Artificial intelligence in dental education: applications, evidence and curricular challenges

Artificial intelligence is entering dental education through simulation, image-based tutoring, and, most recently, large language models, but the evidence for its educational effect and the framework for its curricular integration have not been synthesized. This narrative review traces the evolution of artificial intelligence in dental education in four stages: simulation and e-learning, image-based artificial intelligence in radiology teaching, large language models and generative artificial intelligence, and assessment integrity and curricular integration. The review draws on sources published up to 2025 and on the broader medical education literature. The evidence shows that artificial intelligence can support learning and provide feedback, but that the studies are small, heterogeneous, and mostly short-term, and that the validity of large language models for assessment is uneven. The main challenges are curricular: deciding what to teach, how to teach it, and how to preserve assessment integrity in the presence of generative tools. The review concludes that artificial intelligence should be integrated into dental curricula as a tool to be used critically rather than either embraced uncritically or excluded.