From Virtual Simulation to Generative AI: Mapping Research Trends and Frontiers of Intelligent Technologies in Dental Education.
TL;DR
The field has transitioned decisively from hardware-based simulation to software-driven AI, with LLMs and generative AI defining the current frontier, and rising output from middle-income countries suggests AI is lowering barriers to research participation in dental education.
Abstract
Background
Intelligent technologies in dental education have evolved rapidly over two decades, from virtual reality (VR) and haptic simulators to artificial intelligence (AI) and large language models (LLMs). Although recent bibliometric studies have examined technology-enhanced dental education and digital intelligence in the field, none has yet mapped the full developmental trajectory of intelligent technologies: from virtual simulation to generative AI, across two decades of publication data.
Methods
A Boolean query was executed across Web of Science and Scopus (2006-June 2026). After deduplication, 1,374 articles were analyzed using VOSviewer and R across six dimensions: publication trends, national and institutional collaboration, journal evaluation, co-citation clustering, keyword co-occurrence, and thematic evolution.
Results
Three publication phases emerged: emergent (2006-2013, 15.8 papers/year), growth (2014-2020, 28.0/year), and acceleration (2021-2026, peaking at 348 in 2025). The United States led output (267 publications; 19.4%), with China second (181; 13.2%). By 2025, middle-income economies surpassed high-income countries in publication volume for the first time (52% vs 40%). Co-citation analysis revealed a progression from foundational VR validation studies (2001-2011) through contemporary reviews (2015-2022) to emerging AI and LLM research (2020-2023). Six of seven keywords with mean publication year ≥2025 concerned AI or LLMs. A ring network of 140 keywords (mean year ≥2021) positioned AI, VR, and digital technology as hubs connecting dental education, clinical dentistry, and others.
Conclusions
The field has transitioned decisively from hardware-based simulation to software-driven AI, with LLMs and generative AI defining the current frontier. Rising output from middle-income countries suggests AI is lowering barriers to research participation in dental education. CLINICAL
Significance
The study highlights the rapid transition from traditional simulation to AI-driven platforms in dental training. Understanding these global research frontiers allows dental schools and clinical departments to make evidence-based decisions regarding technology adoption, ensuring that educational innovations align with the practical skills needed for high-quality patient care.