Students' Engagement with Generative AI in Higher Education: A Mixed-Methods Study
Abstract
Generative AI (GenAI) is reshaping tertiary education by facilitating personalized learning, enhancing engagement and improving academic outcomes. However, it simultaneously raises ethical concerns regarding data privacy, academic integrity, and prospective erosion of teacher-student interaction. This study examines students' engagement toward GenAI in higher education through a mixed-methods case study at the Institute of Education and Research (IER), University of Dhaka, involving a survey (N=166) and focus group discussions (n=18). Findings reveal a state of informed ambivalence: students consistently use GenAI for complex academic tasks yet acutely perceive a productivity-skill trade-off, weighing immediate efficiency against long-term erosion of critical thinking, creativity, and independent problem-solving. This ambivalence is conditioned by infrastructural constraints, policy ambiguities and pedagogical traditions configuring a distinctive adoption context where functional reliance precedes philosophical acceptance. Theoretically, this study extends the Technology Acceptance Model (TAM) by incorporating “Concerns” as a moderating construct and identifying a “productivity-skill trade-off” as an evaluative dimension influencing attitudes and behavioral intention. This study provides a policy-relevant case from the Global South and insights on the need for guided and context-sensitive integration of GenAI in higher education, contributing to the broader discourse on bridging the gap between current reality and transformative ambition.
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