(untitled) (The Science Post, 2026)
The rapid proliferation of artificial intelligence (AI) in public health practice has outpaced the development of formal training competencies in educational curricula, particularly within low- and middle-income countries (LMICs). Despite increasing adoption of AI-driven epidemiological modeling, predictive analytics, and health communication tools, a persistent knowledge gap exists in structured, competency-based AI training for public health students and professionals (Semi et al., 2026). This study employed a mixed-methods comparative design to assess curriculum readiness and workforce adaptability for AI integration across 47 public health institutions in six LMICs (India, Nigeria, Kenya, Bangladesh, Nepal, and Ethiopia). Quantitative surveys administered to 849 public health faculty members and 1,204 final-year students revealed that only 12.3% of institutions offered formal AI competency training, while 89.4% of respondents identified curriculum reform as an urgent priority. The AI literacy assessment demonstrated that students scored significantly lower on applied AI problem-solving tasks (mean 41.2%, SD 18.7) compared to foundational public health competencies (mean 76.8%, SD 12.4), t(1203) = 28.4, p < 0.001. A five-pillar framework encompassing technical foundations, ethical and regulatory literacy, experiential learning, governance and policy, and equity and access is proposed as a replicable model. The findings underscore the critical need for systematic integration of AI competencies into public health curricula, with particular attention to faculty development, infrastructural constraints, and ethical safeguards in resource-limited settings. Practical implications include structured faculty training pathways, open-access AI literacy tools, and policy recommendations for institutional curriculum reform.