Desirable difficulties make practice effortful in ways that strengthen the retrieval, judgement, and application a workplace skill will require later.
At an airport gate, a passenger-service agent compares two travel documents while listening to a passenger explain a recent change. The examples in training had a clear mismatch and plenty of time. Here, several details look similar, boarding is under way, and the agent must identify the one difference that changes the decision.
Easy practice can make this skill look secure while the answer is still obvious. The hard moment reveals whether the agent can retrieve the relevant knowledge, distinguish between near-matches, and act independently. Training needs some of that effort before the real situation arrives.
Desirable difficulties are learning conditions that make practice slower or more effortful now while improving retention or transfer later. The Bjork Learning and Forgetting Lab (opens in new tab), which developed the idea, gives spacing, retrieval practice, interleaving, and varying practice conditions as examples.
The word desirable sets an important boundary. The difficulty must make the target skill's retrieval, discrimination, or application more demanding. Dense instructions, ambiguous questions, irrelevant complexity, and unsupported failure merely consume attention.
This distinction matters because performance during practice and learning are different signals. An employee can complete a sequence smoothly while labels, examples, and recent explanations are available, yet struggle to perform it later without those supports. Our article on the fluency illusion explains why that smooth experience can create misleading confidence. Desirable difficulties offer a design response: remove selected support while the learner can still practise safely and receive useful feedback.
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Let the employee produce an answer, next action, or explanation before revealing the solution. Assessment-based learning turns that attempt and its feedback into part of learning. Even a short pause for retrieval makes the learner use the mental process required later, instead of following an answer that remains visible.
Immediate repetition often succeeds because the answer is still active in memory. Bring the skill back after a delay, when retrieval requires more effort. The result gives a more useful view of what remains available and creates another opportunity to strengthen it.
Blocked practice can make ten similar examples feel easy because the same response keeps working. Interleaving mixes related situations so the employee must first decide which principle applies. For the gate agent, that could mean comparing document cases with different decisive cues rather than completing one mismatch type at a time.
Change the surface details, remove a highlighted term, or ask the learner to generate the first step before showing options. A safe attempt before instruction can also prepare attention for the explanation, as our article on testing before learning explores. Keep the cue that would genuinely exist at work and remove the cue supplied only by the lesson.
More difficulty does not automatically produce more learning. A novice who lacks the necessary foundation may be unable to make a useful attempt. A vague question may test interpretation of the writer rather than mastery of the skill. A high-stakes simulation can also create risk when the same learning goal could be practised safely elsewhere.
Productive difficulty stays attainable, relevant, and correctable. Learners understand the task, have enough prior knowledge to engage with it, and receive feedback that explains both the action and the reasoning. The challenge can increase as performance becomes more reliable.
The ten drill design principles place this calibration on a continuum from too easy to productive to too hard. If errors become random or feedback cannot help the learner adjust, restore support or reduce complexity. If everyone answers from obvious clues, remove a cue or vary the case.
Start with the independent workplace performance, then decide where training should create effort. Four questions keep the design practical:
This approach also improves scenario design. Case studies that connect policy with practice work best when the learner must identify the relevant signal and make a decision, rather than recognise a story already explained. For physical or interpersonal skills, question results remain one source of evidence alongside observation and supervised performance.
Drillster combines question-based learning, immediate explanatory feedback, adaptive practice, and reinforcement over time. Practice can return when recall requires effort, while stronger elements appear less often. Variants can change the context without changing the underlying competence being trained.
The goal is a learning journey that prepares people for independent execution at the difficult moment. Choose one critical skill that currently looks effortless in training and examine which support disappears at work. If that gap deserves a more deliberate practice design, talk with our team about where productive difficulty can strengthen the journey.
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