Nurse education in practice · 2025
Use of a generative pre-trained transformer-based virtual patient for health assessment and communication training in nursing education: A mixed-methods study.
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Kim Jiyoung, Won Jiyeong, Lee Yuran
Аннотация
AIM: This study evaluated the use of a generative pre-trained transformer (GPT)-based virtual patient in nursing education.
BACKGROUND: In nursing education, conventional training methods such as interactions with real-life or standardized patients exhibit limitations such as psychological distress, repetitive training and insufficient cost- and time-effectiveness. Because of their capacity to emulate human-like dialogue, GPTs have emerged as a valuable resource for educational nursing activities.
DESIGN: This study employed a mixed-methods design.
METHODS: A GPT-based virtual patient with acute appendicitis was included. Twenty-eight new prospective nurses in South Korea, equipped with a head-mounted display, evaluated and communicated with the virtual patient. Usability, perceived virtual learning environment and self-efficacy in communication were measured. The GPT-generated dialogues and open-ended questions were subjected to qualitative analysis.
RESULTS: Among the subfactors of usability, the subdomains of perceived accessibility of functions and perceived virtual learning environments achieved high scores. Furthermore, a notable increase in self-efficacy for communication was observed (t = -2.82, p = .009). The participants' experiences with the GPT-based virtual patient were divided into "educational effects and learner experience" and "technical limitations and the need for improvement." Evaluation of the dialogue between the GPT-based virtual patient and participants revealed that the readability subdomain achieved the highest score, whereas the accuracy subdomain achieved the lowest score.
CONCLUSIONS: The findings of the present study provide insights into the advantages of employing GPT-based virtual patients, particularly regarding the perceived accessibility of functions, high scores for immersion and enhanced self-efficacy of communication.
BACKGROUND: In nursing education, conventional training methods such as interactions with real-life or standardized patients exhibit limitations such as psychological distress, repetitive training and insufficient cost- and time-effectiveness. Because of their capacity to emulate human-like dialogue, GPTs have emerged as a valuable resource for educational nursing activities.
DESIGN: This study employed a mixed-methods design.
METHODS: A GPT-based virtual patient with acute appendicitis was included. Twenty-eight new prospective nurses in South Korea, equipped with a head-mounted display, evaluated and communicated with the virtual patient. Usability, perceived virtual learning environment and self-efficacy in communication were measured. The GPT-generated dialogues and open-ended questions were subjected to qualitative analysis.
RESULTS: Among the subfactors of usability, the subdomains of perceived accessibility of functions and perceived virtual learning environments achieved high scores. Furthermore, a notable increase in self-efficacy for communication was observed (t = -2.82, p = .009). The participants' experiences with the GPT-based virtual patient were divided into "educational effects and learner experience" and "technical limitations and the need for improvement." Evaluation of the dialogue between the GPT-based virtual patient and participants revealed that the readability subdomain achieved the highest score, whereas the accuracy subdomain achieved the lowest score.
CONCLUSIONS: The findings of the present study provide insights into the advantages of employing GPT-based virtual patients, particularly regarding the perceived accessibility of functions, high scores for immersion and enhanced self-efficacy of communication.