A District AI Use Framework That Protects Learning

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 District AI Use Framework That Protects Learning

CAPABILITY GAP:
Schools are adopting generative AI faster than many districts can define what responsible use looks like. Simple bans or blanket permission do not distinguish between AI that supports learning, AI that replaces learning, and AI that creates unacceptable privacy or equity risks.

FIRST PROTOTYPE:
Develop a policy-and-practice sandbox that classifies proposed AI uses by learner age, instructional purpose, data sensitivity, human oversight, and the capability at risk. It generates a draft classroom protocol and student-facing explanation, while routing high-risk uses for human review.

REPRESENTATIVE USE CASE:
A district considers AI feedback on middle-school writing. The sandbox distinguishes brainstorming, formative feedback, and full-text generation, then asks what evidence must remain visibly the student's own.

LEARNING / HUMAN-SYSTEM FRAME:
The design treats AI literacy and governance as part of the learning system, not as a separate compliance layer. Policy should preserve human agency, make expectations teachable, and align tool use with a specific learning purpose and observable evidence.

QUESTIONS ALREADY IDENTIFIED:
1. Which uses should be encouraged, constrained, or prohibited by age and task?
2. How will teachers and students learn the policy rather than merely acknowledge it?
3. What evidence would show that a rule improves learning instead of only reducing incidents?

FIRST-CYCLE FRAMING:
Understand: Validate that the real gap is the ability to make and explain responsible decisions about AI use in learning, not simply low engagement, low tool use, or a workflow inconvenience.
Map: Map the system: students, teachers, families, district leaders, approved tools, data governance, curriculum, and local policy. Identify where the capability currently succeeds, breaks down, or is masked by other constraints.
Instrument: Predefine evidence: policy comprehension, consistency of decisions, learning-task integrity, privacy incidents, teacher workload, and equity of access. A key disconfirming signal is: rules are followed superficially but teachers and students cannot reason about new AI situations.