Compelling professional case studies

How can you use AI to turn a professional dossier into an educational, credible and verifiable case study?

1960 € per session, excl. VAT

In short

This 7-hour day, excluding breaks, enables participants to turn professional documents into a case study suitable for training. Participants frame a professional decision, organise the evidence, draft a scenario using generative AI, develop a reasoned model answer and assess the coherence and educational value of the result.

Why this training?

An overly descriptive case study leaves learners as spectators; an artificial scenario makes them decide without realistic constraints. The design must bring together a professional situation, an explicit trade-off and sufficient information, without imposing an obvious answer. Generative AI speeds up narrative development and the wording of instructions, but it can invent facts or remove useful contradictions. The approach requires documentary traceability and a separation between the learner pack, assumptions and model answer. Running case study: The fictitious organisation « Rovaline Conditionnement » must weigh up rescheduling an urgent order against maintaining its production schedule. A fictitious pack brings together emails, capacity constraints and quality instructions, with a deliberate contradiction. Participants use it to develop a case for workshop managers. Transfer to the workplace: In the following month, revisit an anonymised workplace incident, apply the template and have a colleague test the case. Record misunderstandings, revise the pack and compare its quality against the assessment grid used during the day.

Objectives of this training

  • Define a professional decision and the associated observable competencies.
  • Classify the information in the pack as facts, assumptions or missing data.
  • Draft a scenario and instructions using an approved AI tool.
  • Develop a model answer that explains acceptable trade-offs.
  • Assess and then revise the case based on peer testing.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Instructional designers and training managers producing job-specific learning materials.
  • Operational experts and internal trainers wishing to pass on professional reasoning skills.

Prior skills

  • Know how to structure a request to generative AI and check its response.
  • Be familiar with a business process and its main operational constraints.

What you will work on

  1. Frame the trade-off to be taught

    Objective: Define a situation that calls for a reasoned decision. Target competency, audience, difficulty, scope and the difference between an illustrative narrative and an educational case.

  2. Assemble the evidence

    Objective: Prepare a usable pack without implicitly inventing information. Sources, chronology, contradictions, anonymisation and the status of information.

  3. Have a controlled scenario drafted

    Objective: Obtain a narrative that is faithful to the documents. Structured request: role, permitted sources, narrative constraints and a prohibition on silently filling gaps.

  4. Build the learner pack

    Objective: Make the trade-off possible without revealing the model answer. Balance of clues, necessary appendices, progressive questions and expected response format.

  5. Justify acceptable answers

    Objective: Assess the reasoning rather than a single answer. Defensible options, blocking constraints, common errors and debriefing questions.

  6. Test and improve the case

    Objective: Measure the quality of the pack and correct its shortcomings. Assessment grid covering coherence, traceability, realism and educational usability.

What you take away

  • Educational scoping sheet and evidence register.
  • Learner pack: scenario, appendices and instructions.
  • Facilitator guide and reasoned model answer.
  • Completed quality assessment grid and revision log.

Method and assessment

  • Short teaching inputs followed by immediate practice on the running case study.
  • Individual work followed by work in pairs, with peer review of the materials produced.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Arrange access to a generative AI tool approved by the organisation; the exercises use only fictitious or anonymised cases. Identify a professional decision that is difficult to teach, without sharing any confidential documents.

Observable criteria: The decision required explicitly draws on the target competency. ; Each professional statement refers to a document or is labelled as an assumption. ; The appendices enable learners to respond without essential hidden knowledge. ; The model answer distinguishes between non-negotiable constraints and defensible options. ; The test measures the criteria met before and after revision and documents the discrepancies corrected..

Frequently asked questions

Can you start from a real incident?

Yes, following anonymisation and authorisation; the fictitious pack provided is sufficient for the exercises.

Can AI fill in missing data?

Only as explicitly labelled assumptions, never as facts from the pack.

Should you provide for only one correct answer?

No: several trade-offs may be acceptable provided the constraints and justification requirements are respected.

How can you avoid making a case too easy?

Introduce relevant competing constraints, then check during testing that they prompt reasoning.

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