Actionable companion · Assessment and evidence of learning

From evidence to district action: Generative artificial intelligence adoption and use in teaching and training healthcare professionals in higher education in the United States: a cross-sectional study

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:

Background The use of generative artificial intelligence (GenAI) is rapidly expanding across medical and allied health education. However, structured and curriculum-integrated training to prepare future healthcare professionals remains limited. This study examined the adoption, self-reported knowledge, and perceptions of GenAI among faculty and students in a U.S. higher education institution. Methods We conducted a cross-sectional study using a self-administered online questionnaire to assess GenAI knowledge, adoption, use, benefits, and challenges among faculty and students in a public Minority Serving Institution in California. Using a convenience sampling technique, data were collected with Qualtrics from January – March 2025 and analysed with IBM SPSS Statistics version 31. Results A total of 559 complete responses were analysed (faculty: 19.3%; students: 78.5%). Overall, 83.2% of respondents reported having used GenAI, including 91.1% of faculty and 81.4% of students, with no statistically significant difference by role (p = .12). Only 7.9% had received formal training in GenAI, while 29.1% reported having conducted substantial independent research on GenAI (faculty: 48.5%; students: 24.3%). Nearly half of participants (48.7%) perceived GenAI as beneficial for learning, teaching, and research (p = .002). Additionally, 47.4% believed GenAI is currently essential, and 70.8% anticipated it will become essential in the future, with significant more faculty respondents asserting the importance of AI (p < .001 and p = .009, respectively). Most respondents relied on free GenAI tools (90.6%), and among users of paid tools, 66.3% paid out of pocket. Participants primarily sourced GenAI information from the internet (77.3%), reported substantial concerns (70.3%), and experienced multiple challenges in GenAI use (84.7%). Conclusions GenAI use among faculty and students is widespread, but formal training and institutional support remain limited. These findings underscore the need for coordinated, campus-wide GenAI training, governance, and policy frameworks to promote ethical, effective, and equitable integration of GenAI into healthcare education and workforce preparation. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-026-09291-8.

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

Obinna O. Oleribe; Parichart Sabado; Kazi T. Begum; Matt G. Mutchler; Benedetto Piccoli; Andrew W. Taylor-Robinson; Christopher Denaro; Ricardo Izurieta; Simon D. Taylor-Robinson. Generative artificial intelligence adoption and use in teaching and training healthcare professionals in higher education in the United States: a cross-sectional study. BMC Medical Education (2026-04-24). https://doi.org/10.1186/s12909-026-09291-8

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.