Development and Use of AI-Assisted Case-Based Learning in Dental and Medical Education
Abstract
Clinical reasoning and diagnostic competencies are widely recognized as essential components in dental and medical education yet remain difficult to achieve effective outcomes. Case-Based Learning (CBL) has been adopted as a structured pedagogical approach to address several challenges in engaging students in real clinical scenarios. In recent years, Artificial Intelligence (AI)-assisted tools, particularly those designed for clinical case development and information curation, have been introduced to support CBL without displacing traditional instructional methods. This narrative review synthesizes current literature to examine the role of AI-assisted approaches in enhancing undergraduates' learning experiences, in terms of engagement, motivation, knowledge integration, and improvement of diagnostic reasoning within CBL frameworks. The present review further highlights the practical consideration for dental and medical educators, and curriculum designers to focus on the integration of AI-assisted tools as a means of strengthening clinical education practices while maintaining educational integrity and learner-centered outcomes in higher education.
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