› Free · print-ready · 14 lessons · grades 3–12
Full classroom lesson plans for topical debates and discussions about AI — homework bots, deepfakes, machine art, jobs, surveillance, regulation — written for the age in the room. Each one is a single PDF: teacher background, vocabulary from the field guide, a timed agenda, two positions with starter arguments, discussion questions, roles, a rubric, and photocopy-ready student handouts.
Free to photocopy for classrooms, co-ops, and homeschool tables. No email, no login. Pick your grade band below.
These lessons are one piece of a wider free path for underfunded schools — see the whole free library.
› What's in every pack
Plain-language framing, the traps, and where people actually argue — no bluffing.
Terms pulled live from the AI concepts field guide so the glossary never drifts.
Warm-up to exit ticket, minute by minute, for a single class period.
Assigned sides with starter arguments written at the band's reading level.
Opening / core / closing questions, discussion roles, and a meets-looks-like rubric.
Background sheet, position organizer, sentence starters, and exit ticket — one per student.
› Grades 3–5 · ages 8–11 · 4 lessons
The homework helper is real. Is using it cheating, learning, or something in between?
Students meet the idea that an AI can write answers that look right but aren't, and argue about when it is okay to ask a machine for help. Four corners keeps eight-year-olds moving while they practice giving one reason for a view.
Vocabulary: Artificial intelligence (AI) · Hallucination · Verification · Prompt
It remembers your name and says nice things. Does that make it a friend?
A gentle first pass at the big questions — does a chatbot feel anything, and what makes a friend a friend? Philosophical chairs lets kids switch sides as their thinking moves, which is exactly what we want them to notice.
Vocabulary: Large language model (LLM) · Consciousness (in machines) · Privacy (when using AI) · Artificial intelligence (AI)
The sorting robot only ever saw dogs. Now show it a cat.
Students discover that AI learns from examples, then argue about whether a machine trained on lopsided examples can ever be fair. A hands-on card sort makes bias concrete before the first mini-debate of the year.
Vocabulary: Training data · Bias (in data and models) · Artificial intelligence (AI) · Human in the loop
Autoplay isn't magic. Somebody built it — and it has a goal.
Recommendation systems are the AI kids meet most. Students figure out what the app is optimizing for, argue whether autoplay should exist for kids, and leave with a rule for their own screens.
Vocabulary: Automation · Training data · Privacy (when using AI) · Automation bias
› Grades 6–8 · ages 11–14 · 5 lessons
Everyone is already using it. The question is what the rule should be.
The debate every middle school is having, run properly: assigned sides, evidence required, and a closing exercise that turns the argument into an actual classroom policy. Students learn how the tools fail before arguing about how they should be used.
Vocabulary: Large language model (LLM) · Hallucination · Verification · Automation bias · Prompt
It won the fair. Then they found out how it was made.
A Socratic seminar on what makes something art and who deserves credit when a model trained on millions of human images produces something new. Copyright and consent enter through the side door — the artists whose work trained the model.
Vocabulary: Generative AI · Training data · Copyright & training · Consent (data use) · Prompt
The résumé screener downgraded anyone who played women's chess. Nobody told it to.
A fishbowl discussion on automated decision-making, with real cases students can reason about. Half the class talks while the other half tracks claims and evidence, then they swap. The concepts — bias, explainability, human in the loop — come from what went wrong in the cases.
Vocabulary: Bias (in data and models) · Explainability · Human in the loop · Training data · Automation bias
It looks like her. It sounds like her. She never said it.
A structured debate on synthetic media that starts from consent and privacy and runs into free expression and enforcement. Students learn what a deepfake is technically, why detection is losing, and why 'just ban it' is harder than it sounds.
Vocabulary: Deepfake · Consent (data use) · Privacy (when using AI) · AI regulation · Verification
It suggested your cousin's best friend. You'd never met. How did it know?
Philosophical chairs on social graphs: apps that suggest friends, guess things about you from your connections, and catch fake accounts by the company they keep. Three real cases, a seat-switching debate, and one rule each pair writes for how apps may use who you know.
Vocabulary: Graph (nodes and edges) · Social graph · Community detection · PageRank / centrality · Privacy (when using AI) · Consent (data use)
› Grades 9–12 · ages 14–18 · 5 lessons
Every generation was told the machines would take the work. Sometimes they did.
An evidence-driven debate on labor displacement that forces students to define 'good job,' weigh historical precedent against what is different now, and confront the question personally: what should a sixteen-year-old do about it?
Vocabulary: Labor displacement & augmentation · Automation · Copilot · AI agent · Inference cost
The model read every book. Nobody asked the authors.
A policy debate on training data and copyright, with the affirmative proposing a licensing plan and the negative attacking it or offering a counter-plan. Students meet fair use, the open-weights question, and the practical problem that the training already happened.
Vocabulary: Training data · Copyright & training · Consent (data use) · Open weights / open models · Closed / proprietary model · Moat (competitive advantage)
It passed the bar exam. Does it know what a law is?
A Socratic seminar on the oldest AI question with the newest evidence. Students work from short definitions of intelligence, consciousness, and stochastic prediction, and are pushed to say what 'understand' would even mean — and whether the answer changes anything about how we should use the tools.
Vocabulary: Large language model (LLM) · Stochastic / nondeterministic · Intelligence (what we mean) · Consciousness (in machines) · AGI (artificial general intelligence) · Benchmark
The people building it signed a letter saying it might be dangerous. Then they kept building.
A policy debate on frontier-model regulation that takes both catastrophic and everyday risk seriously, and then asks whether a license regime would reduce either. Students encounter alignment, red-teaming, and regulatory capture, and must weigh a plan against the world without it.
Vocabulary: AI regulation · Existential risk (x-risk) · Alignment · Red teaming · Open weights / open models · Moat (competitive advantage)
The software flagged her essay about a novel as a threat. It also caught a real one.
Philosophical chairs on AI surveillance in schools, where students are the surveilled. Real trade-offs — the monitoring has flagged real crises and also false positives that outed students or punished creative writing — with privacy, bias, and consent as the vocabulary and seat-switching as the visible evidence of thinking.
Vocabulary: Surveillance AI · Privacy (when using AI) · Bias (in data and models) · Consent (data use) · Explainability
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