AI in the Classroom

In this episode we explore why sequencing matters when AI enters reading instruction. Drawing on Student Achievement Partners’ argument to “keep text at the center,” we look at a practical question for teachers and district leaders: does AI arrive before students have wrestled with a complex text, or after they have made their own first attempt?

Topics covered:

• Why “keeping text at the center” depends on instructional sequence
• The difference between AI as a reading scaffold and AI as a reading substitute
• How pre-reading summaries can short-circuit student comprehension work
• What productive failure has to do with complex text instruction
• Why “access support” and “pre-reading substitution” are not the same thing
• The risk of designing away the productive friction that reading requires
  

Source:

https://learnwithsap.org/literacy/keep-text-at-the-center-of-a-changing-world/

In this episode we explore what happens when students learn with AI or other cognitive supports, and then suddenly have to work without them.

We look at a recent learning science study highlighted by Carl Hendrick in The Learning Dispatch, where students who had been using a simple planning tool saw their performance drop when that tool was removed. 

Topics covered:

• Why introducing an AI tool also means planning for its removal
• The difference between scaffolds, accommodations, and persistent AI tools
• What learning science suggests about tool dependence
• Why “taking the tool away” can reveal whether students have actually built skill
• Why teachers need control over when AI feedback is on, limited, or turned off
• Why AI implementation should be judged not only by access and efficiency, but by whether students can still think and perform when the support is gone
  

Sources:

https://carlhendrick.substack.com/p/the-monthly-dispatch-whats-new-in-a9a

https://link.springer.com/article/10.1186/s41235-026-00722-0

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

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

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

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