Practical guide
AI fluency for L&D managers: test transfer after training
Design a fictional follow-up exercise that tests applied learning without confusing recall, participation and transfer.
A learning and development manager can use AI to help design practice, but needs a clear account of what the follow-up evidence will show. This original exercise asks whether a verification behavior appears in a different workplace task after instruction. It distinguishes transfer from memorizing an example, while avoiding claims that one follow-up establishes general competence or a training programme's causal impact.
Define the behavior to observe
Choose an action visible in a work artifact rather than a broad aspiration.
In the fictional programme, participants practiced checking a supplier claim against a source document. The follow-up question is whether they can identify and verify a consequential claim in a new project-status task. The target is not simply confidence with AI or completion of another lesson.
Write the expected evidence: the claim selected, the source used, the correction or qualification and the final wording. This makes the task reviewable. A self-report about feeling more capable may add context, but it does not replace the work evidence needed for this question.
Change the context without changing everything
A transfer exercise should be different enough to require application while preserving a recognizable target behavior.
Replace the supplier pack with a fictional project update containing a milestone assertion and a conflicting status note. Keep the reading burden manageable and the relevant source available. If the new task adds unfamiliar specialist knowledge, performance may reflect that difference rather than the verification behavior alone.
Ask AI to draft the scenario, then inspect the materials for a genuine, resolvable discrepancy. Do not merely rename the supplier in the original exercise. Equally, do not make the follow-up so ambiguous that the reviewer cannot tell what a supported correction would look like.
Specify conditions and permitted support
Interpretation depends on what the participant was allowed and able to use.
Record the time available, source materials, AI access and any guidance provided. Decide whether a reminder to check claims is part of the intended workplace condition or an instructional prompt that changes what is being observed. Keep that choice explicit when comparing attempts.
Use synthetic materials and avoid unnecessary personal information. If a source fails to open during the exercise, record the access problem. Do not treat a missing correction as evidence of weak verification when the participant lacked the information required to perform the check.
Review evidence before interpreting improvement
Compare concrete work, while keeping alternative explanations visible.
Inspect whether the participant selected the consequential milestone claim, located the conflicting note and revised the final update appropriately. If they checked a minor detail but missed the milestone, describe that pattern rather than marking the entire task simply successful or unsuccessful.
A better follow-up artifact may be consistent with learning, but differences in task difficulty, support or familiarity can also matter. Do not claim that the training caused an improvement from one uncontrolled comparison. The useful immediate result is a bounded description of what the participant demonstrated under the stated conditions.
Use the result to choose the next learning action
A transfer check should inform practice, not become an automatic personnel decision.
If the source was found but the final claim remained overstated, practice translating verification into communication. If the wrong claim was prioritized, practice identifying consequences. If the task conditions were flawed, repair the exercise before interpreting the participant's result.
Keep the original assignment, follow-up artifact and review note together so the next learning conversation has concrete evidence. Plan another context when broader transfer matters. The people function guide covers programme-level evidence choices; this role exercise focuses on designing and interpreting one defensible follow-up task after training.
Sources and scope
NIST addresses AI risk management. Skills England describes workplace AI foundations.
These sources provide background, not endorsement of this exercise. The worked example and suggested review method are original illustrative guidance. They are not customer results, validated benchmarks or evidence of a particular product capability. Adapt the exercise to the task and use qualified review where consequences require it.
Sources: [1] [2]
Sources
- 1.AI RMF Core · NIST
- 2.AI foundation skills for work benchmark · Skills England