An Open-Source, AI-Supported Teaching Tool in Orthodontic Education-Assessment of Acceptance and Effectiveness.

Abstract

This study aimed to evaluate the effectiveness and acceptance of an AI-supported teaching tool for cephalometric tracing in orthodontic education. Munich Cephalometric Application for Training (MCAT) was introduced to students from two consecutive semesters. Participants were given access to independently train on their own computers. Finally, they submitted the results of three manually traced radiographs. To evaluate the effectiveness, their measurements were compared with those of students from another semester who did not have access to the application. The acceptance was assessed using a questionnaire. Students who used MCAT demonstrated reduced variability in their tracings, with a lower interquartile range compared to the control group. Significant improvements were noted for specific cephalometric variables. The tool was positively received, with 86.5% of the participants perceiving greater learning outcomes for themselves when working with MCAT. MCAT effectively enhances cephalometric tracing skills and is well-accepted by students, supporting its integration into orthodontic curricula.