Currently submitted to: JMIR Nursing
Date Submitted: Jan 26, 2026
Open Peer Review Period: Feb 25, 2026 - Apr 22, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Nursing Students' Awareness, Perceptions, and Readiness for Artificial Intelligence Integration: An Extended UTAUT Analysis
ABSTRACT
Background:
Intelligent technologies are transforming healthcare delivery, necessitating that nursing curricula prepare students for digitally enhanced practice environments. However, empirical evidence examining nursing students' readiness for technology adoption, particularly through established theoretical frameworks, remains limited in Middle Eastern educational contexts.
Objective:
The objectives of this study were to (1) assess the level of awareness among nursing students regarding the use of artificial intelligence (AI) applications in nursing education; (2) examine nursing students' perceptions of the potential benefits and challenges associated with AI adoption, guided by the core constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT); and (3) evaluate nursing students' readiness and preparedness for artificial intelligence adoption by analysing the relationships between UTAUT-based theoretical constructs and behavioural intention to use AI technologies in educational and clinical training contexts.
Methods:
A cross-sectional survey was conducted among 314 undergraduate nursing students at a university in the United Arab Emirates. Data were collected using a validated questionnaire (Cronbach α=.89; content validity index=0.92) measuring UTAUT constructs including performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention. Analysis utilized IBM SPSS Statistics version 28.0, including descriptive statistics, Pearson correlation, and multiple regression
Results:
Students demonstrated high awareness (298/314, 94.9%) and training interest (261/314, 83.1%), with favourable perceptions of artificial intelligence's educational benefits. However, practical confidence remained lower (186/314, 59.2%), and three-quarters indicated needing substantial support. Performance expectancy (mean 3.96, SD 0.72) and facilitating conditions significantly predicted behavioural intention. The regression model explained 58% of variance in behavioural intention (R²=0.58; F6,307=71.24; P<.001). Performance expectancy emerged as the strongest predictor (β=.38; P<.001), followed by social influence (β=.20; P<.001) and effort expectancy (β=.19; P=.001). A pronounced gap emerged between theoretical readiness (mean 3.78-4.09) and actual preparedness (mean 2.00-2.56), with insufficient training (mean 2.03, SD 0.76) and limited practical experience (mean 2.13, SD 1.34) as primary deficits.
Conclusions:
The Extended UTAUT framework effectively explained nursing students' artificial intelligence adoption intentions. While students demonstrate positive attitudes, the readiness-preparedness gap highlights urgent needs for structured competency development, faculty training, and phased implementation strategies in nursing education.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.