Why Students Can’t Simply “Choose” Engagement  

David Trend 

In recent years campuses everywhere have been reporting alarming levels of absenteeism and underperformance. It’s certainly something I hear a lot from colleagues at UC Irvine, many of whom are left scratching their heads about exact causes. And of course, it’s typical to lay the blame on students rather than anything relating to instruction or the university itself. Affect theory can help in finding better answers.

Let’s start with how faculty talk about engagement. The typical focus is nearly always on learners themselves. Students choose to scroll or skip class. They take shortcuts or use AI. Of course these behaviors frustrate educators. But they are little more than symptoms of what’s really going on. Most learners have studied hard to get into college and borrow heavily to remain there. The vast majority want to do the work, care about the material, and value courses. But they still can’t bring themselves to fully function. They may show up, take notes, say the right things, and yet still feel nothing. That is not simply a decision. It ‘s a mental state. And often a defensive one.

Classrooms operate as affective machines, creating a vast array of feelings and physical reactions in students. While many educators blame educational problems on motivation or attention lapses, they in fact stem from the systemic creation of negative affect by policiesof the kind critiqued in these pages.  Meritocratic institutions produce conditions of “affective scarcity,” in which success, recognition, and belonging are limited resources to be fought for individually rather than collectively.   Learners internalize beliefs that their struggles stem from personal deficits and nothing else. The resulting shame is so normalized that failures become quietly accepted, making studentsagents of their own oppression. They focus on improving themselves, measuring their success through performance metrics, and aligningwith institutional standards. Today’s pervasive culture of mistrust adds to the degradation of students’ learning experiences. Learners are monitored by LMSs tracking online behavior, exam proctoring software, and AI-detection algorithms presuming dishonesty. 

Busy faculty can miss these subtleties because they read behavior as a transparent matter. They suspect that students procrastinate because they don’t care. Missing class means they must be lazy. An anxious appearance signals fragility.  AI use is simply cheating. While these interpretations may make a certain kind of sense, they can misinterpret the learner’s internal experience. Students don’t simply react in the moment. Rather they anticipate the classroom, act on their fears, prepare for potential outcomes. Their bodies react when instruction generates anxiety and defensive reactions, often in unconscious ways.

Freud called education an “impossible profession.” In saying this, he didn’t mean that that education is ineffective. Freud was describing the often unknown and uncontrollable outcomes of learning encounters, many of which aren’t fully transparent to students themselves. This means that faculty can’t always know how students will react to their teaching.[i]  Learners don’t begin courses as blank slates. They arrive with minds preloaded with prior experiences of judgment. Add to this the current mood of campuses today (exhaustion, surveillance, competition) along with the uncertainty of the world outside, and it’s clear that for many students there is no way to see learning as an invitation. Many see college as a test of their worth.  A term like “motivation” hardly can account for all of this in suggesting that students simply need more desire to learn. What learners actually require is less fear and risk of failure.    

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Outsourcing Effort: How AI Affects the Pleasure of Learning

A quiet transformation is taking place in higher education. Students now turn to artificial intelligence not only for answers but for motivation. At the same time, a new generation of effort-sensitive technologies promises to monitor engagement, track persistence, and infer whether learners are truly trying. These systems are beginning to redefine what colleges count as effort and, by extension, what they count as learning. The goals behind these tools are understandable. Designers hope adaptive systems will make education more equitable and responsive by giving students insight into their habits and struggles. At Carnegie Mellon University, for example, Conrad Borchers created Effort-Sensitive AI for Learning to help students visualize their study patterns before frustration sets in. The intention is reflection rather than surveillance.

But once activity is translated into data, the data begin to influence the behavior they claim to measure. A brief pause can register as disengagement. Rapid typing can signal focus. Students eventually realize that unseen systems are interpreting their actions and may begin performing for algorithmic approval instead of thinking for themselves. What used to be an exchange between learner and teacher becomes a loop between student and machine. This shift matters because effort is no longer understood through experience but through metrics. In a traditional classroom, effort lived in rereading, revising, and wrestling with ideas. In digital spaces, it gets recorded as keystrokes, session length, and completion rates. These numbers are useful but incomplete. They capture what is visible and overlook what is internal. Confusion, insight, doubt, and breakthrough moments rarely leave a trace.

Yet once metrics appear on dashboards, they start to define achievement. Courses are compared. Instructors are ranked. Automated interventions are triggered. What cannot be measured slowly stops being valued. Md. Kamrul Hasan has described this shift as the loss of the joy of effort. Writing in the Annals of Medicine and Surgery, he argues that learning’s pleasure comes from its difficulty. Struggle activates the brain’s reward system and strengthens motivation. When AI tools deliver instant solutions, that developmental cycle gets bypassed. Hasan calls the result cognitive outsourcing, a quiet erosion disguised as progress.

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