Generative AI · Education leadership

Actionable companion · Instructional improvement

From evidence to district action: Generative Artificial Intelligence in Medical Training: Utilization Patterns Across Knowledge, Patient Care, Systems Reasoning, and Innovation

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:

Since 2022, generative artificial intelligence (AI) use has grown rapidly across many sectors, including medical education. While prior research has explored perceptions of AI, the understanding of AI use amongst medical trainees has been limited. This study surveyed medical trainees to better identify the patterns of generative AI use. Results showed varying patterns based on the phase of training, with ChatGPT emerging as the predominantly used platform across all phases. While awareness of AI policies was limited, the majority reported efforts for responsible use of AI. Implications include understanding of equitable access and onboarding regarding AI use policies.

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. Choose one instructional unit and a specific teacher-supervised AI use. Compare it with the existing approach using a common learning objective, equivalent tasks and a shared rubric. 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. Measure unaided learning on a new task, factual errors, student workload and teacher review time. Disaggregate results where group sizes permit responsible interpretation. 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

Grace L. Park; Gary L. Beck Dallaghan; Joe Bradley; Qasim Sikander; Hannah Jung; Kevin Zhang; Gregory Polites; Janet Jokela. Generative Artificial Intelligence in Medical Training: Utilization Patterns Across Knowledge, Patient Care, Systems Reasoning, and Innovation. Medical Science Educator (2026-01-04). https://doi.org/10.1007/s40670-025-02623-1

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.