Application of artificial intelligence-generated clinical cases in case-based learning of histology and embryology

To investigate the effectiveness of a case-based learning (CBL) approach in histology and embryology, integrating instructor-reviewed, AI-assisted clinical cases within a Structure-Function-Clinical (SFC) framework. Undergraduate students from Hangzhou City University's 2022 cohort (control, n = 150) and 2023 cohort (experimental, n = 147) participated. The experimental group engaged in an SFC-oriented CBL approach, utilizing instructor-reviewed, AI-generated clinical cases systematically integrated into the curriculum. The control group received conventional lecture-based instruction without case activities. Effectiveness was evaluated by comparing academic performance (formative assessments, midterms, finals, overall grades) and student satisfaction. The experimental group significantly outperformed the control group in formative assessments, midterms, and overall course grades. While total final exam scores showed no significant difference, the experimental group had a higher proportion of high-scoring students. Regarding satisfaction surveys, the experimental group reported significantly higher ratings in specific dimensions, including learning engagement, clinical relevance, SFC clarity, and perceived effectiveness (all q < 0.05), although overall satisfaction scores did not differ significantly after correction (q = 0.056). Notably, these improvements were achieved without increasing perceived cognitive load (q > 0.05). An SFC-oriented CBL approach, supported by instructor-reviewed, AI-assisted cases, effectively enhances students' ability to apply histological and embryological knowledge in clinical contexts without increasing cognitive load. This method successfully bridges foundational morphological knowledge with clinical practice, offering a practical, scalable model for integrating AI into basic medical education.