Actionable companion · Assessment and evidence of learning

From evidence to district action: Application effect and teaching evaluation of case-based learning combined with ChatGPT in ophthalmology clinical teaching

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:

Abstract Objective To investigate the effectiveness of a case-based learning (CBL) model integrated with ChatGPT in ophthalmology clinical teaching and to compare its educational outcomes with those of the traditional multimedia lecture-based approach. Methods A total of 98 fifth-year clinical medicine students from Shanghai Jiao Tong University School of Medicine (Class of 2021) were randomly assigned to an experimental group (CBL + ChatGPT, n = 49) or a control group (traditional lecture-based teaching, n = 49). The teaching intervention lasted for 8 weeks, with one 90-min session per week. The experimental group adopted a standardized human-AI interaction protocol, including a structured questioning framework, no fewer than three rounds of dialog, and real-time teacher supervision and correction. The control group received conventional multimedia lectures. Outcomes included theoretical knowledge examination scores, case analysis scores, recognition rates of the teaching model across seven dimensions, and overall teaching satisfaction. Subgroup analyses were further conducted according to students’ baseline academic performance (high-, medium-, and low-foundation groups). Results The experimental group achieved significantly higher theoretical examination scores than the control group (86.4 ± 5.2 vs. 78.9 ± 6.1, P < 0.01), as well as higher case analysis scores (84.7 ± 6.3 vs. 74.2 ± 7.5, P < 0.01). Recognition rates across all seven evaluation dimensions, including interactivity, immediate feedback, and clinical relevance, were significantly higher in the experimental group (all P < 0.05). The overall satisfaction rate was 89.8% in the experimental group and 63.3% in the control group (P < 0.01). Subgroup analysis demonstrated that students with weaker academic foundations benefited most from the intervention, showing the greatest improvement in scores (+12.3 points, P < 0.01). These students also reported significantly higher recognition of personalized learning experiences than students with stronger academic backgrounds (95.2% vs. 81.2%, P < 0.05). Conclusion The CBL teaching model integrated with ChatGPT significantly improves both objective learning outcomes and subjective satisfaction in ophthalmology clinical education, particularly among students with weaker academic foundations. This teaching approach demonstrates substantial potential for broader implementation in medical education. Keywords: case-based learning, ChatGPT, clinical teaching, educational evaluation, ophthalmology

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

Yuan Shen; Yuxiao Chen; Mengyao Li; Xiang Gu; Ai Zhuang. Application effect and teaching evaluation of case-based learning combined with ChatGPT in ophthalmology clinical teaching. Frontiers in Medicine (2026-07-03). https://doi.org/10.3389/fmed.2026.1885204

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.