University student perspectives on generative AI: reconfiguring competence, fairness, and authorship in academic work

This study examines how university students engage with generative AI in their everyday academic work – not merely to complete assignments more efficiently, but to navigate deeper questions about competence, authorship, and fairness. Drawing on interview data from 17 students across disciplines in Hong Kong, we explore how learners selectively integrate GenAI into tasks such as essay writing, literature reviews, and coding. Rather than treating AI as a neutral tool, students actively negotiate which tasks to delegate, how to preserve their voice, and what academic integrity means in different classroom contexts. Our findings identify three interrelated shifts: (1) a redefinition of competence from individual task performance to dialogical judgment in using AI; (2) a renegotiation of authorship as students balance linguistic fluency with the desire to retain ownership of ideas; and (3) an emerging understanding of fairness that emphasizes human – AI collaboration and contextual discretion over universal detection rules. These insights complicate output-oriented views of learning and invite a rethinking of educational purpose across Biesta’s three dimensions – qualification, socialization, and subjectification – in light of how students negotiate competence, norms, and authorship in AI-mediated academic work. We argue that students are not simply adapting to AI but actively reshaping what academic work and learning mean in the age of generative AI. These findings suggest that institutions should revisit their assessment practices, AI policies, and writing support frameworks to better align with the evolving realities of student learning in AI-mediated environments.