Use case

Learning reinforcement: what people learned in the course is not lost in the weeks after

A classroom or e-learning course presents the material. Learning reinforcement makes it last. The game brings the course content back in short rounds, at the intervals where it matters most.

Illustrative example of reinforcement after a negotiation course.

The problem

It is the forgetting curve, described by Hermann Ebbinghaus: what people learn starts fading right after, unless it is reviewed.

A one-off session, however good, is not enough. Knowledge sticks when people return to it several times, in sessions spaced out over time.

How the game helps

  • The course presents, the game reinforces. Questions built from the course materials, with help from AI and human review.
  • Spaced repetition per person and per question. Every participant has a memory model for every question. The game picks the right six questions for today.
  • Retrieval practice. Answering forces people to recall, and recalling consolidates more than rereading notes.
  • Visibility for the trainer. Funnel by level, hardest questions and most chosen wrong answers show what needs another explanation.

Three sample questions

01

What is the first step of the negotiation model we practised?

  • Prepare and set objectives (Correct answer)
  • Make the first offer
  • Make a concession
  • Close the deal

Why: Without objectives and limits set beforehand, every offer is improvised.

02

In a feedback conversation, what do you describe first?

  • The behaviour you observed (Correct answer)
  • Your opinion of the person
  • The consequences
  • What colleagues think

Why: Starting with the observed behaviour keeps the conversation on facts, not on the person.

03

Which tool from the course helps decide what to do first?

  • Urgent and important matrix (Correct answer)
  • Organisation chart
  • Gantt chart
  • Contact list

Why: The matrix separates the urgent from the important and helps decide what to do, delegate or postpone.

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