Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Effectiveness of ChatGPT and DeepSeek in Urology Medical Education: Randomized Controlled Trial

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 Since its release in November 2022, generative AI (GenAI) tools, including ChatGPT, have gained widespread attention across various sectors, including medical education. Objective This study seeks to examine the effectiveness and feasibility of GenAI tools (ChatGPT o3‑mini [OpenAI] and DeepSeek R1) in enhancing urology teaching outcomes for medical undergraduates. Methods We assessed the accuracy of responses from ChatGPT o3-mini and DeepSeek R1 to authoritative urology multiple-choice questions. Then, a randomized controlled trial was performed to compare the learning outcomes of students using ChatGPT o3-mini and DeepSeek R1 with those using traditional learning methods. Additionally, a questionnaire was designed to survey medical undergraduates’ perspectives on the application of AI in urology education. Results DeepSeek R1 demonstrated higher accuracy than ChatGPT o3-mini in answering urology-related multiple-choice questions. In the test following the self-study period, the DeepSeek R1 group surpassed both the control and ChatGPT o3-mini groups in total scores across various question types. Despite the superior scores in the ChatGPT o3-mini group, statistical significance was not achieved relative to the control group. Survey results revealed that most students had a positive attitude toward AI-assisted learning, believing it could effectively enhance medical education. Conclusions DeepSeek-assisted self-study was associated with higher posttest scores than traditional internet-based learning, whereas ChatGPT showed numerically higher but nonsignificant results. These findings offer evidence-based insights into the embedding of GenAI within medical education frameworks, providing guidance for educators in developing teaching strategies and for institutions in formulating relevant 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. 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

Wentong Yang; Ting Xu; Junjie Wei; Wentao Zheng; Wenbo Yan; Jingkai Wang; Guangdi Chu; Haitao Niu. Effectiveness of ChatGPT and DeepSeek in Urology Medical Education: Randomized Controlled Trial. Journal of Medical Internet Research (2026-09-21). https://doi.org/10.2196/89315

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.