AI-Powered Problem- and Case-based Learning in Medical and Dental Education: A Systematic Review and Meta-analysis

作者：Hongxia Wei, Yuguo Dai, Kaiting Yuan, Kar Yan Li, Kuo Feng Hung, Emily Hu, Angeline Hui Cheng Lee, Jeffrey Wen Wei Chang, Chengfei Zhang, Xin Li · 发表于：International Dental Journal · 年份：2025 · DOI：10.1016/j.identj.2025.100858 · 被引用次数：36 · 研究领域：Clinical Reasoning and Diagnostic Skills、Problem and Project Based Learning、Innovations in Medical Education

INTRODUCTION AND AIMS: Advances in artificial intelligence (AI) technology have generated a revolution in medical and dental education, which may offer promising solutions to tackle the challenges of traditional problem-based learning (PBL) and case-based learning (CBL). The objective of this study was to assess the available evidence concerning AI-powered PBL/CBL on students' knowledge acquisition, clinical reasoning capability and satisfaction. METHODS: An electronic search was carried out on PubMed, MEDLINE, the Cochrane Central Register of Controlled Trials and Web of Science. Clinical trials published in English with full text available, which implemented AI technologies in PBL/CBL in the medical/dental field and evaluated knowledge acquisition, clinical reasoning and/or satisfaction were included. The quality assessment was conducted using RoB 2 by two calibrated assessors. Data synthesis and meta-analysis were performed, the standardised mean difference (SMD) or standardised mean (SM) and 95% confidence intervals (CIs) were calculated, and heterogeneity was quantified. RESULTS: Six randomized controlled trials were included, with an overall risk of bias judged to have 'some concerns'. For knowledge acquisition, 4 studies were included in the meta-analysis. A low heterogeneity (I² = 20%) was detected and a fixed-effect model was utilised. Compared with the control group, the AI intervention significantly improved knowledge acquisition by 46% (95% Cls [0.18-0.73], P = .0...