Blended learning design: stop making learners join the dots

Give every activity a job

Blended learning combines different learning settings or formats around an outcome. The useful design question is not how much content should be online. It is what learners need to understand, practise, apply and demonstrate, and which setting supports each step.

Start with one observable outcome. For an illustrative AI programme, that might be producing a checked internal briefing from approved source material. Work backwards from the evidence a reviewer would accept. Add activities only when they help learners reach that standard.

Stage one: prepare for the practice

Before a live session, give learners the minimum context they need to participate. That could be a short explanation, a worked example and a prompt to identify a relevant task. Tell them how long preparation should take and why it matters in the next session.

Check access, accessibility needs and the availability of practice material. If preparation depends on an account or document that learners cannot open, the live session inherits the problem. Make the support route visible before the deadline.

Do not move a large lecture online simply to call the programme blended. Preparation should make the next activity more useful, not add an unexplained layer of homework.

Stage two: use live time for judgement

Live sessions are useful for seeing how people approach a task, discussing ambiguity and giving feedback. Demonstrate a complete attempt, including a mistake or uncertainty. Then let learners attempt a fresh version and explain their choices.

For the briefing example, participants might compare two drafts and identify missing context, unsupported statements and unclear decisions. The trainer can explore why those differences matter to the reader. That discussion offers more than watching another demonstration of the same tool.

Keep the group size, facilitation and available time aligned with the amount of feedback you promise.

Stage three: make workplace application feasible

Give learners a specific task to try after the session, an approved environment and a named person who can help. Managers should know what practice requires and when it will happen. “Use this in your job” is too vague to become a reliable assignment.

State what may be submitted as evidence. Redacted examples, synthetic equivalents or a structured explanation may be appropriate when work material cannot be shared. Agree the approach with the organisation instead of asking learners to make a sensitive-data decision alone.

Stage four: review and close the loop

Use the same outcome and quality criteria you started with. Ask learners to explain their process as well as show an output. Give them a chance to improve it after feedback. Record what was demonstrated and what still needs practice.

Attendance, quiz results and workplace evidence answer different questions. Decide which records are required for completion before delivery begins. If your systems store live attendance and independent learning separately, explain how you will reconcile them rather than implying an automatic combined record.

Copy the programme planning sheet

For every stage, write down:

• The learner action and its purpose.
• The time and access required.
• The person responsible for support.
• The evidence produced and who reviews it.
• The handover into the next stage.
• The fallback when someone misses an activity.

Read the sheet from the learner’s perspective. Can they tell what to do next without asking the trainer? Can the trainer see who needs help without opening five unrelated spreadsheets?

Choose delivery and technology together

Use the LMS buyer checklist to test whether your platform supports the designed journey. The training proposal template helps turn it into a brief.

Explore sponsoring an AI literacy programme, or talk to Experrt about connecting practical training with a clearer learner experience.