Use a worked example without turning fiction into a case study

State that the example is fictional

Give the scenario enough detail to practise a decision: purpose, sites, dimensions, dates and constraints. Use neutral references rather than invented customer logos or testimonials. Do not present a fictional outcome as completed work, a supplier review or an environmental achievement.

Show the reasoning and assumptions

For a fictional three-store rollout, identify which quantities are confirmed and which depend on a site survey. Compare the alternatives against the stated requirements. A learning example is strongest when the reader can see how missing information changes the decision rather than merely being told the preferred answer.

Create a clean starting point

When copying an example into a new draft, remove invented names, dates, approvals and results. Ask the learner to enter their own measured facts and confirm the provider’s requirements. Course completion can record that an exercise was completed; it is not professional accreditation, safety acceptance or environmental certification. Keep the exercise output portable so the learner can use it with their team.

Worked example — fictional

A learning exercise describes a fictional two-site recruitment event. It gives illustrative sign dimensions, one missing survey measurement and a retained display frame needing inspection. The learner makes a brief, calculates a quantity and identifies the next decisions. The scenario does not name an actual customer, claim successful delivery or show an invented environmental result. When the learner wants to reuse the structure for real work, the output starts as a clean draft. Fictional dates, identities and approvals are removed; actual facts are entered and supplier conditions are confirmed. The exercise feedback can state that the arithmetic and missing-question identification were correct. It cannot certify a real print file, professional competence or safe installation. This keeps realistic teaching useful without turning the example into fabricated platform activity.

Decision checkpoints

• Scenario: label the exercise as fictional and give enough information to practise a real decision.
• Assumptions: identify which illustrative inputs are supplied, which are unknown and which need confirmation.
• Feedback: explain the reasoning behind an answer and what the learner should do with the result.
• Transfer: remove invented identities and outcomes before creating a private draft for actual work.

Common pitfalls

• An attractive fictional dashboard can be mistaken for real customers or completed cases if its status is unclear.
• A high exercise score does not establish accreditation, safety acceptance or successful external production.
• Copying all example data into real work can preserve invented contact details, approvals and dates unnoticed.

Use it on your next job

Choose a self-guided pathway and complete its output with illustrative values or permitted real information. Explain the assumptions in your export and identify the responsible next check. If you adapt the example, start a draft with actual references and no invented approvals. Share lessons only with a clear description of the exercise and its scope.

Take this to the next job

• Label fiction clearly
• Show assumptions
• Explain the decision
• Remove invented approvals before reuse
• Describe learning outcomes accurately