Audit a learning programme

How can you distinguish content that is enjoyable to read from material that genuinely enables learning?

1960 € per session, excl. VAT

In short

A 7-hour day, excluding breaks, to audit learning materials with support from generative AI. You build a context-specific assessment rubric, check the alignment between objectives, activities and assessments, then prioritise corrections. You leave with an evidence-based diagnosis, an improved extract and a method for checking materials before distribution.

Why this training?

Learning materials can be factually correct yet still leave the learner unable to act. Vague objectives, missing examples and recall quizzes often conceal a gap between what is taught and the expected competence. AI can suggest criteria and flag inconsistencies, but its judgements still need to be checked. A context-specific rubric, precisely located evidence and a shared scoring approach make the diagnosis open to discussion and actionable. Running case study: The fictitious organisation « Avelune Expéditions » is preparing an induction module on handling damaged parcels. Its documentation comprises a slide deck, a reference operational procedure and a quiz. New staff must decide when to isolate a parcel and how to report the incident, but the materials prioritise memorising terminology. Transfer to the workplace: Within the following month, apply the rubric to a second authorised module, have a colleague review the gaps and track their resolution. Then collect learners’ responses to a workplace scenario to distinguish improvements to the materials from actual competence acquisition.

Objectives of this training

  • Build a quality rubric suited to the audience and the target competence
  • Map the links between objectives, activities and assessments
  • Document gaps using verifiable extracts
  • Prioritise corrections according to their educational impact
  • Compare an original extract and its revised version using the same criteria

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Instructional designers and training module developers
  • Training managers and subject-matter leads responsible for validating materials

Prior skills

  • Have previous experience designing, delivering training with or reviewing learning materials.
  • Be able to formulate and refine a request to a generative AI assistant.

What you will work on

  1. Define the scope of the judgement

    Objective: Link the audit to an observable workplace situation. Audience, prerequisites, target competence; distinction between document quality, educational quality and actual effectiveness.

  2. Build a rubric that distinguishes levels of quality

    Objective: Describe observable criteria rather than impressions. Accuracy, alignment, progression, cognitive load, accessibility and feedback; scoring scale and non-applicable criteria.

  3. Check educational alignment

    Objective: Identify competences that are stated but not practised. Links between objectives–activities–assessments; difference between recall, application and decision-making.

  4. Substantiate and calibrate the diagnosis

    Objective: Make judgements traceable and comparable. Analysis instructions covering quotations, uncertainties and abstention from judgement; operational verification against the procedure provided.

  5. Address priority obstacles

    Objective: Turn the diagnosis into targeted changes. Educational severity, extent and effort; guided examples, explicit instructions and explanatory feedback.

  6. Check the revision

    Objective: Measure the quality of the deliverable without assuming learning has taken place. Rescoring against unchanged criteria, checking for regressions and human validation before distribution.

What you take away

  • Audit rubric with defined scoring levels
  • Objectives–activities–assessments matrix and evidence log
  • Prioritised correction plan with proposed owners
  • Revised extract and before/after comparison

Method and assessment

  • Short input sessions followed by immediate practice using the running case study.
  • Individual work followed by work in pairs, with peer review of the outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Prepare fictitious or anonymised learning materials, their objectives and a description of the audience; the running case study documentation remains available. Check confidentiality rules and access to only those tools authorised by the organisation.

Observable criteria: Each objective is linked to an activity and evidence of learning, or their absence is flagged. ; Each gap retained cites a passage and explains its consequence for the learner. ; Unverifiable AI judgements are excluded from the diagnosis. ; Priority corrections address gaps that prevent achievement of the target competence. ; The before/after comparison uses an identical rubric and justifies every change in score..

Frequently asked questions

Can AI decide whether to approve the materials?

No. It helps identify gaps; the educational lead makes the decision based on evidence.

Are learner data required?

Not for auditing the materials. They are useful afterwards for examining actual learning.

Can internal content be used?

Only anonymised content, after checking that transmission to the selected tool is authorised.

Is the rubric suitable for all formats?

Its core can be transferred, but the indicators must be adapted to the format and audience.

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