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.
Abstract
In the context of AI-driven transformation in healthcare education, preparing nursing students to effectively engage with generative artificial intelligence tools has become increasingly important. While self-directed learning (SDL) has been consistently associated with clinical competence, the role of AI acceptance in this relationship remains underexplored. To examine the mediating role of AI acceptance in the relationship between self-directed learning ability and perceived clinical competence among nursing students. A cross-sectional, correlational design was employed. Data were collected from 550 nursing students at Alexandria University, Egypt, using validated self-report instruments measuring self-directed learning ability, AI acceptance, and perceived clinical competence. Structural equation modeling was conducted to test the hypothesized relationships and examine the mediating effects. Self-directed learning ability was significantly associated with clinical competence (β = 0.452, p < 0.001), and AI acceptance was positively associated with perceived clinical competence (β = 0.489, p < 0.001). AI acceptance partially mediated the relationship between self-directed learning and perceived clinical competence (indirect effect: β = 0.206, p < 0.001). The model accounted for 51.0% of the variance in clinical competence. The findings indicate that both self-directed learning and AI acceptance are associated with perceived clinical competence, with AI acceptance acting as a mediating factor. These results highlight the relevance of integrating learner-centered approaches with supportive AI-enabled learning environments in nursing education.
Artificial Intelligence (AI) has emerged as a valuable educational tool that supports nursing students’ learning
processes, clinical preparation, and independent learning. However, the mechanism by which AI use contributes to the
development of clinical competence remains underexplored. This study aimed to determine the mediating role of selfregulated learning in the relationship between the use of artificial intelligence and clinical competence among nursing
students. The study was conducted among Bachelor of Science in Nursing students enrolled during Academic Year 2025–
2026 at a higher education institution in Western Mindanao, Philippines. An explanatory sequential mixed-methods design
was utilized. The quantitative phase involved 252 nursing students selected through simple random sampling, while the
qualitative phase involved eight (8) purposively selected participants who participated in semi-structured interviews. Data
were collected using three researcher-adapted questionnaires measuring AI utilization, self-regulated learning, and clinical
competence, along with an interview guide for the qualitative component. Quantitative data were analyzed using Jamovi
software, including frequencies, percentages, means, standard deviations, Pearson product-moment correlations, and
mediation analyses.
Irina Xanthia S. Ramirez, Christine Grace S. Duaves, Genelyn R. Baluyos· International Journal of Inn...· 0 citations
Nursing education needs to support students not only in developing clinical performance but also in managing and directing their own learning. Self-directed learning (SDL) has been identified as a key factor associated with clinical competence. This study aimed to examine the mediating effect of self-efficacy on the relationship between SDL ability and clinical competence among nursing students.
The study was conducted with 138 nursing students who had clinical practice experience in general hospitals. Data were collected using validated instruments measuring SDL ability, clinical competence, and self-efficacy. Data were summarized descriptively, and associations among variables were examined using Pearson correlation coefficients. The mediation effect was tested using Hayes’ PROCESS macro (Model 4) and bootstrapped confidence intervals.
Among the 138 participants, 73.9% were female, 77.5% were aged 20–24 years, and 51.4% were fourth-year students. Nearly half had more than 8 weeks of clinical practice experience (47.1%), and most reported satisfaction with clinical practice (71.0%). Clinical competence showed significant positive correlations with SDL ability (r = .44,
p
< .001) and self-efficacy (r = .46,
p
< .001). Self-efficacy partially mediated the relationship between SDL ability and clinical competence, and the indirect effect was statistically significant (indirect = 0.13, 95% CI: 0.05–0.32). The model's explanatory power increased from 20% to 29% when self-efficacy was included.
The results suggest that self-efficacy is an important psychological mechanism linking SDL ability to clinical competence.
SDL ability had direct and indirect relationships with clinical competence, mediated by self-efficacy. Educational strategies should combine opportunities for independent learning with experiences that help students build confidence in clinical practice.
Background Core competence is a key indicator of nursing students' clinical readiness. While self-directed learning and professional self-concept are correlated with this competence, their underlying mechanisms remain unclear. Purpose To examine the association between self-directed learning and undergraduate nursing students' core competence and the mediating role of professional self-concept in this relationship. Methods A cross-sectional survey of 237 undergraduate nursing students from a medical university in northern China was conducted. Data collected via structured scales were analyzed using correlation, path analysis, and bootstrapping. Results Self-directed learning, professional self-concept, and core competence were all at moderate levels and significantly positively correlated (P < 0.001). Professional self-concept partially mediated the effect of self-directed learning on core competence (indirect effect = 0.422), accounting for 24.4% of the total effect. Conclusions Self-directed learning is positively related to core competence both directly and indirectly through professional self-concept as a partial mediator. Educators should consider fostering active learning and professional identity to better prepare students for clinical practice.
Lulu Zheng, Li Li, Yuantong Zang et al.· Frontiers in Psychology· 0 citations
BACKGROUND
Artificial intelligence tools are increasingly integrated into nursing education. They bring great potential to improve learning efficiency and access to information. However, whether over-reliance on artificial intelligence affects the development of critical thinking remains to be verified. Critical thinking is a core competency for clinical decision-making and safe nursing practice. Although existing evidence suggests a negative association between AI dependency and higher-order cognitive abilities, the underlying mechanisms remain unclear. In particular, the roles of internal cognitive processes and individual psychological characteristics are not well understood. Existing research has largely focused on external factors related to technology adoption. Less attention has been paid to how AI dependency affects metacognitive functioning or the moderating role of academic self-efficacy. There is a particular lack of in-depth research on nursing students, a group that faces multiple cognitive demands from theoretical study, clinical practice, and professional preparation.
OBJECTIVE
This study aimed to explore the relationship between artificial intelligence dependency and critical thinking disposition among nursing students. It examined the mediating role of metacognitive ability and the moderating role of academic self-efficacy. The goal was to reveal the underlying mechanism of this relationship.
METHODS
A questionnaire survey was conducted among nursing students using the Artificial Intelligence Dependence Questionnaire (assessing independent variable: AI dependency), the Metacognitive Ability Scale (assessing mediator: metacognitive ability), the Critical Thinking Disposition Scale (assessing dependent variable: critical thinking disposition), and the Academic Self-Efficacy Scale (assessing moderator: academic self-efficacy). Moderated mediation analysis was performed using Model 5 in the SPSS macro program PROCESS. Structural equation modeling and simple slope analysis were used to examine the path relationships and moderating effects among the variables.
RESULTS
The direct predictive effect of artificial intelligence dependency on critical thinking disposition was not significant (β = 0.021, P = 0.325). However, the indirect effect through metacognitive ability was significant. This indicated that metacognitive ability played an indirect-only mediating role in this sample between the two. Academic self-efficacy played a negative moderating role in the path from artificial intelligence dependency to metacognitive ability (β = -0.008, P < 0.01). It also played a negative moderating role in the path from metacognitive ability to critical thinking disposition (β = -0.003, P < 0.05). Simple slope analysis showed that for students with low academic self-efficacy, the positive predictive effect of artificial intelligence dependency on metacognitive ability was stronger (β = 0.218, P < 0.01). Similarly, the positive predictive effect of metacognitive ability on critical thinking disposition was also stronger for this group. For students with high academic self-efficacy, the positive effects in both paths were weakened.
CONCLUSION
Artificial intelligence dependency was indirectly associated with critical thinking disposition in nursing students through the indirect-only mediating role of metacognitive ability. Academic self-efficacy played a negative moderating role in both the first and second halves of the model. This model revealed a differentiated mechanism through which artificial intelligence dependency affects critical thinking. For students with low academic self-efficacy, AI dependency may serve as a form of cognitive compensation by activating metacognition. For students with high academic self-efficacy, the positive effects of AI dependency were relatively limited. Rather than simply encouraging or restricting AI use, nursing educators should adopt differentiated guidance strategies based on students' self-efficacy levels-supporting metacognitive engagement for low-efficacy students while fostering critical evaluation of AI outputs for high-efficacy students. The goal is to promote reflective AI use that strengthens metacognitive skills, rather than passive dependence.
Xianwei Wang, Rong Zhang, Xiangge Fan et al.· Nurse Education Today· 0 citations
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.
Remziye Semerci Şahin, Aslı Akdeniz Kudubeş, Sevil Çınar Özbay et al.· Nurse Education Today· 0 citations
The transition from academic learning to clinical practice is a pivotal stage in
nursing education, where students must develop autonomy, competence, and
relatedness—core components of self-determination theory (SDT). However,
many pre-clinical nursing students face challenges that hinder these needs,
particularly during hospital training programs. This study explores the selfdetermination needs of pre-clinical nursing students in Indonesia, adopting a
mixed-methods design. Quantitative data from 243 students, gathered through a
modified Self-Determination Scale, were analyzed using partial least squares
structural equation modeling (PLS-SEM). Additionally, interviews with 20
students provided qualitative insights. Findings indicate that autonomy was
significantly influenced by prior clinical experience (β = 0.42, p < 0.001), while
competence was linked to academic preparation (β = 0.38, p < 0.01) and
mentorship quality. Relatedness depended on team cohesion and gender
dynamics, with female students reporting higher support levels (β = 0.31, p <
0.05). Autonomy-supportive supervision (β = 0.47, p < 0.001), structured
mentorship (β = 0.44, p < 0.001), and cohesive team dynamics (β = 0.36, p <
0.01) emerged as key perceived supports for enhancing self-determination.
Qualitative data revealed that supportive supervision fostered confidence, while
micromanagement hindered autonomy. Competence grew with hands-on
guidance, and meaningful peer interactions promoted relatedness. The study
highlights the importance of addressing systemic and cultural factors to improve
students’ clinical learning experiences. These findings contribute to evidencebased strategies for fostering motivation, engagement, and preparedness in
nursing students, enhancing their transition into professional practice
Rhona Sandra Rhona Sandra· FWU Journal of Social Scienc...· 0 citations