Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Current Status of Generative Artificial Intelligence Utilization in Medical Education: A Cross-Sectional Survey of Medical Students and Faculty

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.

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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 Introduction: Generative artificial intelligence (gAI), particularly large language models such as ChatGPT, is rapidly transforming various sectors, including medical education. Despite increasing interest, few studies have investigated how gAI is actually used in medical education settings, especially in Japan. This study aimed to assess the current use of gAI among medical students and faculty members, and to compare gAI usage patterns and attitudes between these two groups. Methods: A cross-sectional survey was conducted from April to May 2025 at the Oita University Faculty of Medicine. A total of 1,017 students and 470 faculty members from the School of Medicine, School of Nursing, and Department of Advanced Medical Sciences were invited to complete an anonymous online questionnaire. The survey covered gAI usage experience, purposes of use, and attitudes toward gAI in academic contexts. Results: The response rates were 40% for students (402/1,017) and 74% for faculty members (350/470). Most students (82.1%) and faculty (73.4%) had prior experience using gAI tools, primarily for report writing, lecture preparation, and information retrieval, with students showing a higher rate of gAI usage experience than faculty (p < 0.05). While 92.8% of students and 86.5% of faculty supported gAI use under certain conditions, 73.4% of faculty members reported major concerns, including ethical risks and the risk of personal information leakage. In the faculty survey, younger faculty members, and those with gAI usage experience were also significantly more likely to approve the introduction of gAI into medical education (p < 0.05). Conclusions: gAI is widely accepted and used in medical education. However, ethical guidelines, digital literacy education, and thoughtful integration strategies are essential to ensure its responsible use. Keywords: artificial intelligence, generative artificial intelligence, medical education

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

Shigeo Ninomiya; Kyoko Yamamoto; Eiko Mieno; Hirofumi Anai; Naoto Uemura; Takashi Kobayashi; Masato Tanigawa; Takashi Hirano; Isao Saito; Toshikatsu Hanada; Yuichi Endo; Yusuke Matsunobu; Tatsushi Tokuyasu; Kenji Ihara; Masafumi Inomata. Current Status of Generative Artificial Intelligence Utilization in Medical Education: A Cross-Sectional Survey of Medical Students and Faculty. JMA Journal (2026-04-24). https://doi.org/10.31662/jmaj.2025-0580

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.