Registered Nurses' Attitudes Towards ChatGPT and Self-Directed Learning: A Cross-Sectional Study

Li-Chun Chang; Ya-Ni Wang; Hui-Ling Lin; Li-Ling Liao

doi:10.1111/jan.16519

Registered Nurses' Attitudes Towards ChatGPT and Self-Directed Learning: A Cross-Sectional Study

J Adv Nurs. 2024 Oct 9. doi: 10.1111/jan.16519. Online ahead of print.

Authors

Li-Chun Chang^{1

2

3}, Ya-Ni Wang¹, Hui-Ling Lin^{1

2

3

4}, Li-Ling Liao^{5

6}

Affiliations

¹ School of Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan, R.O.C.
² Department of Nursing, Linkou Branch, Chang Gung Memorial Hospital, Taoyuan, Taiwan, R.O.C.
³ School of Nursing, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C.
⁴ Taipei Medical University, Taipei, Taiwan, R.O.C.
⁵ Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung City, Taiwan, R.O.C.
⁶ Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung City, Taiwan, R.O.C.

PMID: 39382347
DOI: 10.1111/jan.16519

Abstract

Background: Self-directed, lifelong learning is essential for nurses' competence in complex healthcare environments, which are characterised by rapid advancements in medicine and technology and nursing shortages. Previous studies have demonstrated that ChatGPT technology fosters self-directed learning by motivating users to engage with it.

Objectives: To explore the relationships amongst socio-demographic data, attitudes towards ChatGPT use, and self-directed learning amongst registered nurses in Taiwan.

Methods: A cross-sectional study design with an online survey was adopted. Registered nurses from various healthcare settings were recruited through Facebook and LINE, a widely used messaging application in East Asia, reaching over 1000 nurses across five distinct online groups. An online survey was used to collect data, including socio-demographic characteristics, attitudes towards ChatGPT use, and a self-directed learning scale. Data were analysed using descriptive statistical methods, t-tests, Pearson's correlation, one-way analysis of variance, and multiple linear regression analysis.

Results: Amongst the 330 participants, 50.6% worked in hospitals, 51.8% had more than 15 years of work experience, and 78.2% did not hold supervisory positions. Of the participants, 46.7% had used ChatGPT. For all nurses, work experience and awareness of ChatGPT statistically significantly predicted self-directed learning, explaining 32.0% of the variance. For those familiar with ChatGPT, work experience in nursing and the technological/social influence of ChatGPT statistically significantly predicted self-directed learning, explaining 35.3% of the variance.

Conclusions: Work experience in nursing provides critical opportunities for professional development and training. Therefore, ChatGPT-supported self-directed learning should be customised for degrees of experience to optimise continuous education.

Implications for nursing management and health policy: This study explores nurses' diverse use of and attitudes towards ChatGPT for self-directed learning. It suggests that administrators customise support and training when incorporating ChatGPT into professional development, accounting for nurses' varied experiences to enhance learning outcomes.

Patient or public contribution: No patient or public contribution.

Reporting method: This study adhered to the relevant cross-sectional STROBE guidelines.

Keywords: ChatGPT; attitudes; professional development; registered nurses; self‐directed learning.