AI in Schools: The Fight to Keep Learning Hard

An educator frames the case for AI in classrooms through a deeply personal story: the “punch-card” grind of academic research versus the thinking that actually changes what you understand. The argument is simple—AI should remove unproductive struggle, so stude

There is a particular sound in the memory of one educator: the steady rhythm of punching cards. over and over. while a graduate student works against the clock for access to a university computer. The story is personal. but it lands as a lesson for schools trying to decide what artificial intelligence should do in the classroom.

His father finished 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. It was done on one of the most advanced computers of the time—using a process that. by today’s standards. looks exhausting by design.

He literally punched out little rectangles in dozens of stiff paper cards and fed the stack into the computer. As a “lowly graduate student. ” he faced a brutal reality: demand for computing time at the university was sky high. so he had to run his analysis in the middle of the night. Many nights were spent punching cards and running them through the machine. Then came the fear that sat behind every punch. Even a single mispunch would cause the entire program to stop running. forcing painstaking troubleshooting. re-punching. and another night at the computer lab.

In that way, the educator describes two kinds of struggle. The first is the kind that feels soul-sapping because it is unavoidable, challenging, and doesn’t add to the intellectual outcome: the sleep deprivation and endless paper punching that stood between his father and the real work.

The real intellectual challenge in the dissertation. he says. was deciding which variables belonged in the model. determining how to represent economic conditions over time. and interpreting the data. That is the second kind of struggle—the effort a learner spends to make sense of concepts. to figure out what is not immediately apparent. It leads to growth and insight and builds judgment, expertise, and understanding.

What frustrates him in hindsight is that so much time and cognitive energy were consumed by barriers that had nothing to do with the thinking itself. Without them, his father would have had more capacity for the productive struggle that creates meaningful learning.

That memory feeds directly into the debate over AI in schools, where some educators worry that it will make learning too easy. The term they use is “cognitive laziness”—the fear that students will offload their reasoning power to AI and eventually lose the ability to think critically.

The argument also acknowledges a real risk: giving a tool the ability to take on cognitively demanding tasks can tempt learners to cede too much. But the educator argues that ceding reasoning power to AI is not automatic. and that simply banning AI from learning settings is not the only way to protect mental capacities.

He draws the comparison to his father’s era: better computing tools would have freed him from punching cards without removing the intellectual rigor of the work. In the same spirit. he says today’s tools—including AI—can offload unproductive struggle while preserving. and even amplifying. the productive struggle that sits at the center of learning.

One example is tied to reading comprehension. When reading comprehension isn’t the goal of a lesson but a necessary prerequisite—he gives the example of students reading an article to understand the causes of the French Revolution—AI tools can adjust reading levels on the fly. That kind of support can help learners who are below grade level or for whom English is not their first language. The goal, in his framing, is to help students focus on history rather than decoding the text.

The question then shifts to educators who are grappling with how to help students use AI effectively. The first step. he says. is to remind everyone that the goal of learning has never been to make learning easy. It is to make it meaningful. Students should be spending their time wrestling with big ideas, not battling logistics or being bogged down by rote tasks.

The harder step is confronting how assignments are built. Many tasks. he argues. contain a mix of productive and unproductive struggle. and educators aren’t always intentional about sorting which is which. Under crushing time and resource pressure. teachers can become unreflective—reusing problem sets. inheriting assignments. and valuing “rigor” without asking where the rigor actually lives.

He offers a concrete example: 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. If AI forces educators to look closely at that divide, he argues, the assignments themselves may have to change.

That means redesigning tasks, rethinking assessments, and letting go of practices that feel rigorous but don’t meaningfully deepen understanding.

If schools do it well, his conclusion is that AI won’t hollow out learning—it will sharpen it. Students would get 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 would also create a chance to be more intentional about the kind of struggle students are asked to engage in.

In his telling, AI doesn’t decide whether students experience cognitive laziness or cognitive growth. Educators do—through the design of assignments and assessments, and through choices about which AI tools to adopt and how they will be used.

His father’s nights punching cards may be gone, but the lesson is still vivid. The stakes, for students and teachers alike, are whether technology becomes another layer of needless burden—or a way to weed out the punch cards and open up more time for learning that truly matters.

Joseph South is chief innovation officer at ISTE+ASCD.

AI in education cognitive laziness productive struggle unproductive struggle reading comprehension support assessment design ISTE+ASCD student learning