Struggle Is Not a Bug
It’s a Feature: The Neuroscience of Doing the Hard Work in Learning
When I talk with educators about AI in the classroom, one concern keeps surfacing: If AI can do the heavy lifting, what happens to the student’s own thinking?
In Chapter 6 of The AI Educator, I write about how tools like ChatGPT risk turning “an opportunity to wrestle with complex ideas into a button-click exercise in outsourcing thought.” That line has resonated with many educators because it speaks to something deeper than academic integrity. It’s about what it means to learn.
We often tell students, “Do the hard work.” But what’s really happening in the brain when learning feels hard? And why does that discomfort matter?
The Neuroscience of Struggle
When learners tackle difficult material, the prefrontal cortex, the brain’s control centre for working memory and planning, activates intensely. This region keeps information “in play,” suppresses distractions, and coordinates reasoning (Miller & Cohen, 2001).
Recent imaging studies show that when people expect a task to be difficult, the prefrontal cortex sustains activation longer, indicating greater mental effort (Master. S et. al., 2024).
In short, the brain quite literally works harder when it anticipates challenge.
This kind of “cognitive strain” isn’t wasted energy; it’s when the brain is forming new neural connections. Cognitive load theorists describe learning as the management of mental effort within the limits of working memory. When tasks are slightly beyond our comfort zone, the brain integrates new information with prior knowledge. This is what psychologists call germane load and it strengthens long-term understanding (Paas & Sweller, 2014).
So when students struggle, their brains are reorganising networks and encoding deeper representations. The feeling of effort is actually evidence of growth.
Why Effort Feels Uncomfortable
Neuroscientists have also found that the brain treats cognitive effort as a kind of cost. The anterior cingulate cortex weighs how much effort a task will take against its perceived reward (Kool & Botvinick, 2018). That’s why difficult work feels aversive, not because students are lazy, but because their brains are managing limited resources.
Yet that very resistance marks the edge of development. Pushing through that “ugh” moment engages dopaminergic reward circuits once progress is made. Over time, the brain learns to associate effort with accomplishment. In other words, struggle rewires motivation itself.
Designing for the Hard Work
What does this mean for classrooms navigating the rise of AI? If AI smooths away all friction, we risk losing the conditions that make learning transformative.
Here are a few strategies to keep the “good struggle” alive:
Make thinking visible. Ask students to submit drafts, prompt histories, or reflection notes explaining their decisions. Assessment becomes a record of thought, not just output.
Reframe difficulty as progress. Remind learners that the “stuck” feeling signals new neural growth, not failure.
Balance challenge and support. Tasks that are too easy don’t engage the prefrontal cortex; too hard, and motivation collapses. Scaffold so students operate in the “productive difficulty” zone.
Reward reasoning, not polish. Grade the clarity of decision-making, evidence of revision, and depth of reflection.
When students understand that effort is where learning happens, frustration transforms into persistence.
Try this:
Next time you teach with AI, ask students to include a short reflection:
“What part of this task felt hard and how did you push through it?”
That’s where the most powerful learning insight usually hides.
AI can generate, summarise, and polish, but it can’t do the truly human work of deciding what matters. When students outsource all the heavy lifting, they miss the very process that builds intelligence: sustained attention, reflection, and conceptual struggle.
As I argue in The AI Educator, the goal isn’t to reject AI but to design learning experiences where AI assists without erasing the thinking journey. The hard work is the learning and it’s that part that makes us more human.
References
Kool, W., & Botvinick, M. (2018). Mental labour. Nature human behaviour, 2(12), 899-908.
Master, S. L., Li, S., & Curtis, C. E. (2024). Trying harder: how cognitive effort sculpts neural representations during working memory. Journal of Neuroscience, 44(28).
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual review of neuroscience, 24(1), 167-202.
Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. In The Cambridge handbook of multimedia learning (pp. 27-42). Cambridge University Press.