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Works with or without student devices — every live-AI activity has a printed no-device path (sample responses ship in the facilitator guides).
1
AI and your health — what you don't know can hurt you
Introduces the reality of AI in healthcare and the confidence gap — AI sounds equally sure whether it's right or wrong. Students meet the PAIR study and learn the six-step VERIFY framework used throughout the curriculum.
2
When AI gets it wrong — real cases, real consequences
Four real, published cases where AI health answers went wrong — skin cancer detection, symptom reasoning, drug information, chest pain triage. Students run VERIFY on each; sample responses support classrooms without devices.
3
The ghosts in the machine — how bias gets built into AI
How bias enters AI at four stages — data collection, labeling, training, deployment — plus five bias types with healthcare examples. The "I" in VERIFY expanded, bridging to the diagnosethebias.org case database.
4
Your turn — evaluating AI like a researcher
Students become the researchers: choose a health topic, prompt an AI (or use the included sample responses), run a full VERIFY analysis, and compare the answer against a credible independent source.
5
Building your own AI health literacy tool
From evaluators to creators: teams design their own AI health literacy tool — a wallet card, a social campaign, a chatbot "warning label" — and pitch it. Low-tech by design; paper and markers are enough.
6
What happens next — AI literacy as a life skill
Capstone: a Jeopardy-style review of all five sessions, a reflection on AI literacy as a life skill, and the pre/post survey that measures what the class learned.
Creative Commons BY-NC-ND 4.0
All Verify Project curriculum materials are licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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