A Practice Lab for Teacher AI Adoption

Act as a critical learning-engineering reviewer. Review the prototype brief below as a thin-slice experiment, not as a finished product.

Return:
1. The strongest design assumption.
2. The most important plausible failure mode.
3. The highest-value falsification test for the next cycle.
4. One credible non-AI alternative that could address the same capability gap.
5. The next smallest prototype worth building.
6. What should be measured to distinguish engagement from learning, transfer, and real performance.
7. Human-agency, equity, accessibility, privacy, or governance concerns that should change the design.

Be concrete. Separate evidence-backed claims from hypotheses. Do not reward novelty for its own sake, and do not assume more AI is better.

COLLECTION: AILE
TITLE: A Practice Lab for Teacher AI Adoption

CAPABILITY GAP:
AI adoption in schools often depends on a few enthusiastic users while other educators receive tool demos without enough time to build judgment about when AI is useful, risky, or instructionally counterproductive.

FIRST PROTOTYPE:
Build a scenario-based professional learning lab where teachers practice making AI-use decisions, try bounded tools on authentic planning tasks, see failure cases, and receive feedback tied to an explicit competency framework and local policy.

REPRESENTATIVE USE CASE:
A teacher chooses whether to use AI to generate feedback on student writing, then compares three configurations: unrestricted generation, rubric-grounded suggestions, and teacher-only planning support.

LEARNING / HUMAN-SYSTEM FRAME:
Teacher learning should involve application, reflection, feedback, and opportunities to revise—not one-time compliance training. The goal is professional judgment and human agency in a teacher–AI–student system.

QUESTIONS ALREADY IDENTIFIED:
1. Which competencies predict responsible classroom use?
2. What kinds of practice change teacher behavior rather than only attitudes?
3. How should local policy and subject-area pedagogy alter the scenarios?

FIRST-CYCLE FRAMING:
Understand: Validate that the real gap is the ability to make context-sensitive instructional decisions about AI use, not simply low engagement, low tool use, or a workflow inconvenience.
Map: Map the system: teacher, students, curriculum, school policy, AI tools, professional learning team, and leadership. Identify where the capability currently succeeds, breaks down, or is masked by other constraints.
Instrument: Predefine evidence: scenario judgment, classroom implementation quality, policy alignment, teacher confidence calibration, and student learning evidence. A key disconfirming signal is: teachers report higher confidence but make no better instructional or risk-management decisions.