Beyond Constructive vs. Disruptive: An Empirical Typology of Student Engagement with Generative AI, Challenging Binary Constructs in Academic Integrity

The rapid integration of Generative AI (GenAI) in higher education has triggered a paradigm shift from initial disruption to widespread normalisation, yet the specific nature of student engagement with these tools remains underexplored. Based on the adapted Extended Technology Acceptance Model (TAM3), this study aims to explore and investigate whether university students utilise GenAI primarily as “Task Implementors” to automate academic workload or as “Learning Catalysts” to deepen cognitive understanding. A quantitative survey was given to 433 undergraduate and graduate students in three leading universities one in Canada and two in Egypt. Cluster analysis identified a dominant group of “balanced integrators” who pragmatically leverage AI automation to complement and perfect their work, rather than replacing it. The results further demonstrate that GenAI adoption is mainly driven by perceived suitability (functional utility) rather than desirability (hedonic enjoyment), confirming that students view these tools as essential professional instruments rather than entertainment. These findings suggest that traditional restrictive policies are rendered obsolete by the practicality of AI technology, necessitating a shift toward process-oriented assessment strategies that acknowledge AI’s permanent role in the academic ecosystem. The study findings have implications for the future of curriculum design, pedagogical strategy, developing assessment and institutional policymaking in higher education.