Bring LBB into a bounded exercise

WHO KNOWS ≠ WHO DECIDES. CAPABILITY ≠ JURISDICTION.

The module teaches five forms of jurisdiction: expertise, evidence, authority, affectedness, and consent. LBB decomposes statements into separate questions and jurisdictions; the learner chooses, changes, and makes the final move.

1. 1.Intro
2. 2.One statement containing multiple jurisdictions
3. 3.Expertise ≠ Authority
4. 4.Lived experience / Simulation ≠ Participation
5. 5.Sometimes the proper jurisdiction is a document or source
6. 6.Consent as jurisdiction
7. 7.Mixed-question routing scenario
8. 8.Learner pastes one real AI answer
9. 9.LBB automatically decomposes into jurisdictions
10. 10.Learner selects or changes the next move
11. 11.Short reflection
12. 12.Completion

No points, badges, streaks, or leaderboards. No claim that the module improves critical thinking, AI literacy, judgment, learning, or calibration until those outcomes are studied. Usable without an account.

PRAISE ≠ REVIEW. MACHINE COMMENTARY ≠ INDEPENDENT REVIEW. BOARD CONVERGENCE ≠ EXPERT CONSENSUS. DISCLOSURE ≠ DISINTEREST.

The module does not teach that AI feedback is useless. AI praise or criticism may be useful internal feedback — its usefulness does not make it independent review.

1. 1.Intro
2. 2.AI praise of a fictional proposal
3. 3.AI criticism of the same proposal
4. 4.Prompt sensitivity / same artifact, different evaluation
5. 5.Useful feedback ≠ independent review
6. 6.Multiple Board agents agreeing
7. 7.Degrees of independence
8. 8.Disclosure ≠ independence
9. 9.Learner supplies one real artifact
10. 10.LBB generates praise, critique, and status audit
11. 11.Learner chooses what those evaluations actually establish
12. 12.Optional outside-review World Return
13. 13.Completion

LBB performs the analytical work. The learner chooses, confirms, disagrees, and makes the final disposition — including what LBB's praise, critique, and status audit actually establish.

No points, badges, streaks, leaderboards, mastery language, or agreement scores. No claim that the module improves critical thinking, AI literacy, judgment, calibration, or learning until studied. Usable without an account.

SIMULATION ≠ PARTICIPATION. SYNTHETIC SAMPLE ≠ HUMAN SAMPLE. PREDICTED REACTION ≠ OBSERVED REACTION. PARTICIPATION ≠ REPRESENTATIVENESS.

The module does not teach that simulated reasoning is useless or deceptive. Simulation can prepare participation — it does not become participation.

1. 1.Intro
2. 2.Fictional teacher panel
3. 3.1,000 synthetic respondents
4. 4.Persona ≠ Person / accessibility example
5. 5.Why actual participation matters
6. 6.Legitimate uses of simulation
7. 7.Predicted response vs actual World Return
8. 8.Participation ≠ representativeness
9. 9.Inspect a vague stakeholder-participation claim
10. 10.Learner supplies a real artifact
11. 11.LBB performs Participation Check
12. 12.Optional World Return
13. 13.Completion

In the final challenge, LBB scans the learner's artifact for claims about human groups and classifies the evidence of their perspective — simulated, inferred, actual individual participation, reported research, or unknown — then offers next moves and an optional World Return.

Learners use fictional, public, or de-identified examples. No student identities, medical information, protected education records, or private employee information.

No points, badges, streaks, leaderboards, or mastery scores. No claim that the module improves critical thinking, judgment, AI literacy, learning, or calibration until studied. Usable without an account.