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<p>Virtual simulation (VS) is pivotal in undergraduate dental education for cognitive and preclinical training. Expanding VS adoption and artificial intelligence (AI) incorporation necessitate an evidence synthesis to clarify current landscapes, challenges, and future directions. Following the PRISMA-ScR guidelines and Arksey and O’Malley’s framework, this scoping review included 57 studies (2007–2025) to map the landscape of VS and its AI-driven future. The included VS systems could be broadly categorized into two generations, with first-generation systems supporting knowledge acquisition and second-generation systems supporting procedural skill development. VS applications spanned multiple dental subspecialties with uneven technological maturity and depth. With assessment of examinations, expert-rated performance, simulator-derived metrics, and learner-reported questionnaires, most studies reported that VS improved learner performance and confidence. Challenges persist regarding uncertain long-term knowledge retention, incomplete replication of clinical settings, resource-intensive implementation, and the limited role in education. This review further explored the implications of future AI–VS incorporation, including AI-driven virtual patients and tutors, fidelity enhancement, adaptive feedback, and personalized competency-based assessment.</p>

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The landscape of virtual simulation in undergraduate dental education with implications for artificial intelligence incorporation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

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  • Shijie Chen,
  • Longshiyu Qiu,
  • Xiaowen Du,
  • Puliang Yao,
  • Xuran Liao,
  • Sixuan Huang,
  • Jiankun Xu,
  • Jieyun Xu,
  • Zetao Chen

摘要

Virtual simulation (VS) is pivotal in undergraduate dental education for cognitive and preclinical training. Expanding VS adoption and artificial intelligence (AI) incorporation necessitate an evidence synthesis to clarify current landscapes, challenges, and future directions. Following the PRISMA-ScR guidelines and Arksey and O’Malley’s framework, this scoping review included 57 studies (2007–2025) to map the landscape of VS and its AI-driven future. The included VS systems could be broadly categorized into two generations, with first-generation systems supporting knowledge acquisition and second-generation systems supporting procedural skill development. VS applications spanned multiple dental subspecialties with uneven technological maturity and depth. With assessment of examinations, expert-rated performance, simulator-derived metrics, and learner-reported questionnaires, most studies reported that VS improved learner performance and confidence. Challenges persist regarding uncertain long-term knowledge retention, incomplete replication of clinical settings, resource-intensive implementation, and the limited role in education. This review further explored the implications of future AI–VS incorporation, including AI-driven virtual patients and tutors, fidelity enhancement, adaptive feedback, and personalized competency-based assessment.