Progress of a Novel Dentistry Teaching Model Based on the Combination of Virtual Reality and Artificial Intelligence Technologies in Optimizing Cognitive Load: A Systematic Review.

Background: Virtual reality (VR) and artificial intelligence (AI) technologies have advanced significantly over the past few decades, expanding into various fields, including dental education.

Purpose: To comprehensively review the application of VR and AI technologies in dentistry training, focusing on their impact on cognitive load management and skill enhancement. This study systematically summarizes the existing literature by means of a scoping review to explore the effects of the application of these technologies and to explore future directions.

Methods: This study utilized a scoping review approach and a literature search using databases such as PubMed, IEEE Xplore, and Google Scholar. Keywords included "virtual reality", "artificial intelligence", "dental training", "medical education", and "cognitive load". "cognitive load". The collected literature was screened and evaluated according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, focusing on studies with rigorous methodology, detailed data analysis, and valid conclusions.

Results: VR offers immersive, interactive 3D environments, allowing users to safely practice complex procedures in a controlled virtual setting. AI enhances this experience by automating and optimizing functions such as image recognition, natural language processing, and decision support through machine learning algorithms and big data analytics. Together, VR and AI significantly enhance students' technical skills and clinical decision-making abilities by providing real-time feedback and personalized learning pathways. Despite these benefits, challenges such as high costs, system complexity, and user acceptance still pose significant obstacles to widespread implementation.

Discussion: The integration of VR and AI technologies presents significant potential for optimizing cognitive load and enhancing clinical skills in dental education. Effective strategies, including task decomposition, dynamic content adaptation, and real-time feedback mechanisms, are central to maximizing learning outcomes. Current training systems have demonstrated the practical benefits of applying these technologies in dental training. However, further research is required to evaluate their long-term impact, cost-effectiveness, and scalability for broader application in dental education.

Conclusion: VR and AI technologies hold significant promise for revolutionizing dental education by offering an integrated training platform that enhances both skill acquisition and cognitive load management. To fully harness the potential benefits of these technologies, ongoing advancements and their strategic application in real-world training scenarios are essential.