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.

采用半结构化数据与大型语言模型（LLMs）相结合，本研究引入了一种医学教育变革框架，以自动化生成医学模拟场景。该创新显著缩短了场景开发所需的时间和资源，实现了更广泛的模拟类型，提高了参与度和知识获取，突显了人工智能在推动教育成果和患者护理标准方面的关键作用。