I want to share with you a story of struggle. Well, actually, of two kinds of struggle.
My father completed his PhD at the University of Utah in the early 1970s. For his dissertation, he ran a multivariate regression analysis on genealogical records to determine the impact of micro- and macro-economic conditions on family size.
He accomplished this on one of the most advanced computers at the time. His method? Literally punching out little rectangles in dozens of stiff paper cards that represented his instructions and feeding the stack into the computer. My father was a lowly graduate student, and since the demand for computing time at the university was sky high, he had to run his analysis in the middle of the night. He spent many nights punching cards and running them through the machine. Even a single mispunch would cause the entire program to stop running and require painstaking troubleshooting, re-punching, and another night at the computer lab.
The late nights in the lab make for a heroic story of academic persistence, but they didn’t add value in intellectual terms. Wrangling punch cards did not make the regression analysis more insightful. Waiting until 2 a.m. did not deepen his understanding of economic cycles or demographic behavior.
Unproductive vs. Productive Struggle
The soul-sapping sleep deprivation and endless paper punching that stood between my father and his goals represents the first kind of struggle in my story: Unproductive struggle. Unproductive struggle is intellectual effort we expend in the pursuit of a learning goal that is challenging, unavoidable, and adds no value to the intellectual outcomes we are pursuing.
The real intellectual challenge lay in deciding which variables belonged in the model, determining how to represent economic conditions over time, and interpreting coefficients that only partially told the story. This is the second kind of struggle: Productive struggle.
Productive struggle is the intellectual effort a learner expends to make sense of concepts–to figure something out that is not immediately apparent. This struggle leads to growth and insight. It builds judgment, expertise, and understanding.
What is frustrating about my father’s story in hindsight is not the difficulty of what he was learning–it should have been difficult. What’s frustrating is that so much of my father’s limited time and cognitive energy was consumed by unproductive struggle–barriers that had no value related to learning statistical reasoning. Had those barriers been lifted, he would not have learned less. He would have had more capacity to engage deeply with the questions that actually mattered. He would have had more capacity for the productive struggle that leads to meaningful learning.
The goal of learning has never been to make learning easy. It is to make it meaningful.
Beyond Cognitive Laziness
When it comes to AI in schools, some educators fear that AI will lead to learning becoming too easy. This is referred to as “cognitive laziness.” The assumption is that we will offload our thinking to AI and, eventually, lose our ability to think critically. There are some early studies that indicate this may be happening when generative AI is used by students in unstructured ways, though other studies indicate that is not necessarily the case when AI is put to use in support of specific pedagogical strategies. Regardless, this tradeoff is a risk with any technology that makes our mental work more efficient, and AI is uniquely adept at taking on cognitively demanding tasks.
But ceding our reasoning power to AI isn’t a foregone conclusion. And simply not using AI in learning settings doesn’t have to be our solution for preserving our mental capacities.
The fundamental danger to our intellect is not that technology reduces struggle. The danger is that we fail to distinguish between the productive struggle that grows understanding and the unproductive struggle that merely exhausts it. Just as better computing tools would have freed my father from punching cards without removing the intellectual rigor of his work, today’s tools–including AI–have the potential to offload unproductive struggle while preserving, and even amplifying, the productive struggle that is central to learning. In this way, technologies like AI can actually open up more time and space for productive cognitive struggle.
Here’s an example: When reading comprehension is not the goal of a lesson but a necessary prerequisite–like when a student is reading an article to understand the cause of the French Revolution–AI tools can adjust reading levels on the fly to assist learners who are below grade level or for whom English is not their first language. This allows them to focus on understanding historical causation and forming their own interpretation rather than decoding the text.
Most of the assignments we give students contain a mix of productive and unproductive struggle, and we are not always very intentional about which is which.
Refining Rigor
So what does this mean for educators who are grappling with how to help students use AI effectively?
First, we need to remind ourselves and help our students understand that the goal of learning has never been to make learning easy. It is to make it meaningful. We must ensure that learners are spending their time wrestling with big ideas, not battling logistics or bogged down by rote tasks.
Second, educators need to face a hard truth: most of the assignments we give students contain a mix of productive and unproductive struggle, and we are not always very intentional about which is which. In fact, under crushing time and resource pressure, we can become unreflective about that distinction. We inherit assignments, reuse problem sets, and value “rigor” without always asking where the rigor actually lies.
If AI forces us to confront that, it may be one of the most productive disruptions education has experienced in decades.
Making the distinction between productive and unproductive struggle is hard work. It requires educators to slow down, examine assignments with fresh eyes, and be honest about where healthy struggle actually happens and what parts of the task are just logistically difficult. For instance, requiring students to format citations manually may feel rigorous, but the cognitive work of formatting has little to do with the intellectual work of evaluating sources and integrating evidence into an argument. This shift requires us to redesign tasks, rethink assessments, and sometimes let go of practices that feel rigorous but don’t meaningfully deepen understanding. In other words, it requires cognitively demanding work from us. But it is also deeply meaningful work.
If we do this well, AI won’t hollow out learning; it will sharpen it. It will give students more space to wrestle with ideas instead of mechanics, more time to interpret instead of transcribe, and more opportunity to make active sense of the world. It will give us a chance to be far more intentional about the kind of struggle we ask students to engage in.
In the end, AI won’t decide whether our students experience cognitive laziness or cognitive growth. We will decide by how we design assignments and assessments and by the choices we make about which AI tools to adopt and how we choose to use them. This is our chance to weed out the punch cards in our practice, opening up more time for students to struggle over things that truly matter.
