Class 02 — Why AI Lies Confidently
Date: TODO · Slides: TODO
Objectives
Section titled “Objectives”- Describe AI training at a conceptual level (data → patterns → prediction).
- Explain why AI produces confident but incorrect answers.
- Understand why wording and context change results.
- Run a basic verification loop on any AI claim.
Agenda (60 min)
Section titled “Agenda (60 min)”| Time | Block |
|---|---|
| 0–5 | Warm-up: students show their “suspected AI” examples from homework. |
| 5–20 | Mini-lesson: training data, tokens and next-token prediction, probability not truth → hallucinations are structural, not a bug. |
| 20–25 | Game: predict the next word in several sentences — feel how prediction works. (First cut if short on time.) |
| 25–40 | Three-question lab: ask the AI an easy factual question, a reasoning question, and an obscure question. Compare results — confidence looks the same when correctness doesn’t. |
| 40–55 | Fact-checking core: source quality; primary/official sources; cross-checking two independent sources; spotting confidence without evidence. Verify one claim from the lab together. |
| 55–60 | Exit ticket: write the verification loop in your own words (ask → check → cross-check → cite). |
Key terms
Section titled “Key terms”- Token — the unit models predict.
- Hallucination — fluent output not grounded in fact.
- Primary source — where claims actually live.
Seven-Question connection
Section titled “Seven-Question connection”Q4 (accuracy) — this class is the deep-dive behind “can I ensure it’s accurate?” The verification loop becomes a required part of every log entry from now on.
Homework / log entry
Section titled “Homework / log entry”Find and document one AI mistake this week (any tool, any topic). Screenshot or quote it, and note how you caught it.
Teacher notes
Section titled “Teacher notes”- Deliberately merged original Modules 2 + 6; drop context-window minutiae.
- The lab works even with one shared account on the projector — pairs take turns.