Trajectories of AI policy in higher education: Interpretations, discourses, and enactments of students and teachers

Generative artificial intelligence (GenAI) in higher education has introduced a spectrum of ethical challenges, significantly impacting learning outcomes, pedagogies, and assessments. Based on the experiences and perspectives of students and teachers at a research-intensive university in Hong Kong, the study draws on qualitative interview data with 58 undergraduate and graduate students and 12 teachers conducted in early 2025. Through the concept of policy trajectories (Ball, 1993; Ball et al., 2012), the research analyses the interconnections between material contexts and discursive constructions in how AI policies (and their absence) are framed, interpreted, enacted, and resisted. The findings reveal general concerns about academic integrity, fairness, equity, privacy, and data security, including specifically the invisible labour in dealing with ambiguous policies, uneven enforcement strategies, loopholes to avoid detection, disparities in access to state-of-the-art tools, and the cognitive and other developmental impacts due to overreliance on GenAI tools. Institutional ambiguity in policy supported experimentation and the appearance of progress, but risked individualising failure on teachers and students. Some actionable insights for university leaders and policymakers, teaching development centres, and individual teachers and programme coordinators include clearer messaging, the need for adaptive policies and guidelines with ongoing student and teacher participation, availability of digital libraries of toolkits, case studies and other resources, building in early “failure experiences”, and exposing students to authentic real-world applications and encounters to cultivate awareness on the limitations of GenAI. Ultimately, policy responses need to be both contextually and pragmatically sensitive, requiring on-the-ground experimentation and care by teachers. • Policy ambiguity leads to diverse interpretations and narratives, and anxieties and inequities. • Accessibility of AI tools and detection systems shapes enforcement and enactment outcomes. • Cultural contexts shape students' interpretations and enactments of AI policy. • Disjunctive narratives and experiences create productive resistance for students to GenAI use.