Artificial intelligence in public health education: current evidence, competencies, and a framework for responsible integration

Abstract

Public health practice and education are being profoundly transformed by artificial intelligence (AI), yet formal AI literacy training in academic programs remains lacking. This narrative review synthesizes literature identified through a structured search of PubMed, Scopus, Web of Science, and Google Scholar for publications issued between January 2020 and June 2026. The narrative synthesis draws on 21 publications, including empirical studies, reviews, perspectives, educational frameworks, and authoritative guidance documents. The review underscores a significant gap between the widespread use of informal AI and the limited availability of formal curricula, with most evidence drawn from medical education rather than public health-specific settings. Key applications include adaptive learning platforms, AI-enhanced simulation, and research training support. We propose the Public Health AI–Education Integration Framework comprising nine domains: learner and curriculum needs assessment; AI literacy foundation; AI tool selection and educational alignment; public health dataset and case integration; adaptive learning and simulation delivery; human-in-the-loop supervision; bias, equity, and ethical auditing; assessment and academic integrity; and feedback, monitoring, and curriculum refinement. The framework is proposed as a conceptual model requiring prospective validation, intended to move beyond ad hoc AI use toward structured, competency-based training that strengthens analytical reasoning, ethical judgment, and equity-oriented practice in AI-enabled public health systems.