Artificial Intelligence in Dental Education: A Scoping Review of Opportunities, Challenges, and Ethical Frameworks for Shaping Accreditation Standards and Future Practice

Background: The integration of artificial intelligence (AI) into dental education offers transformative potential for enhancing learning outcomes, clinical training, and institutional efficiency. However, rapid AI adoption introduces ethical, logistical, and pedagogical challenges that require systematic exploration. This scoping review maps the current applications, challenges, and future directions of AI in dental education, focusing on its integration into curricula while ensuring ethical, equitable, and pedagogically sound practices. Methods: The Joanna Briggs Institute framework was followed, with reporting per the PRISMA-ScR guidelines for scoping reviews. A systematic search was conducted across PubMed, EMBASE, MEDLINE-Ovid, and Google Scholar for studies published between January 2018 and January 2025. The search terms included "artificial intelligence," "dental education," "machine learning," "ChatGPT," and "ethical challenges," with Medical Subject Headings (MeSH) terms applied where applicable. After duplicate removal, 624 510 records underwent title/abstract screening, followed by a full-text review of 57 articles, with 43 studies meeting the eligibility criteria. Data extraction focused on the study design, population, AI type, key outcomes, and challenges. Results: The key findings include the following: 1. AI-Driven Personalization: Generative AI (e.g., ChatGPT) reduced grading time by 45% and improved reflective learning outcomes, although 33% of studies reported algorithmic bias due to nonrepresentative training data. 2. In clinical training, AI tools achieved 99% accuracy in caries detection compared with 77–79% accuracy for students, but models trained on homogeneous datasets underperformed in diverse cohorts. 3. Institutional Efficiency : Automated scheduling reduced administrative workloads by 30%, yet only 18% of institutions had updated curricula to include AI literacy modules. 4. Ethical Governance: Data privacy and data protection breaches occurred in 24% of the studies, and 41% reported faculty resistance to AI adoption, highlighting the need for dental-specific guidelines. Conclusion: AI holds significant promise for dental education but requires addressing ethical, logistical, and pedagogical challenges. Future efforts should focus on updating accreditation standards, fostering interdisciplinary collaboration, and developing hybrid models that balance AI-driven efficiency with traditional mentorship. Longitudinal studies are needed to evaluate the long-term impact of AI on clinical competence and patient outcomes. Significance: Dental educators need clearer guidance on integrating AI into the dental curriculum.