Author: AILesson10 min setupTested with:ChatGPTReviewed: 2026-08-28
Quick answer
Convert authentic professional evidence into a bounded decision case without exposing sensitive details. Provide: Professional source material, Learning design, Adaptation and ethics constraints. Expected result: A teachable case with evidence packet, decision task, facilitator guide, and source-change ledger.
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Your prompt
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Turn the supplied professional material into a classroom case grounded in its evidence.
Facts, artifacts, timeline, decisions, data, roles, outcomes, sources, and confidentiality:
[material]
Learner level, objectives, prerequisites, time, assessment, and decision complexity:
[learning]
Permissions, anonymization, accessibility, invention boundary, language, format, and instructor needs:
[constraints]
Inventory source facts before adapting. Separate retained fact, anonymized detail, simplified structure, composite element, pedagogical assumption, and omitted information. Do not invent a real person's quotation, motive, wrongdoing, protected trait, result, or confidential operational detail. If fictional bridging is permitted, label it clearly and ensure it cannot be mistaken for evidence. Preserve material uncertainty, competing interests, constraints, and at least one reasonable alternative; do not make the instructor's preferred answer inevitable.
Create learner-facing title, context, timeline, roles, evidence packet, decision point, task, deliverable, criteria, and staged disclosure. Include only information learners need and provide accessible equivalents for visuals/tables. Then create a separate facilitator guide with objectives, preparation, likely approaches, misconceptions, probing questions, evidence-based analysis, multiple defensible decisions, debrief, extension, and assessment rubric. Add a source-to-case change ledger, privacy/licensing check, and validation questions for a subject expert.
Private by defaultPrompt assembly happens locally in your browser. Avoid placing confidential information into any AI service unless your organization allows it.
From input to outcome
A worked example
See how concrete context turns this recipe into a usable result
Actual input
Professional source material
Internal library reminder pilot: 214 opted-in adults over six weeks; 61% collected before expiry versus 54% among 198 in prior six weeks; non-randomized and seasonal mix differed. Eleven opt-outs, three late-message reports, staff handled replies ~25 min/day. Operations considered a randomized follow-up but had no approved sample rationale, owner, budget, privacy/accessibility review, or stop thresholds. No rollout decision. Aggregate facts may be taught; library, staff, dates, systems, and patron details must be anonymized. Internal report licensed only for instructor use.
Learning design
Graduate public-service management course. Objectives: distinguish descriptive from causal evidence, identify operational/ethical guardrails, and make a proportionate decision under uncertainty. Students know basic percentages but not trial design. Fifty-minute case discussion; team memo of 300 words assessed on evidence, alternatives, and conditions.
Adaptation and ethics constraints
English learner packet with plain-language table and text alternative. No invented quotes. Fictional organization name allowed and labeled. Learners may choose design, refine, or decline a test; permanent rollout is outside decision authority. Facilitator needs timed plan and rubric.
Example output
Learner case—“The Cedar Service Reminder Decision” (Cedar is a fictional name; aggregate evidence comes from an anonymized real pilot). A public service asks whether to spend limited design capacity on a stronger reminder test. Evidence packet: prior six weeks 107/198=54%; pilot six weeks 131/214=61%; periods not randomized and seasonal mix differed; 11 opt-outs; 3 late reports; about 25 staff-minutes/day. Text alternative states every value and denominator. Missing by design because genuinely unknown: sample rationale, owner, budget, privacy/accessibility review, stop thresholds.
Decision: recommend Design, Refine before design, or Decline; do not authorize rollout. Submit 300 words stating claim boundaries, two alternatives, affected groups, three conditions/guardrails, and evidence that would change the recommendation. Stage 1 gives outcome data; after 12 minutes Stage 2 reveals operational signals so students may revise. Criteria: uses denominators/non-randomization correctly; weighs service, access/privacy, and staff implications; compares alternatives; makes proportionate conditional decision.
Facilitator: 0-5 individual claim; 5-15 teams inspect Stage 1; 15 reveal Stage 2; 15-30 option analysis; 30-40 challenge from patron/staff/governance lenses without inventing their views; 40-47 decisions; 47-50 debrief. Probe: “What can 61 versus 54 establish?” “Which missing gate matters before design versus before launch?” Defensible outcomes include bounded design with gates, refinement to add accessible channels/ownership, or decline if capacity threshold is explicit. “Pilot worked” is a misconception.
Rubric 0-2 each: evidence fidelity, alternatives, guardrails/equity, decision proportionality, revision after disclosure. Ledger: all numbers/limits retained and anonymized; Cedar is labeled fiction; dates/systems omitted; no quotes or outcome invented. Validate with subject expert: whether 107/198 and 131/214 match report after exclusions, whether aggregate teaching permission covers the derived percentages, and whether operational details remain non-identifying.
Why this works
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A change ledger protects authenticity by showing exactly where teaching needs altered the source
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Multiple defensible choices make learners reason from evidence instead of guessing the instructor's answer
Check the result
Can every case fact be traced to the source or a clearly labeled permitted adaptation
Are privacy, permissions, uncertainty, trade-offs, and reasonable alternatives preserved
Do the learner task, evidence packet, guide, and rubric align with the learning objectives
Use it with confidence
Frequently asked questions
Practical answers about when to use this recipe, what to provide, and where human review still matters
What should I prepare before using “Turn professional material into a classroom case”?
For “Turn professional material into a classroom case,” prepare Professional source material, Learning design, and Adaptation and ethics constraints. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.
When is the “Turn professional material into a classroom case” result not ready to use?
The result is not ready if it does not yet deliver the stated outcome—A teachable case with evidence packet, decision task, facilitator guide, and source-change ledger—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.
Which AI tools have recorded tests for “Turn professional material into a classroom case”?
The published test record for “Turn professional material into a classroom case” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.
ChatGPT