TRANSFORMING UNIVERSITY EDUCATION WITH GENERATIVE AI: STRATEGIES, CHALLENGES AND IMPACTS

Generative artificial intelligence (GenAI) is rapidly shifting the contours of university teaching, learning, and administration.To identify concrete pathways for responsible adoption, we conducted 12 in-depth interviews with lecturers, students, and senior administrators at three research-intensive Chinese universities.Reflexive thematic analysis uncovered six inter-locking themes: GenAI as a pedagogical co-designer that accelerates content creation; heightened student engagement through low-stakes, iterative exploration; assessment redesign as a more effective integrity strategy than detection-and-deterrence; persistent digital-equity gaps driven by hardware, bandwidth, and licensing costs; staff capacity as the critical bottleneck, with structured "prompt-engineering" micro-credentials proving particularly effective; and the need for agile, participatory governance to keep pace with fast-moving tools.Taken together, the findings show that GenAI's transformative promise materialises only when technological affordances are matched by inclusive infrastructure, professional learning, and transparent policy.We therefore propose a three-pillar framework for institutions: i) pedagogical co-design: embed GenAI in curricula via task redesign and reflective documentation of AI use; ii) equitable enablement: invest in campus-wide compute resources, subsidised licences, and student support to prevent new digital divides; and iii) agile governance: establish multi-stakeholder committees that iteratively refine ethical guidelines, data-privacy safeguards, and citation norms.This study extends socio-technical theories of educational technology by linking micro-level user practice to macro-level organisational policy, and offers actionable recommendations for policymakers, educators, and technologists to cultivate a future-ready, ethically grounded learning ecosystem.