The Efficiency Trap: Fragmented Curricula and AI-Driven Surface Learning in Higher Education

Abstract

The rapid integration of artificial intelligence (AI) into higher education has generated widespread institutional anxiety, largely addressed through technological surveillance and punitive anti-cheating frameworks. This position paper challenges the dominant narrative by arguing that the erosion of educational quality stems not from AI itself, but from the structural disarray of the contemporary university. Escalating workloads, compressed deadlines, and the fragmented tempo of academic life—where lectures, laboratories, assessments, and administrative demands frequently converge—undermine students’ capacity for sustained intellectual engagement. Within such conditions, delegating cognitive and procedural tasks to AI becomes less an expression of laziness than a rational strategy of academic survival, transforming learning into a model of quasi-productivity characterized by transactional “submit-and-forget” practices. Moreover, this form of digital alienation extends beyond students to faculty members, who increasingly operate within screen-mediated Learning Management Systems (LMS) that privilege metric surveillance over meaningful human interaction. While hypertext historically revolutionized knowledge navigation, modern AI-mediated interfaces risk conflating the mere illusion of information access with true comprehension. Drawing on empirical observations of student attitudes toward intellectual autonomy and educational responsibility, this paper advocates a shift away from algorithmic regulation toward AI-resilient pedagogical design. Ultimately, we contend that sustainable educational innovation does not require abandoning pedagogical heritage; rather, universities must recover enduring traditions of structured academic pacing, intellectual continuity, and face-to-face dialogic mentorship in order to reclaim higher education as a space for authentic human thought.