Agentic education · Learning science

Actionable companion · Feedback & scaffolding

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

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

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.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Design requires full-text appraisal

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Limitations and uncertainty · source passage

Public health students require a progressive, level-appropriate set of AI competencies. At the foundational level, they should understand basic AI terminology, common tool types, the generative-predictive distinction, and the limitations of AI outputs, including recognizing hallucinations, unsupported claims, and fabricated references (1, 7, 15). Medical education frameworks similarly emphasize foundational knowledge of AI and data (16). At the applied level, students should use AI for public health tasks like data exploration, communication drafting, literature searching, code support, and scenario analysis (2, 3), while also verifying outputs, explaining assumptions, and documenting AI use (7, 15). The blended pedagogical intervention described by Parmar et al. demonstrated that students can effectively use ChatGPT to explore occupational hazards and prevention strategies and to critically analyze AI-generated content (3). At the critical appraisal level, students should be able to evaluate AI tools and AI-based studies by asking whether a model was validated, which population was used for training, whether performance differs across subgroups, whether the model is interpretable, and whether the tool is appropriate for the intended public health context (1, 7, 17). Critical appraisal of AI output has been identified as a recurring area of emphasis in competency frameworks (16).

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Hisham Arab; Ghazal Assaad Mirdad; Maha Abulfetoh Mohamed Abd-Allah. Artificial intelligence in public health education: current evidence, competencies, and a framework for responsible integration. Frontiers in Public Health (2026-09-15). https://doi.org/10.3389/fpubh.2026.1951054

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.