Work

The Effect of Using ChatGPT on Nursing Students’ Learning and Satisfaction in a Flipped Physiology Classroom

Year: 2026

Type: article

Abstract: Background: Nursing students in Iran are facing various challenges while learning complex physiological concepts. The flipped classroom approach can rarely be seen in the Iranian curriculum of nursing, and there is a lack of research on how to use AI-powered chatbots such as ChatGPT to offer the most appropriate nursing physiology education. Purpose: This study evaluated the effects of integrating ChatGPT into a flipped physiology classroom on nursing students' learning outcomes and satisfaction. Methods: A pre-test/post-test intervention was conducted among nursing students. A total of 51 students were divided into an intervention (n=26) and a control group (n=25). Both groups were taught neurophysiology using a flipped classroom instruction, over four ADDIE model-based sessions. However, in the intervention group, the students were trained in prompt writing and asked to use ChatGPT to answer prompts in the presence of an instructor. The learning outcome and satisfaction of the students were evaluated using questionnaires. Data were analyzed in SPSS version 27, and P<0.05 was considered statistically significant. Results: Following the intervention, the study group demonstrated significantly higher learning scores than the control group (7.15 ± 1.43 vs 5.52 ± 1.61; P < 0.001). Additionally, satisfaction scores were significantly higher in the intervention group (4.69 ± 0.35 vs 4.41 ± 0.49; P = 0.022). A non-parametric comparison of learning gain scores further confirmed a significantly greater improvement in the experimental group compared to the control group (U=132.50, Z=-3.70, P<0.001). Conclusion: A ChatGPT-supported flipped classroom improved learning outcomes and student satisfaction in nursing physiology education. These findings suggest that integrating artificial intelligence into medical education can enhance teaching effectiveness. Further studies with larger samples and other medical disciplines are recommended.

Source: Advances in Medical Education and Practice

DOI: 10.2147/amep.s650889

Authors: Masoumeh Gholami, Maryam Aalaa, Aeen Mohammadi

Institutions: Arak University of Medical Sciences, Tehran University of Medical Sciences

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Quality education, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,320