Aim: To explore nursing students' perceptions and experiences of using large language models and identify the facilitators and barriers by applying the Theory of Planned Behaviour.
Design: A qualitative descriptive design.
Method: Between January and June 2024, we conducted individual semi-structured online interviews with 24 nursing students from 13 medical universities across China. Participants were recruited using purposive and snowball sampling methods. Interviews were conducted in Mandarin. Data were analysed through directed content analysis.
Results: Analysis revealed 10 themes according to 3 constructs of the Theory of Planned Behaviour: (a) attitude: perceived value and expectations were facilitators, while perceived caution posed barriers; (b) subjective norm: media effects and role model effectiveness were described as facilitators, whereas organisational pressure exerted by medical universities, research institutions and hospitals acted as a barrier to usage; (c) perceived behavioural control: the design of models and free access were strong incentives for students to use, while geographic access restrictions and digital literacy deficiencies were key factors hindering adoption.
Conclusion: This study explored nursing students' attitudes, subjective norms and perceived behavioural control regarding the use of large language models. The findings provided valuable insights into the factors that hindered or facilitated nursing students' adoption.
Implications for the profession: Through the lens of this study, we have enhanced knowledge of the journey of nursing students using large language models, which contributes to the implementation and management of these tools in nursing education.
Impact: There is a gap in the literature regarding nursing students' views and perceptions of large language models and the factors that influence their usage, which this study addresses. These findings could provide evidence-based support for nurse educators to formulate management strategies and guidelines.
Reporting method: Reporting adheres to the consolidated criteria for reporting qualitative research (COREQ) checklist.
Public contribution: No patient or public contribution.
Keywords: artificial intelligence; large language model; nursing education; qualitative study.
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