AI-supported case-based learning in medical education: a comprehensive scoping review

Introduction – Recent literature indicates that generative artificial intelligence (GenAI) is also being integrated into case-based learning (CBL) through activities such as clinical case generation, clinical reasoning support, and structured feedback. However, the evidence about GenAI’s role in CBL remains fragmented. Given the diverse nature of available GenAI-CBL studies, we conducted a comprehensive scoping review to map and synthesize the evidence in this area, highlighting key themes, outcomes, challenges, and limitations to inform future research, curriculum development, and policies. Method – A comprehensive search was performed across multidisciplinary databases, including PubMed, ERIC, Scopus, Web of Science, and Google Scholar, covering publications from 2019 to 2025. Title and abstract screening, followed by full-text review and data extraction, were conducted independently by two reviewers using predefined eligibility criteria. The data synthesis involved thematic analysis to create an evidence map of GenAI-supported case-based and case-anchored learning in medical education. Results – The findings were organized into six key themes that showcase the role of GenAI in enhancing case-based learning, covering areas such as clinical reasoning and contextual thinking; efficient and scalable case creation; learner engagement, motivation, and perceived usefulness; accuracy, reliability, and ethical issues; faculty adaptation and pedagogical integration; and hybrid and reflective learning methods. Conclusion – Overall, the evidence indicates that GenAI can effectively support CBL in medical education, especially during early and intermediate stages. It also highlights the ongoing importance of faculty oversight and the need for further research to address advanced clinical judgment and ethical reasoning. Systematic review registration – https://doi.org/10.17605/OSF.IO/28E3G, identifier (28E3G).