Mapping AI-Driven Pedagogical Interventions and Competency Assessment Tools in Public Health Workforce Upskilling: A Scoping Review of Ethical, Clinical, and Technical Frameworks

• Abey Litty

  Texas University

  Author

The integration of artificial intelligence (AI) into public health education has catalyzed transformative shifts in pedagogical approaches and competency assessment methodologies, yet the landscape remains fragmented with limited synthesis of ethical, clinical, and technical frameworks. This scoping review maps existing literature on AI-driven pedagogical interventions and competency assessment tools for public health workforce upskilling, identifying key frameworks, implementation patterns, and research gaps. Following Arksey and O'Malley's methodological framework with PRISMA-ScR guidelines, we systematically searched PubMed, Scopus, IEEE Xplore, ERIC, and Web of Science for studies published between January 2015 and May 2026. Of 1,185 initially identified records, 26 studies met inclusion criteria. The XGBoost machine learning model demonstrated superior predictive performance for identifying training needs, achieving an AUC of 0.702 and accuracy of 89.4% in stratified competency classification. Thematic analysis revealed four distinct competency patterns among public health personnel: novice (25.3%), public health experts (15.1%), potential expansion talents (24.7%), and versatile talents (34.9%). Key findings indicate that AI-powered adaptive learning platforms and simulation-based assessments show promise for scalable competency development, yet significant gaps persist in communicative literacy assessment, ethical framework integration, and validation across diverse workforce settings. The review synthesizes a comprehensive competency framework comprising functional, critical, and communicative literacy dimensions, alongside a staged competency model for AI literacy development. Practical implications include actionable recommendations for curriculum reform, institutional capacity building, and equitable AI implementation strategies to foster a digitally competent public health workforce equipped for emerging challenges.

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Copyright (c) 2026 Abey Litty (Author)

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