Aug 2026· JMIR Nursing· Vol 9, pp. e92533-e92533· 0 citations· 41 references
Medicine
TL;DR
Several contributing factors influenced nursing students’ readiness to embrace AI, with barriers, attitudes, and perceptions emerging as the most consistent, whereas self-efficacy and anxiety may play indirect roles.
Abstract
Abstract Background Enhancing nursing students’ awareness, attitudes, beliefs, and preparedness toward AI may help improve their health care knowledge and practice. Objective This study aimed to assess nursing students’ attitudes, perceptions, self-efficacy, barriers, and anxiety, which influence their readiness to adopt AI in nursing practice. Methods This study used a cross-sectional, correlational design. Data were collected from 307 nursing internship students using an 8-part, self-administered questionnaire. Results Increased self-efficacy with computers was correlated with decreased barriers to accessing AI technology, lower computer anxiety scale scores (r=−0.27, P<.001 and r=−0.57, P<.001, respectively), and higher perceptions of using AI (r=0.27, P<.001). Meanwhile, nursing students’ readiness to adopt AI in nursing practice was negatively associated with barriers to accessing AI technology (r=−0.20, P<.001) and positively associated with attitudes toward and perceptions of using AI (r=0.32, P<.001 and r=0.14, P=.01, respectively). Increased barriers to accessing AI technology were associated with negative attitudes toward AI and nursing students’ perceptions of using AI (r=−0.34, P<.001 and r=−0.39, P<.001, respectively). A multilayer neural network model identified barriers (relative importance=0.27), attitudes (relative importance=0.16), and perceptions (relative importance=0.15) as the most significant predictors, while self-efficacy (relative importance=0.11) and anxiety (relative importance=0.07) showed smaller contributions, despite nonsignificant bivariate associations with nursing students’ AI readiness. The model demonstrated strong predictive performance, achieving a low relative error of 0.62 in the training set. The stability and generalization ability of the model were supported by the training and testing set results, which yielded a training sum of squares error of 65.93 and a testing sum of squares error of 35.49, showing no signs of overfitting. Conclusions Several contributing factors influenced nursing students’ readiness to embrace AI, with barriers, attitudes, and perceptions emerging as the most consistent, whereas self-efficacy and anxiety may play indirect roles. To improve the adoption of AI among nursing students, such factors should be dealt with in such educational programs; an interrelated adoption of AI in nursing practice is expounded as a more favorable environment.
Community health nursing (CHN) is central to primary healthcare delivery, but undergraduate nursing students may encounter educational, organizational, resource-related, and sociocultural barriers that affect their readiness for community-based practice. This study aimed to identify and quantify perceived barriers to CHN practice among nursing students in Saudi Arabia and to examine their relationships with self-reported educational preparedness and organizational support.
A quantitative cross-sectional survey was conducted among nursing students enrolled in Saudi universities. A structured online questionnaire assessed perceived CHN practice barriers, educational preparedness, and organizational support. The instruments underwent cultural adaptation, forward-backward translation, expert-panel content validation, pilot testing, and internal-consistency assessment. Descriptive statistics, non-parametric tests, correlation analyses, and exploratory multiple linear regression were used.
A total of 139 nursing students completed the survey. Participants reported minor-to-moderate overall barriers (
M
= 2.69, SD = 0.96). Resource barriers were rated highest, with shortage of equipment (
M
= 2.93, SD = 1.38) and lack of educational materials (
M
= 2.86, SD = 1.32) identified as the most salient challenges. Educational preparedness (
M
= 3.25, SD = 0.98) and organizational support (
M
= 3.46, SD = 1.03) were rated moderately positive. Small-to-moderate positive correlations were observed between barriers and educational preparedness (
r
= 0.269,
p
= 0.001) and between barriers and organizational support (
r
= 0.242,
p
= 0.004); these findings were interpreted cautiously as potentially reflecting awareness effects, conceptual overlap, or shared-method variance. The multivariable regression model was not statistically significant and explained a small proportion of variance (
R
2
= 0.089,
p
= 0.052).
Nursing students in Saudi Arabia perceived meaningful, though not severe, barriers to effective CHN practice, with resource limitations and skills gaps as primary concerns. The findings support strengthened curriculum-practice integration, expanded simulation and community-immersion experiences, improved placement resources, and targeted faculty development aligned with healthcare transformation priorities.
It is indicated that both self-directed learning and AI acceptance are associated with perceived clinical competence, with AI acceptance acting as a mediating factor.
Boshra Karem Mohamed El-Sayed, Ayman Ateq Alamri, M. G. R. Asal et al.· BMC Medical Education· 0 citations
Objective: Empathy is a cornerstone of nursing care, yet its development is often influenced by factors outside the formal curriculum. The ability to understand and share a patient’s perspective is vital for building trust and improving care outcomes. This study aimed to explore the perception of undergraduate nursing students regarding non-academic factors influencing their development of empathy.
Materials and Methods: A quantitative approach with a cross-sectional descriptive design was adopted. Convenience sampling was used to collect data from 200 undergraduate nursing students (across first, third, fifth, and eighth semesters) from four selected nursing colleges in Ernakulam. Data were collected using a socio-demographic data sheet and the Perceived Non-Academic Influences on Empathy Scale (PNAIES). The Cronbach’s score is 0.8 for the present tool.
Results: The majority of undergraduate nursing students (76%) reported that non-academic factors had a moderate influence, 12.5% of students reported strong influence, and 11.5% reported low influence on their empathy development. Family and upbringing factors were perceived to have the highest influence (50.4%), while technology and media exposure were perceived to have the least influence (24.8%).
Conclusion: The findings highlight that personal background, specifically family and upbringing, plays a more significant role in shaping a student’s empathetic capacity than modern environmental factors like technology. Peer dynamics and clinical exposure are also vital in fostering compassionate professional behavior. Nursing education should acknowledge these diverse personal backgrounds to better support students' professional development and therapeutic relationships.
A. J. V, Usha Marath, Mereena Shibu et al.· Journal of Nursing Research,...· 0 citations
Background
Nursing education is crucial for preparing students to navigate the complexities of healthcare.
Aim
The current study aims to investigate the relationships among higher education-related stress, academic self-efficacy (ASE) and satisfaction with clinical education experiences among nursing students to inform current policy and practice in nurse education and support in Saudi Arabia.
Methods
A descriptive, cross-sectional study was done with undergraduate nursing students at the College of Nursing at Qassim University, in Saudi Arabia. Data were collected by a socio-demographic questionnaire, the student nurse stress index (SNSI) scale, the academic nurse self-efficacy (ANSE) scale and the 'Assessment of Nursing Student Satisfaction with First Clinical Practical Education Questionnaire'.
Results
Nursing students exhibited moderate levels of SNSI (M = 70.53, SD = 17.35), ASNE (M = 52.91, SD = 8.96) and satisfaction with clinical education (M = 142.29, SD = 25.09). Furthermore, Interface Worries (includes students' worries and stress when interfacing with educators, clinical personnel, colleagues or the academic environment; 95% CI: -0.407 to -0.120) and Personal Problems (95% CI: -0.205 to -0.019) were statistically significant negative predictors of nursing students' ANSE.
Conclusion
The study underscores the significant relationship between higher education-related stress, ASE and satisfaction with clinical education.
Rasha Mohammed Hussien· Journal of Research in Nursi...· 0 citations
Findings show that nursing students who feel more prepared for AI tend to be less anxious about technology and add AI training more consistently throughout the nursing curriculum and building digital skills may help reduce fear and make it easier for students to use AI tools in the future.
Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into nursing education, yet structured AI literacy training and ethical guidance remain limited. Consequently, nursing students often rely on informal learning, resulting in variability in AI readiness, confidence, and responsible use. Aims: This study was conducted to examine (1) whether AI literacy was positively associated with AI self-efficacy and AI attitudes and (2) whether AI self-efficacy mediated the relationship between AI literacy and AI attitudes. Methods: A cross-sectional survey using convenience sampling was conducted with 100 prelicensure nursing students in New York City. Data were collected using the AI Literacy Scale (AILS), AI Self-Efficacy Scale (AISES), and Generative AI Attitude Scale (GAIAS). Correlation and path analyses were performed using SPSS and Amos 30.0. Results: The participants had a mean age of 30.25 years, and 71% were women. AI literacy and AI self-efficacy were both positively associated with AI attitudes (all p < 0.001). Path analysis showed that AI literacy significantly predicted AI self-efficacy (β = 0.39, p < 0.001) and AI attitudes (β = 0.28, p = 0.003). AI self-efficacy significantly predicted AI attitudes (β = 0.31, p = 0.001) and partially mediated the relationship between AI literacy and AI attitudes. Conclusions: AI self-efficacy partially mediated the relationship between AI literacy and AI attitudes. Nursing curricula may benefit from structured AI education that integrates guided GenAI practice, case-based learning, and faculty feedback. Such educational frameworks warrant further empirical investigation regarding their potential to foster AI literacy, AI self-efficacy, and positive attitudes toward responsible AI integration, particularly through longitudinal studies assessing subsequent behavioral outcomes.
Shinhi Han, H. Kang, P. Gimber et al.· Nursing Reports· 0 citations