Dental Students' and Educators' Perceptions Toward the Adoption of Artificial Intelligence in Dental Education: A Survey-Based Study.

PURPOSE: The integration of artificial intelligence (AI) into dentistry has the potential to transform dental education, clinical training, and research. However, the perspectives of direct stakeholders remain underexplored. This cross-sectional study aimed to describe current knowledge, perceptions, and self-reported usage patterns of AI tools among dental students and faculty at a single institution and to identify specific educational needs and barriers to curriculum integration. METHODOLOGY: A cross-sectional, questionnaire-based survey was distributed both online and in-person to dental students and educators. The survey collected demographic data and included closed-ended items addressing knowledge, perception, and attitudes toward the adoption of AI in dental education. Descriptive statistics and comparative analyses were performed to identify differences across roles, gender, and academic levels. RESULTS: The study included 333 dental students and 55 faculty members, with a higher proportion of females (53.4%). Most participants were familiar with AI, with significant differences in knowledge levels across academic years (p = 0.001). Females reported higher usage of AI for academic tasks (mean = 3.89, p = 0.004) and familiarity with AI tools like language translation (mean = 3.49, p = 0.009). Faculty showed greater AI knowledge (81.8% vs 27.9%, p = 0.001) and were more likely to see its benefits in dental education compared to students. CONCLUSION: Both dental students and faculty demonstrated high awareness and positive perceptions regarding AI use and relevance to dental education. The majority expressed strong interest in receiving formal training on AI applications through structured lectures, workshops, and courses. These findings suggest stakeholders' readiness for AI curriculum integration and indicate a need for curriculum development and establishment of ethical guidelines to support future AI integration, though the extent to which such training would influence educational or clinical outcomes remains to be investigated.