TEACHER ONLY · LESSON GUIDE
AI Ethics Council Day 2 — Follow the Incentive
Run the lesson here; the student item below remains the independent and absence route.
Daily Learning Contract
Topic: Recommendation systems, incentives, and visibility
Objective: Students will analyze how a recommendation system's incentives distribute benefits and visibility, then justify a rule change and its tradeoff.
- §126.19(c)(9)(D) — evaluate the bias of digital information sources, including websites.
Demonstration of learning: Casebook stakeholder map, redesigned recommendation rule, and explanation of who gains, who loses, and what new problem may result.
Before class
Time: One class period
Student route: Day 2: Recommendation Systems + Incentives
Use what already exists: Casebook and Day 1 evidence.
Lesson flow
- Launch: Launch with a concrete question or failure that makes incentives and visibility matter.
- Model: Model one current example or tool move before students begin.
- Do: Guide the first decision together, then release students to the existing Canvas task.
- Check + support: Check for the named evidence, not completion alone; keep a visual sequence, sentence stems, and purposeful partner roles available.
- Close + handoff: Collect or verify the evidence and name the next lesson's connection.
Check before students leave
Ask students to point to the specific evidence above and explain one choice, test, or revision. Completion alone is not evidence of understanding.
Support + troubleshooting
Scaffold: Keep the visual task sequence visible, assign purposeful partner roles, and use the sentence stems on the student page before simplifying the task.
When students get stuck: Ask for the exact step where the tool, code, or task stopped. Check setup, inputs, and saved work before replacing the task or lowering the thinking.