Hu Ying, Zeng Yan

 Hainan Vocational University of Science and Technology

Abstract：

Objective With the increasing demand for clinical thinking skills in nursing education, High-Fidelity Simulation (HFS) has become a core instructional approach. However, traditional manual case development is time-consuming, highly homogeneous, and often lacks dynamic disease progression. This study aims to explore the application pathways and teaching strategies of generative artificial intelligence based on ChatGPT in the development of nursing scenario simulation cases. Method Based on the principles of Prompt Engineering, a standardized prompt framework consisting of “Role Definition-Patient Profile-Clinical Context-Teaching Objectives” was constructed. Using acute myocardial infarction (AMI) as an example, ChatGPT-4.0 was employed to generate a full-process simulation lesson plan, and corresponding teaching implementation strategies were designed. Result ChatGPT was able to generate high-quality nursing cases within a short time frame that conformed to medical logic, incorporated dynamic changes in vital signs, and included psychosocial interference factors. It also supported real-time adjustment of disease progression based on student feedback. Conclusion The application of ChatGPT in nursing scenario simulation teaching not only significantly reduces teachers’ lesson preparation burden but also effectively enhances nursing students’ clinical decision-making and adaptive thinking by generating diverse and personalized clinical scenarios. It represents an effective tool for the digital transformation of nursing education.

Key Words：

ChatGPT; nursing education; scenario simulation; case generation; prompt engineering; clinical thinking