Impact of ChatGPT on case creation efficiency and learning quality in case-based learning for undergraduate nursing students
Asahiko Higashitsuji, Tomoko Otsuka, Kentaro Watanabe
Abstract
ABSTRACT Background Artificial intelligence (AI) enhances quality of life by reducing labor and managing complex networks. Generative AI, like ChatGPT, could improve education outcomes. However, its effectiveness in nursing education through case-based learning (CBL) remains unclear. Aim To assess the effectiveness of ChatGPT in CBL through case creation and student group discussions. Methods A feasibility trial was conducted using a single-group pre-post design and a blinded nonrandomized crossover design. Eight faculty members and nine students from a Japanese college were recruited from June to November 2023. The sample size was determined based on feasibility trials recommendations. Faculty members created cases manually and using ChatGPT while students conducted group discussions on each case. Case creation time, faculty members’ burden, and group discussion quality were evaluated. Results Case creation time differed significantly with 106 min and 71 min for manual and. ChatGPT, respectively (95% CI = 1.0–1,299.6, p = 0.042). There were no significant differences in the perceived burden of creation and discussion quality. Conclusion ChatGPT reduced case creation time without affecting learning quality, suggesting applicability beyond nursing.