGPT for answers * Why “think first, AI later” may be a stronger classroom model * How teachers can structure AI use without banning it * Why assignment sequence matters as much as assignment policy * The risks of AI-assisted brainstorming at the very start of student work Sources: https://carlhendrick.substack.com/p/the-monthly-dispatch-whats-new-in-a9a  https://link.springer.com/article/10.1007/s10648-026-10118-7

The Friction That Teaches

In this episode we explore how AI tools can either preserve or undermine one of the most important parts of learning: productive struggle. We look at Student Achievement Partners’ recent piece on AI in literacy and math classrooms, with a focus on the caution that students may lose essential learning opportunities when AI does the thinking too early. Topics covered: * What Student Achievement Partners warns about AI in literacy and math classrooms * Why productive struggle matters for student learning * Manu Kapur’s research on productive failure * The difference between cognitive surrender and skipped thinking * How AI can either extend or eliminate learning effort * What teachers should look for when AI enters an assignment * How instructional coaches can observe AI use in classrooms * What district leaders should ask before approving AI tools Sources: https://learnwithsap.org/cross-content/ai-is-so-hot-right-now-opportunities-and-cautions-in-literacy-and-math-classrooms/ https://arch.kuleuven.be/studeren/tall/artikels/productive-failure-kapur.pdf/@@download/file/Productive%20Failure%20Kapur.pdf https://www.tandfonline.com/doi/abs/10.1080/07370000802212669 https://www.tandfonline.com/doi/full/10.1080/00461520.2016.1155457 https://bellwether.org/publications/productive-struggle/?activeTab=1

Is This the Better Way to Prove Student Authorship?

In this episode we explore a practical classroom response to one of the hardest problems AI has created for teachers: how to know whether a student actually understands the work they submitted. Rather than relying on unreliable AI detectors, we look at an emerging approach from educators like AJ Giuliani: using a student’s submitted paper to generate a short comprehension quiz that helps reveal whether the student truly knows the material. Topics covered: * Why AI detectors are often unreliable for judging student authorship * How quiz-based authentication can help teachers assess whether students know the work they submitted * AJ Giuliani’s classroom approach to AI-era authorship questions * Nick Potkolitsky’s distinction between “ownership” and “formation” * Why retention, transfer, and agency matter in student learning * What “generate before you delegate” means for student writing * How oral explanation, reflection, and defense of ideas can strengthen assessment * Why classroom bots may be useful for probing student understanding, not just catching misuse * Practical implications for teachers, instructional coaches, and district leaders Sources: https://ajjuliani.beehiiv.com/p/the-easiest-way-to-stop-ai-plagiarizing https://nickpotkalitsky.substack.com/p/is-ownership-the-best-metaphor-for

When the University Goes All In

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

Students Need to Think About Their Thinking

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