Reimagining mentorship in competency-based dental education: An AI-enabled mentoring network framework

Mentoring is increasingly recognized as a critical determinant of academic achievement, professional identity formation, career advancement, and well-being in health professions education. Conventional mentor-mentee models, typically centered on a single assigned faculty mentor, may be inadequate to address the diverse and evolving needs of contemporary dental learners and early-career faculty. The transition towards competency-based dental education (CBDE) under emerging National Dental Commission (NDC) reforms further underscores the need to enhance mentoring approaches that support competency attainment, reflective practice, research engagement, and career development. Recent mentoring literature advocates the development of mentoring networks and individualized mentoring pathways that extend beyond conventional dyadic relationships. Concurrently, advances in artificial intelligence (AI), machine learning, and learning analytics offer new opportunities to deliver personalized educational support at scale. This perspective explores the potential of AI-enabled mentoring networks as a strategy to strengthen mentoring and competency development in Indian dental education. Drawing on established mentoring theories, health professions education literature, and emerging educational technologies, we propose a conceptual framework integrating learner profiling, faculty profiling, intelligent mentor matching, dynamic mentoring road-maps, and longitudinal competency tracking. AI-enabled mentoring systems have the potential to enhance CBDE implementation and support the improvement of competent, adaptable, and future-ready oral health professionals. Future work should focus on ethical implementation, faculty preparedness, and evaluation of educational outcomes associated with technology-enhanced mentoring ecosystems.