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<p>Artificial intelligence (AI) and large language models (LLMs) such as ChatGPT-5 are increasingly applied in medical education. However, their potential role in clinical simulation remains largely unexplored. This descriptive proof-of-concept study aimed to examine ChatGPT-5’s ability to synthesize and generate educational content related to clinical simulation, focusing on the coherence, factual accuracy, and understandability of its outputs. Seven exploratory questions covering conceptual, historical, and technological aspects of clinical simulation were submitted to ChatGPT-5. Each query was regenerated three times to assess consistency. Responses were independently evaluated by multiple reviewers using a five-point Likert scale for content quality and accuracy, and the Patient Education Materials Assessment Tool (PEMAT) for understandability. Authenticity of AI-generated references was verified through PubMed and Google Scholar. ChatGPT-5 produced coherent and organized responses reflecting major milestones and trends in clinical simulation. Approximately 80% of cited references were verifiable, while some inconsistencies indicated residual fabrication. The average agreement score for accuracy and coherence was 4 (“agree”), suggesting generally acceptable quality. PEMAT analysis showed that content was structured and clear but occasionally used complex terminology, limiting accessibility. Within the exploratory scope of this proof-of-concept study, ChatGPT-5 demonstrated potential as a supportive tool for synthesizing information about clinical simulation. Nonetheless, interpretive depth, citation reliability, and pedagogical adaptation require further refinement. Future research should assess the integration of LLMs into immersive simulation environments under robust ethical and educational frameworks.</p>

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Assessment of ChatGPT-5 as an Artificial Intelligence Tool for Exploring Emerging Dimensions of Clinical Simulation: A Proof-of-concept Study

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Wagner Rios-Garcia,
  • Sashenka Silva-Jiménez,
  • Estefani Gálvez-Rodríguez,
  • Yerson Alberca-Naira,
  • Abigail D. Via-y-Rada-Torres,
  • Alondra A. Rios-Garcia

摘要

Artificial intelligence (AI) and large language models (LLMs) such as ChatGPT-5 are increasingly applied in medical education. However, their potential role in clinical simulation remains largely unexplored. This descriptive proof-of-concept study aimed to examine ChatGPT-5’s ability to synthesize and generate educational content related to clinical simulation, focusing on the coherence, factual accuracy, and understandability of its outputs. Seven exploratory questions covering conceptual, historical, and technological aspects of clinical simulation were submitted to ChatGPT-5. Each query was regenerated three times to assess consistency. Responses were independently evaluated by multiple reviewers using a five-point Likert scale for content quality and accuracy, and the Patient Education Materials Assessment Tool (PEMAT) for understandability. Authenticity of AI-generated references was verified through PubMed and Google Scholar. ChatGPT-5 produced coherent and organized responses reflecting major milestones and trends in clinical simulation. Approximately 80% of cited references were verifiable, while some inconsistencies indicated residual fabrication. The average agreement score for accuracy and coherence was 4 (“agree”), suggesting generally acceptable quality. PEMAT analysis showed that content was structured and clear but occasionally used complex terminology, limiting accessibility. Within the exploratory scope of this proof-of-concept study, ChatGPT-5 demonstrated potential as a supportive tool for synthesizing information about clinical simulation. Nonetheless, interpretive depth, citation reliability, and pedagogical adaptation require further refinement. Future research should assess the integration of LLMs into immersive simulation environments under robust ethical and educational frameworks.