Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Artificial intelligence in perioperative medicine education: A feasibility test of case-based learning

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 The use of artificial intelligence in medicine is rapidly expanding. Large language models, such as ChatGPT, have the potential to enhance perioperative medicine through education and clinical practice. However, concerns remain regarding the accuracy of these models, particularly the risk of hallucinations, generating factually incorrect outputs. This feasibility test explores the use of a large language model–enabled platform to assist in case-based education in perioperative clinical cases. Methods: Five perioperative cases addressing core topics were developed and uploaded to a custom large language model platform. The large language model platform allows free-text questions to be asked to the artificial intelligence, which then uses the derived cases to provide answers. Anaesthetic trainees engaged with the artificial intelligence, asking questions to obtain information regarding history, examination, and investigations. Artificial intelligence question-and-answer pairs were then evaluated independently in duplicate for the presence of inappropriate responses, including hallucinations. Results: The large language model responded appropriately to nearly all questions, with no hallucinations observed. The proportion of questions that were answered appropriately was 99.3% (543/547). In the four instances of inappropriate responses, the large language model declined to provide information in the case description rather than hallucinate. Conclusion: The large language model appears capable of supporting the delivery of case-based perioperative medicine content with a high degree of accuracy. Keywords: Large language models, Machine learning, Medical education, Natural language processing, Case-based learning, Artificial intelligence

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

Timothy Trewren; Nicholas Fitzgerald; Sarah Jaensch; Olivia Nguyen; Alexander Tsymbal; Christina Gao; Brandon Stretton; Stewart Anderson; D-Yin Lin; Dario Winterton; Galina Gheihman; Guy Ludbrook; Kelly Bratkovic; Stephen Bacchi. Artificial intelligence in perioperative medicine education: A feasibility test of case-based learning. Journal of Perioperative Practice (2025-06-13). https://doi.org/10.1177/17504589251346634

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.