Arrive Framework For Implant Education And Its Derived Ar System

Current dental implant training techniques lack standardized evaluation frameworks and have inherent limitations. This study proposes the ARRIVE framework (Anatomical Understanding, Recognition of Drill Positions, Real-Time Feedback, Interrelation to Freehand Surgery, Versatility, Engagement), a structured approach for evaluating implant education tools. Based on this framework, an augmented reality implant surgery navigation system (AR-ISNS) was developed and evaluated for its advantages over traditional methods and static guides. This study compared implant placement trainings using standardized partial edentulous plastic models. Six postgraduate and ten undergraduate students compared traditional training with AR-ISNS and static guide-assisted training with AR-ISNS. After theoretical training, students performed implant placements. CBCT and STL data assessed deviations in coronal, apical, and angular positions. SUS, NASA-TLX, and surveys evaluated system usability and cognitive load. In postgraduates, AR-ISNS coronal, apical, and angular deviations were 0.58±0.29 mm, 0.81±0.39 mm, and 0.91±0.27°, with angular deviation significantly smaller than traditional training (P<0.05). In undergraduates, deviations were 0.85±0.30 mm, 0.88±0.24 mm, and 1.48±0.76°, comparable to static guides (P=0.0596). SUS (70.11) and NASA-TLX (51.37/100) indicated good usability and acceptable cognitive load. AR-ISNS demonstrates feasibility, accuracy, and educational advantages in implant surgery training. Aligning with the ARRIVE framework, it provides intuitive, real-time guidance, enhances procedural precision, and addresses limitations of existing training methods, representing a valuable innovation for dental education.