Exploring the Role of Case-based Learning with Artificial Iintelligence in Enhancing  Dental Education

Samia Nasir; Asma Siddiqui; Yusra Nasir; Shehrish  Habib; Affan Ahmad; Syed Shah  Faisal

doi:10.51253/pafmj.v76iSUPPL-9.14354

Authors

Samia Nasir Department of Dental, Karachi Medical and Dental College, Karachi Metropolitan University, Karachi Pakistan
Asma Siddiqui Department of Dental, Karachi Medical and Dental College, Karachi Metropolitan University, Karachi Pakistan
Yusra Nasir Department of Medicine, Liaquat National Hospital, Karachi Pakistan
Shehrish Habib Department of Medicine, Liaquat National Hospital, Karachi Pakistan
Affan Ahmad Department of Dental, Karachi Medical and Dental College, Karachi Metropolitan University, Karachi Pakistan
Syed Shah Faisal Department of Dental, Karachi Medical and Dental College, Karachi Metropolitan University, Karachi Pakistan

DOI:

https://doi.org/10.51253/pafmj.v76iSUPPL-9.14354

Keywords:

Artificial Intelligence, Case-Based Learning, Critical Learning, Clinical Reasoning, Dental Education, Educational Technology, Health Professions Education.

Abstract

Objective: To assess and understand the students’ perception and acceptance regarding the Case-based Learning (CBL) with Artificial Intelligence (AI), and to provide deeper insights into this emerging education trend, as well as to offer valuable recommendations for future development and optimization of AI-powered educational tools and platforms.

Study Design: Qualitative Exploratory study.

Place and Duration of Study: Karachi Metropolitan University, Karachi Pakistan, from Nov 2025 to Mar 2026.

Methodology: A qualitative, exploratory design was employed with 100 final-year BDS students. The students attended two lectures on the same topic, one week apart. The first session was conducted as a small group discussion via traditional human facilitation, and the second session employed an AI generated CBL lecture. Data collection was done using a reflection sheet, while it was analyzed thematically using Braun and Clarke’s framework.

Results: Reported challenges were minimal, and primarily related to technological interruptions or difficulty engaging with complex case material. Overall, AI-assisted CBL was experienced as a meaningful and cognitively enriching learning approach that supported deeper engagement with clinical concepts while reinforcing the continuing pedagogical role of the instructor.

Conclusion: Our results demonstrate that AI-assisted CBL can effectively bridge the gap between theory and practice. However, the findings also underscore that AI's current role is best characterized as an adjuvant rather than primary facilitator.

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