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The effects of ChatGPT-driven blended teaching model on nursing rounds: A quasi-experimental study
Jinneng Yin1, Xianfang Hao1, Guangwei Xing2
1Department of internal medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Aim:
This study aims to evaluate the application effect of Chat Generative Pre-trained Transformer (ChatGPT)-driven blended teaching model in nursing rounds.
Background:
Traditional teacher-centered nursing rounds often lead to passive learning and low efficiency. It remains uncertain whether ChatGPT-based nursing rounds is superior to traditional teaching in nursing rounds.
Design:
A quasi-experimental study was conducted, involving pretest and post-test assessments with both a study group and a control group.
Methods:
A total of 124 nursing students from three tertiary hospitals were divided into two groups. The teaching intervention (TI) group, consisting of 64 participants, received training using a ChatGPT-driven teaching rounds. In contrast, the traditional teaching (TT) group, with 60 participants, underwent conventional nursing rounds. The impact of these two teaching methods on nursing rounds was compared through pre- and post-training evaluations of professional skills and questionnaires.
Results:
Following the intervention, nursing students in the TI group exhibited enhanced clinical competency and critical thinking ability compared with those in the control group (p < 0.05 for both). Furthermore, the learning efficiency of students in the TI group was elevated and they provided a more favorable evaluation of the innovative ward round teaching method.
Conclusion:
Traditional nursing rounds adopt a conventional method, whereas the ChatGPT-integrated blended teaching model revolutionizes them. It boosts students' clinical skills, critical thinking and scenario comprehension, enabling them to tackle complex cases and make sound judgments. Additionally, it optimizes learning efficiency, saving time and facilitating targeted teaching.
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