This study introduces a transformative framework for medical education by integrating semi-structured data with Large Language Models (LLMs), primarily OpenAIs ChatGPT3.5, to automate the creation of medical simulation scenarios. Traditionally, developing these scenarios was a time-intensive process with limited flexibility to meet diverse educational needs. The proposed approach utilizes AI to efficiently generate detailed, clinically relevant scenarios that are tailored to specific educational objectives. This innovation has significantly reduced the time and resources required for scenario development, allowing for a broader variety of simulations. Preliminary feedback from educators and learners has shown enhanced engagement and improved knowledge acquisition, confirming the effectiveness of this AI-enhanced methodology in simulation-based learning. The integration of structured data with LLMs not only streamlines the creation process but also offers a scalable, dynamic solution that could revolutionize medical training, highlighting the critical role of AI in advancing educational outcomes and patient care standards.

翻译：本研究提出了一种变革性医学教育框架，通过整合半结构化数据与大语言模型（LLMs）（主要为OpenAI的ChatGPT3.5），实现了医学模拟场景的自动化创建。传统上，开发此类场景是一项耗时且灵活性有限的过程，难以满足多样化的教育需求。本研究提出的方法利用人工智能高效生成详细且临床相关的场景，可针对特定教育目标进行定制。这项创新显著减少了场景开发所需的时间与资源，支持更广泛的模拟类型。教育工作者和学习者的初步反馈显示，该方法增强了参与度并提升了知识获取效果，证实了这种人工智能增强方法在模拟学习中的有效性。结构化数据与大语言模型的整合不仅简化了创建流程，还提供了一种可扩展、动态的解决方案，有望革新医学培训，凸显人工智能在推动教育成果与患者护理标准中的关键作用。