ABSTRACT Purpose This study aimed to evaluate the impact of an artificial intelligence (AI)‐generated formative feedback system on third‐year dental students’ crown preparation performance, perceptions, and burnout levels in a preclinical fixed prosthodontics course. Methods Fifty‐eight students were eligible; two withdrew, leaving 56 randomized: AI Feedback Group ( n = 28) and Faculty Feedback Group ( n = 28). Five students were excluded from final analyses due to missing data, resulting in 51 students being analyzed. The AI system provided automated, multi‐modal feedback on five critical parameters (occlusal/axial reduction, angulation, finish line quality, surface smoothness, and undercuts) via a 3D color‐coded map, numerical score, and written comments. Academic performance was assessed via final practical exam scores; perceptions were collected via a 12‐item custom survey; and burnout was measured using the validated School Burnout Inventory (SBI‐9). Performance gains were compared based on post‐hoc stratification by AI system usage frequency. Results The mean final exam score for all participants was 84.3 ± 7.1(out of 100). A sub‐analysis across the entire sample revealed that students with high AI engagement (≥ 5 uses; n = 24) scored significantly higher (87.2 ± 6.1) than those with lower AI engagement (< 5 uses; n = 27; 80.6 ± 7.2) ( U = 388, p = 0.011). This highly engaged group also showed a greater mean reduction in flagged errors (27.8%) compared to the less engaged group (9.6%). Most students found AI feedback helpful (84%) and easy to understand (78%), with 76% reporting improved skill development. The preferred feedback method was a blended AI + faculty approach (69%). SBI‐9 scores indicated moderate emotional strain (exhaustion = 3.6 ± 0.8) but low cynicism (cynicism = 2.0 ± 0.7); no significant differences in burnout were found between AI engagement groups. Conclusions Integration of AI‐based formative feedback was positively received and correlated with significantly improved crown preparation performance in students who engaged more frequently with the system. AI‐enhanced simulation supports self‐regulated learning and autonomy in dental education without significantly increasing academic stress. This model offers a valuable complement to traditional instruction, supporting objective and timely skill development.
Merve Benli (Thu,) studied this question.