Duolingo: One lesson. A reason to return.

Start with a fictional learner, Sam. Sam has five minutes tonight and completes one lesson. A product can recognise that effort, encourage more practice, or imply that the day did not count. Those choices communicate different definitions of success. Before suggesting a reminder, inspect the rule behind the feedback. Your project question is whether the product recognises a useful action at the moment the user performs it. Write the action, eligibility conditions, and confirmation state in plain language. A screen redesign cannot repair an incoherent rule by itself.

Use the simulator to compare the rules. The four-lesson target is a teaching assumption, not a reconstruction of a particular learner's account. Notice that the goal can remain ambitious while the minimum recognised action becomes smaller. For your own product, define a minimum action that still creates value. Opening a dashboard may be too weak; completing a useful task may be enough. Check for accidental incentives: if the easiest qualifying action becomes all people do, will the product still help them? Put that concern next to your main outcome before launching.

A useful sequence is action, confirmation, then celebration. If the learner has to watch an animation to discover whether their work saved, decoration has taken priority over clarity. The essential outcome should remain visible when motion is paused or disabled. Try the replay below. Our original illustration is a teaching reconstruction, not Duolingo's interface. In your own prototype, test whether someone can explain what happened after a brief glance. Then ask whether the motion feels rewarding, distracting, or obligatory. Those observations can help you refine timing without treating delight as a universal preference.

In the illustrative week below, a protected missed day preserves the counter but does not represent a completed lesson. Keep those concepts separate. The interface should not imply that protecting a record is the same as acquiring knowledge. For your product, map the return after a lapse as carefully as the first success. What does the person still have? What must they do next? Can they decline a reminder or return without shame? An ethical recovery design gives an understandable route back and preserves choice. It should not manufacture urgency or pretend a setback erased genuine progress.

Relative change needs a baseline. The calculator uses a hypothetical baseline only to teach the arithmetic. It does not recover Duolingo's undisclosed control rate. A 3.3% relative increase is different from adding 3.3 percentage points. Before deciding on your own experiment, define eligibility, random assignment, a meaningful return event, and a review window. Track a quality guardrail as well as activity. Predefine how much deterioration would trigger investigation. Public results rarely include every detail you would need to reproduce a study, so keep missing sample sizes, uncertainty estimates, and segment effects visible in your notes.

Describe the user situation in two sentences. Name the useful action that should count, the current rule, and the proposed rule. Sketch the confirmation and recovery states. Include an explicit way to decline reminders or leave the flow when that is relevant. Choose a main outcome, a guardrail, and the evidence you need before shipping. Use AI to challenge the weakest assumption, not to invent a result. Keep source facts, your interpretation, and your proposed experiment in separate sections. Finish with a reason to stop or revise the idea. That turns a compelling story into a piece of product work you can defend.