Faculty Perspectives on Integrating Artificial Intelligence Into Orthodontic and Interdisciplinary Dental Education: Opportunities, Challenges, and Strategies

Abstract

INTRODUCTION: Artificial intelligence (AI) is transforming dental education by enhancing diagnostic precision and personalized learning. This study aimed to investigate faculty perspectives on integrating AI into orthodontic and interdisciplinary dental curricula and identifying opportunities, challenges, and strategies to prepare students for technology-driven practice. The objectives included exploring AI's potential in enhancing diagnostic tools and learning systems, identifying barriers such as faculty training gaps and ethical concerns, and developing evidence-based strategies for AI adoption. MATERIALS AND METHODS: A cross-sectional survey was conducted in the Department of Orthodontics. A structured questionnaire developed by a multidisciplinary team comprised 12 items across three sections: opportunities, challenges, and strategies, rated on a numerical scale (-1 = Disagree, 0 = neutral and +1 = Agree). After pilot testing (Cronbach's alpha = 0.82), the survey was distributed via WhatsApp to 410 faculty members from North Indian dental colleges, targeting orthodontists and related specialties with at least two years of teaching experience. Data were analyzed using independent t-tests and one-way analysis of variance (ANOVA) for comparison (p < 0.05). RESULTS: A total of 250 (61%) participants responded to the survey. The study population included 140 (56%) males with a mean age of 38.11 ± 5.08 years and 110 (44%) females, with orthodontists as the largest specialty group. Females reported higher opportunity (mean = 3.14 vs. 2.82, p = 0.048) and challenge scores (mean = 3.41 vs. 3.14, p = 0.029) than males, with no significant difference in strategy perceptions (p = 0.179). Full-time academicians noted greater challenges (p = 0.013) than clinician-academicians. Orthodontists showed the highest opportunity scores (mean = 3.33) and lowest challenge scores (mean = 3.06), whereas prosthodontists reported the highest challenges (mean = 3.57, p = 0.004). Age and experience were positively correlated with opportunity and strategy perceptions, with weaker negative correlations with challenges. CONCLUSION: The findings revealed strong faculty support for AI integration, with perspectives influenced by sex, specialty, and role. Tailored training and curriculum redesign are needed to address these barriers and enhance AI adoption in dental education.

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