I want to share a story of struggle. Actually, two kinds of struggle.
My father completed his doctorate at the University of Utah in the early 1970s. For his dissertation, he ran a statistical analysis on genealogical records to determine the impact of certain economic conditions on family size.
He accomplished this on one of the most advanced computers of the time. His method? Literally punching out little rectangles in dozens of stiff paper cards, and feeding the stack into the computer.
My father was a lowly graduate student, and because 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.
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 — the challenging, unavoidable tasks we must perform toward a learning goal, but which add no value to the intellectual outcome.
The real intellectual challenge in my father’s work was in deciding which variables belonged in the model, determining how to represent economic conditions over time, and interpreting the data. This is the second kind of struggle: productive struggle. That is, the 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 that so much of his time and cognitive energy were consumed by the unproductive struggle of punching cards and managing the computer. Without those barriers, he would have had more capacity for the productive struggle that leads to meaningful learning.
Thinking About What Matters
When it comes to AI in schools, some educators fear that it 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. This 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.
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.
Here’s an example: When reading comprehension is not the goal of a lesson but a necessary prerequisite — a student having to read an article to understand the causes of the French Revolution, for example — 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 the history rather than on decoding the text.
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 about the assignments we give students. Many assignments contain a mix of productive and unproductive struggle, and we are not always very intentional about which is which. Under crushing time and resource pressure, we can become unreflective about the distinction between productive and unproductive work. 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 useful disruptions education has experienced in decades.
For instance, requiring students to write citations according to a set format 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, if necessary, let go of practices that feel rigorous but don’t meaningfully deepen understanding.
Sharpening Learning
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 that 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 and open up more time for students to struggle over things that truly matter.
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