Struggle is a fundamental part of the learning journey, yet not all struggles yield meaningful results. This piece reflects on two types of struggles through the lens of my father’s experience as a graduate student.
In the early 1970s, my father completed his doctorate at the University of Utah. As part of his dissertation, he conducted a statistical analysis of genealogical records to examine how different economic conditions influenced family size.
To conduct his research, he utilized one of the most advanced computers available at the time. His tasks involved physically punching out small rectangles in stiff paper cards and feeding them into the machine.
As a graduate student, my father faced immense competition for computing time at the university, which forced him to run his analysis during the late-night hours. Many were the nights spent punching cards and managing the computer, where even a single error could halt the entire program, necessitating extensive troubleshooting, re-punching, and yet another night in the lab.
Unproductive vs. Productive Struggle
The overwhelming fatigue and continuous card-punching that obstructed my father’s path illustrate the first type of struggle: unproductive struggle. These are the unavoidable tasks we must undertake in pursuit of learning, yet they offer little intellectual benefit.
On the other hand, the true intellectual rigor of my father’s work lay in choosing the right variables for his model, representing economic conditions over time, and interpreting the resulting data. This constitutes the second type of struggle: productive struggle. It involves the effort required to understand complex concepts and make connections that are not immediately obvious. This form of struggle fosters growth, insight, judgment, and expertise.
What is particularly frustrating about my father’s story is that so much of his time and cognitive resources were consumed by unproductive struggle, which inhibited his ability to engage in the productive struggle essential for meaningful learning.
Thinking About What Matters
In modern education, some educators worry that the introduction of AI could lead to an overly simplistic learning experience, often referred to as “cognitive laziness.” The concern is that we might rely too heavily on AI, sacrificing our ability to think critically. This is a valid risk associated with any technology that makes our cognitive tasks more manageable, and AI excels at tackling challenging cognitive demands. However, relinquishing our reasoning skills to AI is not inevitable, nor must abstaining from its use be our only strategy to maintain mental proficiency.
Just as more advanced computing tools could have relieved my father from the drudgery of card punching without compromising the intellectual rigor of his research, today’s tools, including AI, can alleviate unproductive struggles while preserving and even enhancing the productive struggles central to learning.
For example, if reading comprehension is a prerequisite for understanding a lesson—such as reading an article to grasp the causes of the French Revolution—AI can adjust reading levels in real time to assist students who may be below their grade level or for whom English is a second language. This shifts the focus from decoding the text to engaging with the material.
Refining Rigor
What does all this mean for educators trying to effectively integrate AI in their classrooms?
First, we must remind ourselves and guide our students to recognize that the aim of learning is not merely to simplify the process but to make it meaningful. We should ensure that our students engage with significant concepts rather than getting bogged down by logistical issues or rote tasks.
Second, educators need to confront the reality of the assignments we assign. Many tasks contain a blend of productive and unproductive struggles, and we often lack clarity on which is which. In the face of time and resource constraints, we can sometimes overlook this essential distinction. We may inherit assignments, reuse problem sets, and prioritize rigor without scrutinizing where that rigor genuinely lies.
If AI prompts us to reevaluate our approach, it could represent one of the most valuable shifts in education we’ve seen in decades.
For instance, while requiring students to format citations in a particular style might seem rigorous, the cognitive effort involved in formatting is far removed from the intellectual endeavor of evaluating sources and synthesizing evidence into an argument. This realization calls for a redesign of tasks, a rethinking of assessments, and a willingness to discard practices that may feel rigorous but do not significantly enhance understanding.
Sharpening Learning
If executed wisely, AI will not dilute learning; rather, it will refine it. It will give students more opportunities to engage with ideas rather than mechanics, more time to interpret rather than transcribe, and more chances to make sense of the world. This presents us with the opportunity to be more deliberate about the type of struggle we encourage in our students.
Ultimately, whether our students fall into a pattern of cognitive laziness or experience genuine cognitive growth rests on us. Our choices regarding assignment design and the AI tools we select and employ will shape their educational experience.
This is our moment to eliminate the unproductive struggles and create room for students to engage with what truly matters.
