As a child, my family took an annual road trip from Queens, New York, to New Ringgold, Pennsylvania. My brother memorized the map of our route and could narrate us there. I was not as committed but still had a general sense of the path.
Last week, I opened my GPS to get home because I would get lost without it. I was only 10 minutes away.
Somewhere between those family trips and this moment, I lost the spatial memory that came more naturally. Mostly, because I no longer need it.
Humans naturally avoid cognitive effort. Studies show that when people can take an easier path, they do (Kool et al., 2010). From an evolutionary standpoint, effort avoidance helps us survive. But when technology like AI and search engines makes learning effortless, our brains stop practicing how to learn.
Psychologists have studied this tendency to rely on external tools and call it cognitive offloading. Memorizing where to find information instead of the information itself leads to what researchers call the “Google effect” (Sparrow et al., 2011). That’s why we can remember the name of an article but not its argument. The brain adapts to easy access and offloads what no longer feels essential.
When I started my talent development career in 2017, I devoured every training book I could find—The 10-Minute Trainer, 150 Ways to Teach It Quick and Make It Stick, Telling Ain’t Training, and The Art and Science of Training. I highlighted pages, hoping to find the secret formula for helping adults learn (and to our point, I remember much of what I learned).
The takeaway from all of them was the same: Adults learn by doing. Kolb’s experiential learning theory, Bloom’s Taxonomy, and decades of research on memory all confirm that learning sticks when people retrieve, apply, and reflect on knowledge in context.
Cognitive psychology shows why this works:
- Retrieving knowledge strengthens long-term memory more than rereading it (Roediger & Karpicke, 2006),
- spacing out learning helps retention (Cepeda et al., 2006),
- generating even imperfect answers improves recall (Slamecka & Graf, 1978),
- and the harder something feels, the deeper it tends to stick (Bjork, 1994; Kang, 2016).
The problem is that we now live in a world designed to remove friction. In efforts to learn faster or work smarter (and, frankly, be more perfect), we use tools that sometimes do the work for us. When that happens, the mental muscles we stop using start to weaken.
Let’s use an old principle to solve this problem. Cognitive load theory (Sweller, 1988) gives us a framework. This theory explores offloading extraneous load (tasks that don’t build skill) while preserving germane load (the kind that strengthens understanding).
Here’s what that balance can look like in practice:
- A nurse might use AI to handle charting but still take the time to study patterns in patient data.
- A manager might ask a chatbot to summarize meeting notes, then spend their energy thinking about decisions those notes inform.
- A startup founder might use AI to brainstorm possibilities but still evaluate and tailor the top two that best align with their strategy.
- A lawyer could offload case summaries but still write their own opening and closing arguments.
The goal is to lighten the administrative load while keeping the thinking work human.
AI can also support the time real learning takes by tracking progress or offering reminders, but it can’t internalize the habit for us. Time and effort is what builds the scaffolding for future knowledge. Each time we resist the temptation to offload too early, we reinforce our ability to think critically, retrieve information, and connect ideas. Those moments shape new wiring in the brain.
The challenge for the everyday leader is to design systems, teams, and workspaces that preserve that kind of engagement and make room for the discomfort that comes with learning.
So here’s a question: Where can you allow for more cognitive effort in your work?
References
Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. P. Shimamura (Eds.), Metacognition: Knowing about knowing. MIT Press.
Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380.
Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights From the Behavioral and Brain Sciences, 3(1), 12–19.
Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and the avoidance of cognitive demand. Journal of Experimental Psychology: General, 139(4), 665–682.
Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255.
Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning and Memory, 4(6), 592–604.
https://doi.org/10.1037/0278-7393.4.6.592
Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
