The literacy-profile dependent mediation identified highlights differentiated literacy-attitude-behavior pathways and underscores the necessity of adopting tailored, profile-specific professional development strategies instead of one-size-fits-all approaches.
Abstract
Background
Nursing educators play a pivotal role in the effective integration of artificial intelligence (AI) into nursing education. Investigating the heterogeneity of their AI literacy and its impact on attitude and behavior is a prerequisite for developing targeted interventions.
Objective
This study aimed to identify latent profiles of AI literacy among nursing educators and to examine the mediating role of AI attitude in the relationship between literacy and behavior.
Methods
A cross-sectional survey was conducted with 339 nursing educators in China. Latent profile analysis (LPA) was performed in Mplus to identify distinct AI literacy subgroups. Mediation analyses were performed using both variable-centered and person-centered approaches.
Results
Three distinct AI literacy profiles were identified: foundational (12.4%), developing (47.5%), and proficient (40.1%). The variable-centered analysis revealed that AI attitude partially mediated the relationship between AI literacy and behavior. Person-centered analysis further indicated that this mediating effect was significant only for the proficient literacy profile, but not for the developing profile, highlighting subgroup-specific mechanisms in the literacy-behavior pathway.
Conclusion
The literacy-profile dependent mediation identified highlights differentiated literacy-attitude-behavior pathwaysand underscores the necessity of adopting tailored, profile-specific professional development strategies instead of one-size-fits-all approaches.
Higher AI literacy was associated with lower AI anxiety, and this association was partly accounted for by AI attitudes and AI self-efficacy in the proposed serial mediation model, which suggests that more favorable attitudes may be linked to stronger self-efficacy, which may be related to lower anxiety.
Qin Zeng, Shenghua Zhang, Jiachen Hu et al.· Frontiers in Public Health· 0 citations
Background Current research does not examine how distinct AI literacy profiles are differentially associated with learning engagement, thereby impeding the development of stratified and precise training plans for nursing students. Objective To identify latent profiles of artificial intelligence literacy among undergraduate nursing students, characterize their distributional features, and examine the relationship between distinct AI literacy profiles and learning engagement. Methods The study included 479 Chinese undergraduate nursing students who finished the Utrecht Work Engagement Scale-Student Version and the Artificial Intelligence Literacy Scale. Latent profile analysis was conducted using item-level AI literacy scores as manifest indicators. Results Three distinct profiles of AI literacy were identified: low literacy—ethically cautious, medium literacy—balanced development, and high literacy—fully mature. Non-parametric test results demonstrated significant differences in learning engagement and its dimensions across the three AI literacy profiles. After controlling for relevant confounding factors in multilevel linear regression analyses, AI literacy profile remained significantly associated with learning engagement, accounting for an additional 31.2% of the variance. Students in the medium and high AI literacy groups demonstrated significantly higher levels of learning engagement compared to those in the low literacy group. Conclusion Undergraduate nursing students’ AI literacy is heterogeneous and markedly related to learning engagement. These findings provide valuable insights for improving student engagement in AI-supported learning environments.
Min Li, Yue Cao, Rui-Lin Zhang et al.· Frontiers in Public Health· 0 citations
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
The findings indicate that nursing students had generally positive levels of AI literacy and attitudes toward AI, and higher AI literacy was associated with more positive attitudes toward AI.
M. Çil, Berna Eren Fidancı, D. Yildiz· Journal of Education and Res...· 0 citations
The integration of artificial intelligence (AI) technologies into clinical nursing is changing nursing practice and creating new competency requirements for nurses. Some nurses may still face difficulties in technical adaptation, ethical judgment, and practical use of AI tools in intelligent healthcare environments. Understanding nurses’ AI literacy and its relationship with thriving at work may help hospitals design more targeted support strategies.
This study aimed to investigate the current status of nurses’ artificial intelligence literacy, identify latent profiles of self-reported AI literacy, analyze factors associated with profile membership, and examine differences in thriving at work across AI literacy profiles.
A cross-sectional study.
In January 2026, 1000 nurses from 62 hospitals in Anhui Province, China were recruited by convenience sampling. Data were collected using a general information questionnaire, the Artificial Intelligence Literacy Scale, the Chinese version of the Thriving at Work Scale, a nine-item measure of attitudes toward AI in nursing, and the General Self-Efficacy Scale. Latent profile analysis was used to classify nurses’ AI literacy profiles. Factors associated with profile membership were examined using univariate analysis and multinomial logistic regression. Scores on the Thriving at Work Scale were compared across AI literacy profiles.
Three distinct latent profiles were identified: Ethical Awareness Deficit profile (
n
= 332, 33.20%), Cognition Practice Gap profile (
n
= 538, 53.80%), and Comprehensive Literacy Advantage profile (
n
= 130, 13.00%). Educational level, key nursing position/department management role, years of work experience, AI training experience, AI usage frequency in the past 6 months, attitude toward AI in nursing, and self-efficacy were associated with AI literacy profile membership (all
P
< 0.05). Scores on the Thriving at Work Scale differed significantly across the three profiles (
P
< 0.05).
This multicenter cross-sectional study identified three latent profiles of self-reported artificial intelligence literacy among nurses from hospitals in Anhui Province, China. Thriving at work differed significantly across these profiles. The findings may inform stratified educational and managerial strategies to support nurses’ AI literacy and thriving at work in similar clinical contexts.
Not applicable.
OBJECTIVE
The integration of digital technologies and artificial intelligence (AI) into healthcare has transformed nursing education, while also raising concerns about AI-related anxiety among students. This study aimed to examine the mediating role of self-efficacy in the relationship between digital literacy and AI anxiety among nursing students.
METHODS
This correlational study was conducted with 319 undergraduate nursing students enrolled at two universities in Türkiye. Data were collected using the Artificial Intelligence Anxiety Scale, Digital Literacy Scale, and Student Self-Efficacy Scale. Pearson correlation analysis and regression-based mediation analysis were performed using PROCESS macro (Model 4) with 5000 bootstrap samples.
RESULTS
Digital literacy was negatively associated with AI anxiety (r = - .20, p < .01) and positively associated with self-efficacy (r = .54, p < .01). Self-efficacy was also negatively related to AI anxiety (r = - .23, p < .01). Mediation analysis revealed that self-efficacy fully mediated the relationship between digital literacy and AI anxiety. While the total effect of digital literacy on AI anxiety was significant (B = - 0.40, p < .001), the direct effect became nonsignificant after controlling for self-efficacy, whereas the indirect effect through self-efficacy remained significant (B = - 0.19, 95% CI [- 0.35, - 0.05]).
CONCLUSIONS
Self-efficacy plays a critical mediating role in the relationship between digital literacy and AI anxiety among nursing students. Interventions aimed at enhancing both digital literacy and self-efficacy may be effective in reducing AI-related anxiety and supporting nursing students' psychological adaptation to emerging technologies.
Gamze Akay, Uğur Saruhan, Yeşim Saruhan et al.· BMC Nursing· 0 citations