This study examines how generative artificial intelligence functions as a governance stressor within higher education institutions and investigates how ethical boundary ambiguity, perceived academic risk and institutional policy clarity shape support for governance reform.
A mixed methods design was employed. The quantitative phase involved 228 participants comprising 142 undergraduate students and 86 lecturers. A structured survey measured perceptions across four governance related domains. Multiple regression analysis identified predictors of reform support. The qualitative phase included semi structured interviews to explore implementation level tensions and policy interpretation dynamics.
Ethical boundary ambiguity and perceived academic risk significantly predicted support for governance reform. Institutional policy clarity was negatively associated with reform demand, indicating a stabilising effect. Qualitative findings revealed that inconsistent assessment expectations and uneven policy communication translated abstract governance into everyday uncertainty. Results suggest that reform pressure emerges from misalignment between policy articulation and pedagogical practice rather than from misconduct alone.
The study proposes a governance alignment model that reframes academic integrity governance as a problem of communicative coherence linking policy clarity, ethical boundary definition and perceived academic risk. The findings contribute empirical evidence to institutional level debates on sustainable AI integration in higher education.
