Evaluate what worked, gather evidence of learning, and plan adjustments for next time.
Observable indicators during jigsaw and group mapping:
Group comparison charts show:
Captured via exit ticket self-report:
Reflect on the lesson after delivery. Responses are saved locally.
Consider: evaluating an AI definition vs. generating an analogy. Which produced richer critical thinking?
Did students trust AI output too readily? Were there particular theories where AI errors were harder to spot?
Note any device access issues, login problems, or students who felt left out of the AI tasks.
Track how each group engaged with AI output during Part A of the lesson.
| Group # | AI Errors Spotted | Accepted from AI | Modified | Rejected | Notes |
|---|---|---|---|---|---|
| 1 |
Use these targeted adjustments based on what you observe during or after the lesson.
Add a dedicated activity where groups are given an AI output known to contain factual errors and tasked specifically with finding them. Celebrating the act of correction shifts students' relationship to AI output.
Reduce open-endedness: provide specific prompts for students to copy verbatim, then evaluate the output. Reduces the cognitive load of prompt design and focuses attention on critique.
Assign one theory per group rather than all three. Groups become true experts in a single theory, then share out during the jigsaw. Reduces coverage breadth but deepens engagement.
Introduce structured peer feedback forms with specific prompts: "Name one strength of the explanation," "Identify one inaccuracy or gap," "Ask one clarifying question." More structure deepens critical dialogue.