Application of AI-assisted PBL-CBL combined teaching in the diagnosis and treatment of post-herpetic neuralgia: a randomised controlled trial
Year: 2026
Type: article
Study designs: Randomized Controlled Trial, Clinical Trial
Abstract: BACKGROUND: This study aimed to explore the efficacy of a teaching model integrating artificial intelligence-assisted problem-based learning (PBL) with case-based learning (CBL) in standardized clinical teaching of postherpetic neuralgia (PHN) for medical interns. METHODS: A total of 120 interns at the Affiliated Hospital of Yunnan University were randomly divided into an observation group and a control group (n = 60 each). The control group received conventional lecture-based teaching, while the observation group was taught using the AI-assisted PBL-CBL combined model. After four weeks of teaching, the educational outcomes were evaluated through theoretical examinations, clinical skill assessments, Mini-Clinical Evaluation Exercise (Mini-CEX) scores, and a teaching satisfaction questionnaire. RESULTS: After four weeks, the theoretical score of the observation group was 87.35 ± 5.66, and the clinical practice score was 90.12 ± 3.47, both significantly higher than those of the control group (84.67 ± 4.05 and 88.23 ± 3.94, respectively; P < 0.01). The Mini-CEX evaluation showed that students in the observation group had significantly improved clinical practice skills (P < 0.001), whereas no significant differences were observed between the two groups in humanistic qualities, professionalism, or organization/efficiency (P > 0.05). In addition, the observation group reported higher teaching satisfaction than the control group. CONCLUSIONS: The AI-assisted PBL-CBL teaching model significantly improves medical interns' theoretical knowledge and clinical skills related to PHN, enhances their clinical reasoning ability, and increases teaching satisfaction.
Source: BMC Medical Education
Authors: Fang Yang, Jingmei Wang, Liping He
Institution: Yunnan University
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W7162017658