Jul 2026· Frontiers in Public Health· Vol 14· 0 citations· 51 references
Medicine
TL;DR
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.
Abstract
Background The rapid integration of artificial intelligence (AI) into health care demands that future nurses develop foundational AI competencies. However, the mechanisms through which AI literacy alleviates AI-related anxiety among nursing students remain inadequately understood, particularly regarding the sequential psychological processes involved. Objective This study aimed to test whether attitudes toward AI and AI self-efficacy mediate the relationship between AI literacy and AI anxiety in nursing students, and to examine the serial mediation pathway among these variables. Methods A multicenter cross-sectional survey was conducted among 1,482 nursing students from 11 universities in Sichuan Province, China, between May and October 2025. Validated Chinese versions of the Artificial Intelligence Literacy Scale (AILS), General Attitudes toward Artificial Intelligence Scale (GAAIS), Artificial Intelligence Self-Efficacy Scale (AISES), and Artificial Intelligence Anxiety Scale (AIAS) were administered. Structural equation modeling (SEM) was employed to examine direct and indirect effects. Results AI literacy was positively correlated with favorable AI attitudes (r = 0.285) and AI self-efficacy (r = 0.509), and negatively correlated with AI anxiety (r = −0.434). The SEM showed a significant negative direct association between AI literacy and AI anxiety (β = −0.329, P < 0.001). Significant indirect associations were observed through AI attitudes and AI self-efficacy separately and through the serial pathway AI literacy → attitudes → self-efficacy → anxiety. These findings describe model-based associations and should not be interpreted as evidence of temporal or causal ordering. Conclusion 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. The observed ordering suggests that more favorable attitudes may be linked to stronger self-efficacy, which may in turn be related to lower anxiety; however, the cross-sectional design does not establish causality. Nursing education may therefore consider combining AI knowledge and skills training with opportunities to develop balanced attitudes and confidence in using AI.
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
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
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
Background The rapid integration of Artificial Intelligence (AI) in higher education offers transformative learning potential but has concurrently triggered psychological challenges, specifically AI anxiety and AI dependence. While these phenomena are increasingly prevalent, the psychological mechanisms converting affective strain into behavioral reliance remain underexplored. Grounded in Cognitive Behavioral Theory and Conservation of Resources Theory, this study investigates the relationship between AI anxiety and AI dependence among undergraduates. Specifically, it examines whether AI self-efficacy mediates this relationship and whether AI literacy acts as a boundary condition moderating these pathways. Methods A cross-sectional survey was conducted with 400 undergraduates recruited via stratified sampling from a normal university in Southwest China. Participants completed validated scales measuring AI anxiety, AI self-efficacy, AI literacy, and AI dependence. Confirmatory factor analysis was performed to verify instrument validity. Hypotheses were tested using the PROCESS macro (Models 4 and 8) to analyze mediation and moderated mediation effects, utilizing bootstrapping techniques with 5,000 resamples to determine the statistical significance of direct and indirect effects. Results The analysis revealed a significant positive association between AI anxiety and AI dependence. AI self-efficacy was found to partially mediate this relationship, indicating that anxiety exacerbates dependence by eroding students’ confidence in their capabilities. Furthermore, AI literacy served as a significant moderator. Results indicated that the negative impact of AI anxiety on AI self-efficacy was stronger for students with higher AI literacy, suggesting that high literacy may sensitize students to competency gaps under emotional strain. AI literacy did not moderate the direct relationship between anxiety and dependence. Conclusion This study identifies AI dependence as a maladaptive coping response to AI anxiety, driven by diminished AI self-efficacy. The findings challenge the assumption that literacy acts solely as a protective buffer, revealing that without emotional regulation, high literacy may intensify the erosion of self- confidence during anxious states. To mitigate maladaptive dependence, higher education institutions should move beyond technical training to adopt holistic strategies. These should include psychological support to build resilience, mastery experiences to foster self-efficacy, and comprehensive curricula that address the emotional and cognitive dimensions of human-AI interaction.
Houyu Wu, Haiyan Ni, Jiaqi He et al.· Frontiers in Psychology· 2 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.
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.