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Application of an AI-enhanced interactive virtual clinical reasoning training system in orthodontic residency
Qiannan Niu1, Yi Wen1, Qiuxiang Yuan2
1State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Orthondontics, School of Stomatology, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Background:
Case-based discussion is essential for developing clinical reasoning in orthodontics education, but its traditional format faces limitations in terms of case accessibility, teaching consistency, and scheduling flexibility. To address these challenges, we developed an AI-enhanced interactive virtual clinical reasoning training system and implemented it in our orthodontic curriculum. This study evaluates the educational effectiveness and practical utility of this innovative teaching system.
Methods:
A controlled trial was conducted with 74 residents who were randomly assigned to either an experimental group (n = 37) or a control group (n = 37). Both groups participated in case-based learning sessions on four common orthodontic cases. The experimental group used an AI-enhanced interactive virtual clinical reasoning training system, while the control group engaged in traditional small-group discussions. Teaching effectiveness was evaluated through objective examinations (theory test and case analysis) and an online questionnaire. System satisfaction was also assessed in the experimental group.
Results:
The experimental group achieved significantly higher case analysis scores and total scores than the control group (p < 0.001), although no significant difference was found in theory examination scores. In the teaching effectiveness survey, the experimental group rated all seven items significantly higher than the control group (p < 0.05). The system satisfaction survey indicated positive feedback across system functionality, skill improvement, and overall experience (mean scores: 4.35-4.57; Cronbach's α = 0.919).
Conclusion:
The AI-enhanced interactive virtual clinical reasoning training system significantly improved residents' case analysis performance and learning experience compared to traditional case-based learning. By simulating authentic clinical scenarios derived from real patient cases, the system bridges theoretical knowledge and clinical practice while fostering the development of humanistic competencies. This integrative approach offers a promising pathway for supporting medical students' transition to clinical practice and enhancing case-based education in residency training.