Actionable companion · Assessment and evidence of learning

From evidence to district action: Ethical considerations for teaching with artificial intelligence: a scoping review in medical education settings

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

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

Abstract In the evolving realm of medical education, where artificial intelligence (AI) technologies play an increasingly pivotal role, embedding AI ethics within educational frameworks has become crucial. This scoping review explores the breadth of literature concerning ethical considerations for teaching with AI in medical education settings. Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, the team analyzed 82 peer-reviewed English-language articles published between 2018 and October 2023, sourced from PubMed, Embase, and Web of Science. The review presents insights into the geographic distribution of studies, publication trends, article types, common keywords, areas of focus, and ethical considerations. Ethical considerations are framed around the core principles of autonomy, beneficence, non-maleficence, justice, and transparency. Emerging themes and notable gaps are identified, offering suggested directions for future research. Review articles were found to be the most common study type, and “artificial intelligence” and “education” were the most frequently recurring keywords in the articles. The articles primarily focused on (a) incorporating AI in medical education, (b) AI ethical principles in medical education, and (c) ChatGPT in healthcare education. Ethical concerns raised in the articles centered on privacy, bias, informed consent, and algorithmic transparency, with many offering implications for curriculum development. These findings underscore the significance of integrating AI tools as instructional aids to enhance medical education, improve student learning experiences, and address the ethical challenges associated with their use in instructional settings. Future research opportunities include refining standardized competencies for AI in medical education, developing evidence-based curricula, and conducting longitudinal studies to assess AI’s long-term impact on learning outcomes. Keywords: Medical education, Artificial intelligence, AI in education, AIED, AI ethics, Curriculum development, ChatGPT, Scoping review

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Amani Itani; Susie L Gronseth; Salma Musaad; Thuy Nguyen; Yiming Mirabile; Bettina M Beech. Ethical considerations for teaching with artificial intelligence: a scoping review in medical education settings. International Journal of Educational Technology in Higher Education (2025-11-17). https://doi.org/10.1186/s41239-025-00563-9

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.