Are eLearning companies serving fast food for the mind?

The hard stuff is easy, the soft stuff is hard

Tom DeMarco once observed that the hard stuff is easy and the soft stuff is hard. The things we can measure, optimise and systematise get labelled as the difficult problems, while the things that actually matter, like judgement, understanding and long-term outcomes, stay stubbornly resistant to clean measures of success. You can see the pattern everywhere once you look for it, and few places show it more clearly than fast food.

Fast food did not take over because it was the best way to nourish people. It took over because it was quick, standardised, scalable and easy to optimise. As a product it is well optimised, as long as you only measure the hard stuff. A burger sold is a clean signal. A drive-through time is a clean signal. Nutrition is messy and long-term health more so, so over time the industry drifted towards what was visible and trackable and away from what mattered.

eLearning is following the same incentives

A lot of eLearning now feels uncomfortably similar, not because anyone set out to lower standards but because the same incentives are at play. Replace burgers with modules and fries with short clips and the pattern holds. What wins is engagement, watch time and completion, all neat numbers that move in the right direction when content gets easier to consume. The problem is that these signals are partial proxies for learning at best, and often weak ones. Recent research into learning analytics shows that most systems measure observable behaviour like clicks and time on task, while the deeper cognitive side of learning stays largely invisible. Meta-analyses suggest the relationship between engagement and actual outcomes is moderate at best. Engagement matters, but it is not understanding.

This is where product thinking quietly shapes the outcome, not through bad intent but through the metrics that define success. If your job is to cut drop-off and raise completion, you smooth the path, shorten the content, remove friction and simplify the explanation until little resistance remains. A study from edX shows that shorter videos perform far better on engagement and often recommends segments under six minutes. The authors caution that engagement is a necessary condition for learning, not a sufficient one, but the measurable signal tends to win the decision. Once something can be tracked easily, it becomes the thing that gets optimised, and everything else follows.

Understanding requires friction, even if it feels worse

The issue is that real understanding rarely comes from frictionless consumption. It comes from structure, effort and time spent working through ideas that do not resolve straight away. Research on whole-task learning shows that people learn more effectively when they engage with coherent, structured problems rather than fragments, and studies on spacing and retrieval practice point the same way. Learning deepens when it is revisited, tested and applied, not simply consumed. None of this sits comfortably inside a model optimised for short bursts of attention.

There is a psychological trap here that becomes obvious once you look at the evidence, because what feels easy often gets mistaken for what is effective. In the PNAS study by Deslauriers et al., students placed in more active, effortful learning environments went on to score higher on assessments, yet consistently reported feeling as though they had learned less than students in more passive settings. The reason was that the process itself felt harder and less straightforward. That gap between perceived learning and actual learning matters. If platforms optimise for what feels smooth and immediately rewarding, they tend to filter out the very conditions that make knowledge stick.

AI is accelerating the trend

AI has pushed this further, because it lets people skip the process and jump straight to outcomes. The data from higher education already tells a clear story. The vast majority of students now use generative AI in some form, and a meaningful share use it directly in assessed work. Researchers warn that this shifts the focus from understanding to output, from learning to performance. The same pattern shows up in software, where AI tools make it possible to produce working code without fully grasping the underlying system. Early experimental evidence suggests this comes at a cost, with AI-assisted learners scoring worse on follow-up assessments and struggling more with debugging. The risk is not that people use these tools. It is that they never build the mental models they need to work without them.

This is an incentives problem, not just a product problem

It is tempting to blame product managers, but that is only part of the story. The deeper issue is the measurement regime. When success is defined by engagement, watch time and completions, the rational move is to design for those outcomes. The system does not reward depth, so depth erodes. Over time the platform starts to look like a fast food chain for the mind, built for convenience and repeat consumption, while intellectual nutrition gets harder to find.

A different direction

The alternative is not to reject modern formats or default back to static textbooks. It is to rethink how digital learning is structured so that it keeps the conditions understanding needs. That means coherent narratives rather than isolated fragments, progression that builds over time, space for reflection, and mechanisms that force recall and application rather than passive recognition. It means treating learning as something that unfolds, not something that is skimmed.

This is where approaches like ExpertEdge point in a different direction, taking structured, long-form content and reshaping it into courses without stripping out the depth that gives it value. Instead of flattening books into disconnected assets, it builds around their structure, layering video, assessment and interaction in a way that reinforces the original material rather than diluting it. The aim is to make learning more navigable without losing substance, not to make it shorter.

If eLearning is to avoid becoming the fast food of education, it needs more of this thinking. Systems that respect attention but do not reduce learning to whatever is easiest to measure. Otherwise the trajectory is already clear: more engagement, more consumption and less understanding.

For the full framework that sits behind this argument, see our pillar guide on multimodal learning content for engineering teams.