AI Is Taking Root in Coaching. What's the Comparison Group?

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

The Problem With Branding a School Around AI

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