  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.
