When training doesn’t work, the spontaneous reaction from trainers, managers and L&D is to blame motivation: employees are resistant or just not seeing the value. Daniel Willingham’s Why Don’t Students Like School? suggests this diagnosis is usually wrong. His book is aimed at classroom teachers, but the cognitive science underneath applies to workplace learning too: disengagement usually happens because of what the training design is asking people’s minds to do.
Thinking is hard
Willingham’s first argument is that people are not naturally good thinkers. Thinking is slow and effortful, so the brain avoids it when it can. In routine situations, most of what we do runs on memory and habit rather than deliberate thought.
This has a direct implication for learning design. When working memory — the space where conscious thinking actually happens — is overwhelmed by multistep instructions, walls of text with complicated language, or too many new concepts at once, the thinking that learning requires doesn’t occur. When the brain can’t process the load, the person checks out. To an observer, this looks like a lack of interest, but for the learner, it feels like being lost or deciding the trainer is incompetent.
We can break content into smaller chunks, add white space, reduce slide density (and we certainly should), but this addresses the symptom rather than the mechanism. The underlying question is what someone’s mind is being asked to do with the information.
Curiosity is a condition, not a trait
The second argument is that people like to think, but only when the conditions are right. Specifically, when a problem sits just at the edge of what they already know: close enough to feel resolvable, far enough to feel worth pursuing. Within that range, problem-solving is genuinely pleasurable.
If the problem is too easy, people ignore it. If it’s too hard, people feel anxious and stop trying. Both states get read as motivational failure. People who appear inattentive are often not incapable of focusing, they’re probably choosing not to (if you have a child, you know).
Curiosity is less a stable trait some people have and others don’t, and more a response to a specific mental state called information gap. Therefore, the more precise design question is not “is this engaging?” (a content focused question), but “is this engaging for this person?” (a people centered question). The second requires actual knowledge of your audience before you start designing.
It also assumes the person arrives with at least some openness to engage. When the training is mandatory, the topic feels irrelevant, or the organizational context is broken calibrating the curiosity gap is necessary but not sufficient. And that’s a different design problem.
Memory is the residue of thought
The third argument has the most direct consequences for how we design learning experiences: memory is the residue of thought. You only remember what you actually thought about. It doesn’t matter what you were exposed to or what the trainer said; it only matters where your mental energy went.
An activity can require genuine cognitive effort while directing that effort at entirely the wrong thing. Willingham illustrates it with a busy classroom: just because students are actively doing something, it doesn’t mean they’re learning.
This creates the engagement illusion. A person might be “active”—playing a game, watching a story, or having a lively debate—but if their mind is focused on the mechanics, the plot of the story or the nice anecdote in the discussion, they won’t learn the core message. Good design must be disciplined enough to ensure that the “fun” elements don’t distract from the core concepts.
For learning experience designers this is a particular discipline, because the tools of the craft — narrative, interaction design, the emotional arc of an experience — are very good at directing attention. But are we directing it toward where the learning actually needs to happen? The relevant variable is not the activity itself but what the activity is asking people to think about. For that, you need to be very intentional in your design.
Structured instruction is not a relic
In more modern workplace learning environments, discovery and learner agency are the default preference, while direct instruction is viewed with strong suspicion. But cognitive science (Willingham and Kirschner among others) shows that novices cannot learn effectively from open-ended experience alone, because they don’t yet have the background knowledge to distinguish what matters. This applies to adults as much as anyone else. The novice/expert distinction is not about age or general experience, but about having sufficient background knowledge in that specific domain to know what’s relevant.
Without sufficient structure to organize what they’re encountering, novice learners in discovery-based experiences don’t discover, they get confused. That confusion rarely gets identified as a design problem, but gets attributed to the person not being ready or not motivated. From the inside, it feels like working hard and going nowhere or not knowing what you don’t know well enough to ask for help. And the mental models built from that confusion can be harder to correct later.
On the other hand, applying novice-appropriate structure to an expert doesn’t just waste their time, but it can actively get in the way. Cognitive scientists call this the expertise reversal effect: when experts are walked through content they already understand, processing what they don’t need increases their cognitive load rather than reducing it. From the inside, it feels like being talked through something you already know and the well-known feeling that whoever built it, doesn’t know who they’re talking to.
Novice and expert are not fixed identities. A twenty-year industry veteran can be a genuine novice in a specific tool, process, or regulatory context. And the reverse is equally true, someone early in their career may already have deep practical knowledge in exactly the area the training covers. Seniority is a poor proxy for prior knowledge. What matters is where a specific person sits relative to this specific content, which is a question that only real analysis can answer.
Three basic questions
Willingham’s argument reinforces a set of important diagnostic questions before starting a learning design project.
- Where does this audience actually sit relative to this content? Based on real analysis rather than assumptions. Prior knowledge shapes where a curiosity gap can be opened, what level of structure is appropriate, and how much scaffolding the experience needs before discovery becomes productive rather than disorienting.
- What, specifically, do I need this person to think about? The answer is usually more precise than the learning objective suggests. The gap between the objective and what you’re actually asking people to think about is where designs go wrong and where the engagement illusion is most likely to form.
- Does the structure of this experience match where the person actually sits? And is it framed in a way that treats them as an agent rather than a recipient? Getting either of those wrong tends to undermine the experience, even if the content matters.
Prior knowledge shapes where the curiosity gap can open, which determines what cognitive direction is realistic, which determines what instruction type is appropriate and how to frame it honestly.
Willingham, Daniel T., Why Don’t Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom. Kindle Edition.
Developed in dialogue with Claude Sonnet 4.6 (Anthropic). All original concepts and final conclusions are my own.
