Understanding nursing students’ acceptance of generative AI-assisted e-picture book creation in pediatric health education
6 affiliations
- 1Department of Nursing, Central Taiwan University of Science and Technology, No. 666, Buzi Rd., Beitun Dist, Taichung City, 406053 Taiwan
- 2School of Nursing, National Taipei University of Nursing and Health Sciences, No. 365, Mingde Rd., Beitou Dist, Taipei City, 112303 Taiwan
- 3Department of Electrical and Mechanical Technology, National Changhua University of Education, No.2, Shi-Da Rd, Changhua City, 500208 Taiwan
- 4Graduate Institute of Technological and Vocational Education, National Changhua University of Education, Bao-Shan Campus, No.2, Shi-Da Rd, Changhua City, 500208 Taiwan
- 5NCUE Alumni Association, National Changhua University of Education, Jin-De Campus, No. 1, Jinde Rd, Changhua City, 500207 Taiwan
- 6International Medical Service Center, Taichung Veterans General Hospital, No. 1650, Sec. 4, Taiwan Blvd., Xitun Dist, Taichung City, 407219 Taiwan
Abstract
Background: Generative artificial intelligence (AI) is increasingly integrated into health professions education; however, limited research has examined nursing students’ acceptance of generative AI as a task-based creative tool for developing child-centered health education materials. In pediatric care, translating complex medical information into developmentally appropriate and emotionally supportive formats remains a critical yet underexplored competency.
Objective: This study aimed to examine nursing students’ acceptance of using generative AI to create pediatric health education e-picture books and to identify associated factors based on the Technology Acceptance Model.
Methods: A cross-sectional study was conducted with 80 nursing students from a private university in central Taiwan. Participants engaged in a structured learning activity using generative AI to develop pediatric e-picture books prior to completing a TAM-based questionnaire. Actual use was operationalized as participants’ self-reported engagement during the AI-assisted e-picture book creation activity. Data were collected between January and December 2025 and analyzed using descriptive statistics, Pearson correlation analysis, and multiple linear regression.
Results: Nursing students demonstrated moderate to high levels of acceptance. Perceived ease of use was strongly associated with perceived usefulness (β = 0.878, p < 0.001). Both perceived usefulness and perceived ease of use were significantly associated with attitudes toward use, which was strongly associated with behavioral intention (β = 0.902, p < 0.001). Behavioral intention showed a very strong association with actual use (β = 0.971, p < 0.001); given the cross-sectional design, these findings reflect statistical association rather than causal pathways, and the exceptionally high behavioral intention–actual use correlation also suggests limited discriminant separation between these two constructs. Additionally, year of study was significantly associated with perceived usefulness, with first- and third-year students reporting lower scores than fourth-year students (p < 0.05).
Conclusions: This study provides novel empirical evidence on nursing students’ acceptance of generative AI in pediatric nursing education. Findings are consistent with the applicability of TAM in this task-based learning context, pending confirmation through structural equation modeling in larger, more balanced samples, and suggest that generative AI may serve as a useful educational tool in pediatric health education.
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BibTeX
@article{lai2026understanding,
title = {{Understanding nursing students’ acceptance of generative AI-assisted e-picture book creation in pediatric health education}},
author = {Ching-Yi Lai and Li-Yun Tsai and Ya-Hui Tseng and Wei-Sho Ho and Wan-Yun Hsu},
journal = {BMC Nursing},
year = {2026},
month = aug,
doi = {10.1186/s12912-026-05191-w},
url = {https://doi.org/10.1186/s12912-026-05191-w},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Understanding nursing students’ acceptance of generative AI-assisted e-picture book creation in pediatric health education
AU - Ching-Yi Lai
AU - Li-Yun Tsai
AU - Ya-Hui Tseng
AU - Wei-Sho Ho
AU - Wan-Yun Hsu
PY - 2026
DA - 2026/08/12
JO - BMC Nursing
DO - 10.1186/s12912-026-05191-w
UR - https://doi.org/10.1186/s12912-026-05191-w
AB - Background: Generative artificial intelligence (AI) is increasingly integrated into health professions education; however, limited research has examined nursing students’ acceptance of generative AI as a task-based creative tool for developing child-centered health education materials. In pediatric care, translating complex medical information into developmentally appropriate and emotionally supportive formats remains a critical yet underexplored competency. Objective: This study aimed to examine nursing students’ acceptance of using generative AI to create pediatric health education e-picture books and to identify associated factors based on the Technology Acceptance Model. Methods: A cross-sectional study was conducted with 80 nursing students from a private university in central Taiwan. Participants engaged in a structured learning activity using generative AI to develop pediatric e-picture books prior to completing a TAM-based questionnaire. Actual use was operationalized as participants’ self-reported engagement during the AI-assisted e-picture book creation activity. Data were collected between January and December 2025 and analyzed using descriptive statistics, Pearson correlation analysis, and multiple linear regression. Results: Nursing students demonstrated moderate to high levels of acceptance. Perceived ease of use was strongly associated with perceived usefulness (β = 0.878, p < 0.001). Both perceived usefulness and perceived ease of use were significantly associated with attitudes toward use, which was strongly associated with behavioral intention (β = 0.902, p < 0.001). Behavioral intention showed a very strong association with actual use (β = 0.971, p < 0.001); given the cross-sectional design, these findings reflect statistical association rather than causal pathways, and the exceptionally high behavioral intention–actual use correlation also suggests limited discriminant separation between these two constructs. Additionally, year of study was significantly associated with perceived usefulness, with first- and third-year students reporting lower scores than fourth-year students (p < 0.05). Conclusions: This study provides novel empirical evidence on nursing students’ acceptance of generative AI in pediatric nursing education. Findings are consistent with the applicability of TAM in this task-based learning context, pending confirmation through structural equation modeling in larger, more balanced samples, and suggest that generative AI may serve as a useful educational tool in pediatric health education.
SN - 1472-6955
ER -