The findings suggest that AIlessphobia may be modestly associated with lower academic self-efficacy among nursing students; however, this relationship appears to have limited practical impact.
Abstract
Background
Artificial intelligence (AI) technologies are increasingly integrated into higher education and influence students' learning behaviors. Although AI tools can support learning processes, excessive reliance on them may lead to AIlessphobia, defined as anxiety about completing academic tasks without AI support. Understanding how this emerging psychological factor relates to students' self-regulated learning and academic self-efficacy is particularly important in nursing education.
Aim
This study aimed to examine the relationships between AIlessphobia, AI-enhanced self-regulated learning, and academic self-efficacy among undergraduate nursing students.
Design
A quantitative, descriptive, cross-sectional correlational design was employed.
Setting
The study was conducted in two universities in Türkiye.
Participants
A total of 503 undergraduate nursing students participated in the study.
Methods
Data were collected using a Demographic Information Form, the AIlessphobia in Education Scale (AILPES), the AI-Enhanced Self-Regulated Learning Scale (AI-SRL), and the Academic Nurses' Self-Efficacy Scale (ANSEs). Descriptive statistics, Pearson correlation analysis, confirmatory factor analysis, and structural equation modeling were conducted using SPSS and AMOS to test the hypothesized relationships among the variables.
Results
Participants had a mean age of 20.88 ± 2.61 years, and 66.20% were female. Structural equation modeling indicated that AIlessphobia had a statistically significant but practically negligible negative effect on academic self-efficacy (β = -0.049, f2 = 0.002), accounting for less than 1% of its variance. AIlessphobia did not significantly predict AI-enhanced self-regulated learning (β = 0.046, p = .089), and AI-enhanced self-regulated learning did not significantly influence academic self-efficacy (β = -0.008, p = .729). Model fit indices indicated an acceptable, although not ideal, model fit (χ2/df = 3.899, RMSEA = 0.076, CFI = 0.936, TLI = 0.916).
Conclusion
The findings suggest that AIlessphobia may be modestly associated with lower academic self-efficacy among nursing students; however, this relationship appears to have limited practical impact. AI-enhanced self-regulated learning did not mediate this relationship. These findings highlight the importance of promoting balanced AI use and supporting students' independent learning skills in nursing education.
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
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
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
Artificial intelligence (AI) is increasingly integrated into higher education. However, evidence on theory-informed AI interventions supporting nursing research training remains limited.
PURPOSE
To evaluate an AI-facilitated teaching assistant (INSPIRE-AI) on final-year undergraduate nursing honors students' research self-efficacy, motivation, and research interest, and explore students' experiences of using INSPIRE-AI.
METHODS
An embedded mixed-methods study comprising a one-group quasi-experimental pretest/posttest design with postintervention semistructured qualitative interviews. Nursing students received access to INSPIRE-AI throughout the honors year. Quantitative survey data (N = 146) were analyzed using paired t-tests and repeated-measures general linear models.
RESULTS
Research self-efficacy improved significantly (P < .001). Students reported that INSPIRE-AI supported structured thinking and reduced uncertainty, though engagement varied due to trust concerns, perceived surveillance, and preference for familiar AI tools.
CONCLUSIONS
Together, these findings suggest that INSPIRE-AI has the potential to support research self-efficacy through structured scaffolding; however, this interpretation should be considered alongside the broader educational support that students received throughout the honors program.
J. Ng, Jia-Ning Chew, T. Akkadechanunt et al.· Nurse Educator· 0 citations
Background The increasing integration of artificial intelligence (AI) in medical education has raised important questions regarding its impact on higher-order cognitive processes, particularly critical thinking. Aim and Objectives This study examines the interplay of cognitive load and self-regulated learning in explaining how AI-based educational technology influences critical thinking among medical university students. Methods Using a cross-sectional research design, data were collected from 480 undergraduate medical students with prior experience using AI tools. Data were collected using standardized measures assessing AI usage, cognitive load, self-regulated learning, and critical thinking. Hayes' PROCESS Model 14 was employed to test mediation and moderated mediation effects. Results The findings revealed that AI-based technology use was positively associated with critical thinking and self-regulated learning, while cognitive load showed a negative association with both variables. Mediation analysis indicated that cognitive load partially mediated the relationship between AI use and critical thinking. Furthermore, the moderated mediation analysis demonstrated that self- regulated learning significantly moderated the indirect effect, such that the negative impact of cognitive load on critical thinking weakened at higher levels of self-regulation. Conclusion These findings highlight that the effectiveness of AI in enhancing critical thinking depends not only on its cognitive support but also on learners' ability to regulate their engagement. The study provides theoretical and practical implications for integrating AI in educational contexts.
Arooj Arshad, Ayoob Lone, Loveena Arickswamy et al.· Frontiers in Psychology· 0 citations
This study examined multi-level factors influencing GenAI-supported experiential learning behavior in nursing education to highlight the importance of building AI confidence, fostering peer collaboration, and strengthening institutional readiness for the effective integration of GenAI into nursing education.
Nualyai Pitsachart, Vitsanu Nittayathammakul, Tepin Craivanich et al.· Journal of Education and Tra...· 0 citations