In this episode we explore what it means to be literate in a world where algorithms increasingly decide what information students see, and what they never encounter.

We examine the OECD’s new Media and Artificial Intelligence Literacy framework, known as MAIL, and its argument that AI literacy must extend beyond evaluating whether a piece of content is accurate.

Topics covered:

• The shift from using AI tools to living inside AI-mediated environments
• The OECD’s Media and Artificial Intelligence Literacy framework
• PISA’s three foundational ideas: authors and audiences, messages and meanings, and representations and realities
• How platforms are designed to capture and monetize attention
• The effects of algorithmic personalization, echo chambers, and filter bubbles
• Why students cannot always see which perspectives have been filtered out
• The values and biases embedded in AI training data and system design
• Why AI literacy should include asking, “Why did I see this?”
• What the planned 2029 PISA assessment could mean for teachers, schools, and education systems
  

Source:

https://www.oecd.org/content/dam/oecd/en/about/projects/edu/the-pisa-2029-media-and-artificial-intelligence-(mail)-assessment-will-shed-light-on-whether-young-students-have-had-opportunities-to-learn-and-to-engage-proactively-and-critically-in-a-world-where-production,-participation,-and-social-networking-are-increasingly-mediated-by-digital-and-ai-tools-/PISA%202029%20MAIL%20Assessment%20Framework%20First%20Draft.pdf

In this episode we explore a growing problem for school districts: how do you govern AI use when you cannot clearly see where or how it is already being used?

Looking at recent policy moves in New York City, Fairfax County Public Schools, and Katy ISD, we examine the gap between official AI rules and the reality inside schools. 

Topics covered:

• Why districts often lack a complete inventory of AI tools
• New AI restrictions in New York City, Fairfax County, and Katy ISD
• Why defining “AI” is harder than it sounds
• The limits of bans, firewalls, and approved-tool lists
• The trade-off between surveillance and teacher autonomy
• The risks of policies that districts cannot consistently enforce
• Why AI governance is now more urgent than AI adoption
• What the forthcoming student-led AI Bill of Rights for Schools could add to the debate

Sources:

https://www.houstonchronicle.com/neighborhood/katy/article/katy-isd-ai-policy-generative-ai-students-22357245.php

https://www.the74million.org/article/students-convene-to-hammer-out-ai-bill-of-rights-for-schools/

https://www.ffxnow.com/2026/07/23/fairfax-county-school-board-approves-restrictions-on-generative-ai-digital-devices/

In this episode we explore why AI tutors may be improving student performance without producing the deeper, transferable learning schools actually want.

Topics covered:

• Why strong tutoring results may not translate beyond the tutoring environment• The difference between task completion and genuine transfer• What brain-training research can teach us about AI tutors• Why current assessment systems shape what AI tools optimize for• What teachers and district leaders want from next-generation assessment• How multimodal AI could capture student reasoning and problem-solving• Why changing assessment may be the key to improving AI tutoring

Sources:

https://thedigitaldelusion.substack.com/p/are-ai-tutors-really-teaching-students

https://www.gettingsmart.com/2026/07/17/weve-been-judging-symphony-rehearsals-by-the-last-note-why-multimodal-ai-can-finally-measure-mathematical-practice/

https://marketbrief.edweek.org/meeting-district-needs/whats-next-for-k-12-assessment-the-demand-for-innovation-in-k-12-testing/2026/06

In this episode we explore whether schools are actually falling behind on AI—or whether educators are adopting the technology faster than headlines suggest.

Topics covered:

• How teacher AI adoption compares with other professions
• Why experimentation does not always lead to regular, confident use
• The training and access barriers facing educators
• What students misunderstand about AI and truthfulness
• Why AI literacy matters for student health and safety decisions
• The difference between moving fast and building sustainable infrastructure

Sources:

https://www.gallup.com/699797/indicator-artificial-intelligence.aspx

https://www.sciencedirect.com/science/article/pii/S2666920X25001043

https://www.k12dive.com/news/ai-is-moving-quickly-how-can-districts-keep-up/816265/

In this episode we explore how teachers are moving beyond restrictive AI policies and building classroom approaches rooted in trust, transparency, and student learning.

Topics covered:

• Why classroom AI policies are shifting from restriction to transparency
• How disclosure can create opportunities for teaching and reflection
• The value of including student voices in policy design
• Why AI use should be understood as a spectrum rather than a yes-or-no question
• How teachers can explicitly teach responsible AI decision-making
• Why classroom policies need to evolve throughout the school year
• How educators can model the transparency they expect from students
• The difference between designing for compliance and designing for integrity

Sources:

https://www.edutopia.org/article/8-classroom-ai-policies-developed-by-teachers/

https://www.edutopia.org/article/ai-transparency-surveys-tool-navigating-student-choices-together

https://wpvip.edutopia.org/wp-content/uploads/2026/07/Class-AI-Agreement_Stacy-Kratochvil.pdf

In this episode we explore why two recent studies—one showing AI weakening students’ math performance and another showing AI-enhanced feedback improving writing—may actually point to the same conclusion: the impact of AI depends on how learning is designed.

Topics covered:

• What research reveals about AI use and declining math performance
• How combining peer and AI feedback can strengthen student writing
• The difference between receiving an answer and exercising judgment
• Why AI exposes assignments built around task completion
• Understanding by Design and the role of backward planning
• How to identify meaningful transfer goals
• Designing writing tasks around audience, purpose, and lived experience
• Creating math assessments that measure genuine problem-solving
• Why stronger assignment design may be more effective than banning AI

Sources:

https://arxiv.org/pdf/2605.21629

https://hechingerreport.org/proof-points-ai-eroding-math-skills/

https://www.sciencedirect.com/science/article/pii/S8755461525000088

In this episode we explore three stories that reveal where AI in education actually stands, not where the industry says it is headed, but what schools are dealing with right now.

Topics covered:

• Why Claude for Teachers raised student-data and FERPA concerns• Why district governance must come before teacher adoption• What the collapse of InBloom can teach today’s AI companies• How AI data centers are affecting laptop and Chromebook prices• Why some schools are pulling back from one-to-one devices• Y Combinator’s vision for AI tutors• The influence of The Diamond Age and “The Primer”

Sources:

https://drjoephillips.substack.com/p/anthropic-launched-claude-for-teachers?r=5qyht

https://www.idahoednews.org/top-news/schools-face-soaring-laptop-costs-as-ai-data-centers-squeeze-supply/

https://www.nytimes.com/2026/03/29/technology/chromebook-remorse-kansas-school-laptops.html 

In this episode we speak with Jim Bentley, a fifth- and sixth-grade teacher in Elk Grove, California, who is entering his 31st year in the classroom. We examine how Jim has begun using AI with students. 

Topics covered:

• The classroom as a laboratory for curiosity, experimentation, and productive mistakes
• How project-based learning shaped Jim’s teaching philosophy
• An AI image-generation project connected to Raymie Nightingale by Kate DiCamillo
• Prompt writing, revision, and healthy skepticism toward AI-generated results
• Media literacy, lateral reading, and fact-checking AI responses
• The difference between using AI as a productivity tool and using it as a learning tool
• What it means for students to do “real work,” not merely “schoolwork”
• Rubric design, teacher judgment, and the risk of overvaluing polished final products

In this episode, part 4 of our series on the productive struggle for teachers, we explore a growing challenge in teacher preparation: how can supervisors tell whether a teacher candidate used AI as a thinking partner, or simply submitted a polished AI-generated lesson plan?

In this episode we explore:

• Why AI can make a teacher candidate’s thinking invisible
• The difference between thoughtful AI-assisted planning and minimal revision
• Why teacher preparation programs should grade decision logs, not just lesson plans
• How supervisors can identify genuine pedagogical reasoning
• Which parts of lesson design should remain productive struggles
• How AI can remove unnecessary friction without replacing professional judgment

In this episode, part 3 of our series on the productive struggle for teachers, we explore how AI is shaping the habits of early-career teachers, and why the choices teachers make in their first five years could influence the rest of their careers.

Topics covered:

• Why early-career teachers may be especially vulnerable to passive AI use• How generic AI-generated materials can affect student engagement• The connection between teacher confidence, burnout, and retention• Why productive struggle matters when learning to teach• The importance of revising and personalizing AI-generated lessons• Why protected reflection time should be part of AI implementation• How early-career teacher support affects equity and the long-term teacher pipeline