Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: ChatGPT in medical education: a cross-sectional analysis of usage, attitudes, perceptions, and practices among Saudi medical students

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 Background Generative artificial intelligence (AI) platforms such as ChatGPT are rapidly reshaping higher education, particularly in medical learning environments. Although several researches have examined students’ perceptions and attitudes toward ChatGPT in Saudi Arabia, context-specific evidence focusing on medical students and their practical engagement with ChatGPT remained limited. Objectives This study aimed to describe patterns of ChatGPT usage, students’ perceptions, attitudes, and practices among medical students and to explore their associations with demographic and academic characteristics. Methods A cross-sectional study was carried out between August to October 2025 involving 328 undergraduate medical students in King Faisal University, Saudi Arabia. Data were obtained using a validated 44-item instrument measuring students’ perceptions, attitudes, and practices toward ChatGPT. Descriptive and inferential statistics were performed using Statistical Package for Social Science (SPSS, Version, 27), with statistical significant set at p 4.5), and students from higher-income families exhibited more favorable responses. Conclusion The findings highlight strong awareness, frequent use, and positive perception toward ChatGPT among medical students at King Faisal University, viewed as an accessible and effective academic aid. Nevertheless, apprehension remain regarding reliability, ethical use, and risk of overdependence. Institutional frameworks incorporating AI literacy, critical appraisal training, and ethical guidelines are essential to ensure effective integration of ChatGPT in medical education. Keywords: artificial intelligence, ChatGPT, attitude perception, practice, medical education, Saudi students

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

Ali Qasem Mohammed AlAlwan; Ayoob Lone. ChatGPT in medical education: a cross-sectional analysis of usage, attitudes, perceptions, and practices among Saudi medical students. Frontiers in Artificial Intelligence (2026-04-22). https://doi.org/10.3389/frai.2026.1751367

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.