Preparing future-ready public health professionals: a blended, AI-integrated pedagogical innovation

Abstract

INTRODUCTION: In the evolving landscape of higher education, especially in public health, innovative pedagogical approaches are essential to promote deeper learning and real-world readiness. This study explored a blended, mixed-method teaching intervention combining generative AI, field exposure, expert interaction, and student-led presentations to teach occupational health to postgraduate public health students in India. METHODS: A group of 22 Master's in Public Health students participated in a structured learning sequence including classroom lectures, an industrial site visit, interaction with an occupational health expert, and hands-on use of ChatGPT to explore occupational hazards and prevention strategies. Students worked in groups to generate structured outputs, critically analysed AI-generated content, and presented findings. Faculty facilitated the session and reinforced key concepts. Student feedback was collected using a semi-structured questionnaire comprising Likert-scale and open-ended questions. RESULTS: Quantitative analysis revealed high engagement (mean = 4.3), improved ability to structure content (mean = 4.7), and appreciation for AI's role in learning (mean = 4.1). Students also reported increased confidence in critically evaluating AI-generated information. Thematic analysis of qualitative responses highlighted usefulness, practical relevance, enhanced interaction, and the value of field exposure as key strengths. It also revealed, areas for improvement, such as time management and AI training. CONCLUSION: This blended, student-centred model combining experiential learning and generative AI proved effective in promoting engagement, critical thinking, and digital literacy. The approach offers a replicable framework for pedagogical innovation in health sciences education.

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