Integrating Generative AI into public health education: a mixed methods evaluation of student experiences

Introduction: As Generative Artificial Intelligence (GenAI) continues to influence pedagogical practices in higher education, prevailing discourse has largely focused on issues of assessment integrity, often overlooking student perspectives. Yet, understanding student voices is essential to ensure that AI-enhanced teaching and assessment remain student centred, equitable, and aligned with intended learning outcomes. This study aimed to explore postgraduate health science students' perceptions and experiences of the structured integration of GenAI into teaching and assessment within the Epidemiology and Principles of Research unit at the University of Canberra. Methods: A mixed-methods study was conducted using pre- and post-intervention surveys administered to all enrolled students, with 78 participants completing the evaluation. The intervention involved the scaffolded integration of GenAI into a critical appraisal assessment. Quantitative data were analysed using descriptive statistics and Fisher's exact test to assess changes over time. Qualitative data from open-ended responses were analysed using thematic analysis following Braun and Clarke's six-phase approach. Results: < 0.001). Thematic analysis identified five key themes: tensions between automation and authentic learning; prompt literacy as a new academic skill; GenAI as a support for metacognitive engagement; ethical ambiguity and cognitive dissonance; and future-oriented learning. Conclusion: The findings highlight the importance of thoughtful pedagogical designs that center student voice, support critical engagement, and prepare learners for responsible and reflective use in future professional practice.