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The Load-Bearing Layer: Why AI-Era Learning Design Is an Institutional Problem

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A teacher can ask students to show their thinking. But if the gradebook, pacing guide, policy language, parent communication, and schedule still reward only the polished product, that teacher is working against the system rather than with it. That is the institutional problem underneath generative AI in schools. The System Around the AssignmentFor the first […]

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When Guidance Lags Behind Use: Educator Sensemaking in the Age of AI

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When formal guidance is missing, AI policy does not disappear. It becomes a thousand classroom decisions. A teacher decides whether a student may use AI to understand assignment directions. Another decides whether AI-generated feedback supports revision or replaces it. A department debates whether brainstorming counts as assistance or authorship. A principal responds to families who

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Beyond Academic Integrity: AI Is a Learning-Design Problem

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// artificial intelligence // curriculum design Most school conversations about student AI use begin with a familiar question: Did the student use AI? Sometimes that question is necessary. Schools need expectations for attribution, disclosure, acceptable assistance, privacy, and responsible conduct. Students need to understand when using a tool violates the conditions of an assignment. Teachers

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Productive vs. Exclusionary Friction: Protecting Learning Without Creating Unnecessary Barriers

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One of the central problems generative AI creates for schools is that it changes the relationship between academic performance and learning process. A student can now produce a fluent summary, polished essay, organized explanation, or plausible argument without necessarily doing the interpretive and authorial work those products used to signal. That does not mean every

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Infographic showing a tiered decision-rights framework for AI in schools, ranging from AI-generated drafts to human-approved recommendations, limited automation, and actions that must remain human.

Tiered Decision Rights: Governing the Actions of Autonomous Systems

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Series Overview Designing with Friction: AI, Learning, and K–12 Leadership Generative AI is changing more than the tools students and educators use. It is changing the conditions under which work is produced, learning is demonstrated, data is remembered, and institutional decisions are made. This five-part series examines what K–12 leaders need to preserve, redesign, and

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Symbolic infographic showing persistent AI memory as a student data layer, with icons for privacy, retention, review, correction, deletion, and parent transparency.

The Compliance Ghost in the Machine: FERPA, COPPA, and Persistent AI Memory

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Series Overview Designing with Friction: AI, Learning, and K–12 Leadership Generative AI is changing more than the tools students and educators use. It is changing the conditions under which work is produced, learning is demonstrated, data is remembered, and institutional decisions are made. This five-part series examines what K–12 leaders need to preserve, redesign, and

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Symbolic infographic showing the shift from AI as a simple assistant to agentic AI systems that can plan, remember, access tools, and take action inside school workflows.

From Assistant to Actor: Navigating the K–12 Procurement Crisis in Agentic AI

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Series Overview  Designing with Friction: AI, Learning, and K–12 Leadership Generative AI is changing more than the tools students and educators use. It is changing the conditions under which work is produced, learning is demonstrated, data is remembered, and institutional decisions are made. This five-part series examines what K–12 leaders need to preserve, redesign, and

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Infographic showing a learning sequence in which students first read, think, draft, and explain before using AI for feedback, revision, or extension.

Writing Policy for the Learning Sequence: Moving Beyond Tool Permission

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Series OverviewDesigning with Friction: AI, Learning, and K–12 LeadershipGenerative AI is changing more than the tools students and educators use. It is changing the conditions under which work is produced, learning is demonstrated, data is remembered, and institutional decisions are made. This five-part series examines what K–12 leaders need to preserve, redesign, and govern as

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Symbolic infographic comparing polished AI-assisted student work with visible evidence of learning, showing that completion, fluency, and clean formatting do not prove understanding.

The Illusion of Completion: Why Flawless Student Work Can Signal Zero Learning

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Series Overview Designing with Friction: AI, Learning, and K–12 Leadership Generative AI is changing more than the tools students and educators use. It is changing the conditions under which work is produced, learning is demonstrated, data is remembered, and institutional decisions are made. This five-part series examines what K–12 leaders need to preserve, redesign, and

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From Paper to Play: Using AI to Bring Critical Digital Citizenship Frameworks to Life

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Before I begin, I want to make sure that I credit this idea to Ethan Mollick. Ethan Mollick is the Ralph J. Roberts Distinguished Faculty Scholar, Rowan Fellow, and Associate Professor at the Wharton School of the University of Pennsylvania, where he studies the effects of artificial intelligence on work, entrepreneurship, and education. He did a

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