ArticleJMIR medical education2026
Generative AI-Assisted Progressive-Disclosure Case-Based Learning for Clinical Reasoning in Occupational Medicine: Quasi-Experimental Study.
Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCase-based learning (CBL) promotes transfer of knowledge to practice, yet occupational health CBL must also develop exposure assessment and epidemiologic thinking. Static, single-session cases that disclose all information upfront can truncate iterative reasoning and encourage premature diagnostic closure. Generative AI (GenAI) can support the efficient development of high-fidelity, progressively disclosed cases, but hallucination risks require strict quality control.
objectiveThis study aimed to develop and evaluate a multicomponent GenAI-assisted, 4-act progressive-disclosure CBL package for an occupational lead poisoning module, using a human-in-the-loop workflow to mitigate hallucination risk.
methodsIn a nonrandomized posttest controlled quasi-experimental study, 224 undergraduates were assigned by administrative class to an intervention group (n=114) or a control group (n=110). The control group received conventional static CBL; the intervention group received a GenAI-assisted progressive-disclosure CBL package. The primary outcome was the standardized individual case-analysis assignment score. Secondary outcomes were the delayed final examination score, 5 self-reported learning experience domains, and 3 video-derived behavioral engagement indicators. Subgroup analyses by academic major and group-by-major interaction tests were exploratory.
resultsBaseline characteristics were comparable between groups. The intervention group scored higher on case analysis (mean 88.41, SD 3.85 vs mean 82.51, SD 3.94; P<.001; Cohen d=1.51) and on the delayed final exam (mean 80.96, SD 8.47 vs mean 78.62, SD 6.63; P=.02; Cohen d=0.31). Significant improvements were observed in information gathering, hypothesis generation, and differential diagnosis (all P<.001 after Holm correction), while treatment/management planning did not differ (P=.19). The intervention group reported higher perceived difficulty, perceived improvement in clinical reasoning, engagement, transfer of self-efficacy, and evaluation of the course materials (all P<.001). Voluntary responses (mean 2.30, SD 1.11 vs mean 1.68, SD 0.92; P=.008), evidence-referencing statements (mean 1.90, SD 0.88 vs mean 1.45, SD 0.71; P=.01), and net group discussion time (mean 36.50, SD 6.88 vs mean 28.42, SD 7.02 minutes; P<.001) were all higher in the intervention group. The group-by-major interaction for the final examination was not statistically significant (P=.41). Audit logs showed that all AI-generated case drafts required expert correction.
conclusionsA multicomponent GenAI-assisted, 4-act progressive-disclosure CBL package implemented with rigorous human-in-the-loop verification was associated with higher case-analysis performance, modestly higher delayed examination performance, and greater behavioral engagement in one nonrandomized cohort. Randomized and class-level multilevel designs with longer follow-up are needed to determine the durability and generalizability of these findings.
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