Evidence-Informed Learning Design: Creating Training to Improve Performance: Summary & Key Insights

One of the early distinctions we emphasize is between 'evidence-based' and 'evidence-informed.' Evidence-based practice, a term borrowed from medicine, implies that decisions can and should always be directly derived from the best available empirical studies. However, in learning design, variables multiply: context, learner diversity, organizational goals—all of which alter what 'best' looks like. So rather than pretending that a rigid application of research will always fit a complex workplace, we advocate an 'evidence-informed' approach, one that combines empirical findings with professional expertise and contextual judgment.

In simple terms, evidence provides direction, but design requires navigation. For example, we know from research that retrieval practice significantly strengthens memory. Yet, applying retrieval practice in workplace learning might vary depending on the audience’s time constraints or motivation. A designer informed by evidence understands the principle (‘retrieval strengthens retention’) but adapts its application (‘integrate short recall tasks at intervals’) to context.

This nuance is critical because absolute evidence does not exist in social sciences; studies provide probabilities, not certainties. Our task is to translate those probabilities into informed design decisions. And that translation relies as much on critical thinking as it does on data.

Another key aspect of being evidence-informed is intellectual humility. Many practitioners cling to fads—learning styles, multitasking, digital natives—because they sound plausible or resonate intuitively. But intuition is not evidence. So we urge designers to cultivate skepticism: to ask, 'What’s the source of this claim? Under what conditions was it tested? Does it generalize to my learners?' By questioning rather than accepting, learning professionals grow as reflective practitioners capable of explaining not just what they do, but why it works.

Before one can design learning effectively, one must grasp how learning actually happens. Human cognition operates under constraints: we can attend to only limited information at once, and our working memory is easily overloaded. Learning occurs when information is meaningfully encoded and connected to existing knowledge in long-term memory.

We describe attention as the gatekeeper of learning. You cannot remember what you never attended to. Yet attention is fickle, slipping easily under distraction. Good design therefore directs attention purposefully—through clarity, relevance, and thoughtful sequencing. Simplicity is not dumbing down; it’s respecting our cognitive capacities.

Memory follows attention. But memory isn’t a file drawer where information is deposited—it’s reconstructive. Each retrieval reshapes what was stored, strengthening neural connections. This is why techniques like spaced repetition and interleaving (mixing related topics) outperform massed practice. What feels harder often helps learning more—a phenomenon known as 'desirable difficulty.'

We also highlight the interplay between motivation and cognition. Motivation isn’t about superficial engagement or mere fun; it’s about expecting success, valuing outcomes, and perceiving control over one’s own learning. Designers can nurture motivation by aligning learning tasks with authentic work challenges, giving learners a sense of competence and autonomy. It’s about connecting the cognitive to the meaningful.