The relationship between AI dependency and critical thinking disposition in nursing students: The mediating role of metacognitive ability and the moderating role of academic self-efficacy.
Artificial intelligence dependency was indirectly associated with critical thinking disposition in nursing students through the indirect-only mediating role of metacognitive ability, which revealed a differentiated mechanism through which artificial intelligence dependency affects critical thinking.
Abstract
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.
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
BACKGROUND
Generative artificial intelligence tools are increasingly used in nursing education, but excessive reliance on externally generated reasoning may be associated with reduced cognitive engagement. Evidence remains limited on whether within-person changes in generative artificial intelligence dependency are prospectively associated with nursing students' metacognitive awareness and self-perceived clinical decision-making.
OBJECTIVES
To examine longitudinal within-person associations among generative artificial intelligence dependency, metacognitive awareness, and self-perceived clinical decision-making, and to test whether metacognitive awareness mediates these associations.
DESIGN
A three-wave prospective longitudinal panel study.
SETTINGS
Nursing schools at three medical universities in China.
PARTICIPANTS
A convenience sample of 687 undergraduate nursing students completed the baseline survey; 618 students completed the third wave, yielding a retention rate of 90.0%.
METHODS
Data were collected at the beginning, middle, and end of one academic semester. A random-intercept cross-lagged panel model was used to separate stable between-person differences from within-person fluctuations. Missing data were handled using full-information maximum likelihood. Common method bias was examined using Harman's single-factor test and an unmeasured latent method construct.
RESULTS
At the within-person level, higher generative artificial intelligence dependency was prospectively associated with lower metacognitive awareness at the subsequent wave (β = -0.18 to -0.21, p < .001). Higher metacognitive awareness was prospectively associated with higher self-perceived clinical decision-making (β = 0.22 to 0.25, p < .001). The indirect pathway from dependency to self-perceived clinical decision-making through metacognitive awareness was significant (standardized indirect effect = -0.045, 95% bootstrap confidence interval [-0.074, -0.019]), whereas the direct pathway was not statistically significant.
CONCLUSIONS
Generative artificial intelligence dependency was prospectively associated with students' self-perceived clinical decision-making through metacognitive awareness. Because the study was observational and the outcome was self-reported, the findings indicate an educational risk pathway rather than demonstrated clinical harm. Nursing educators may benefit from pairing artificial intelligence use with metacognitive scaffolding, verification routines, and reflective assessment.
Xin Yang, Yan Deng, Shuren Luo et al.· Nurse Education Today· 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
BACKGROUND
The nursing process is a systematic, patient-centered framework essential for clinical reasoning and decision-making in nursing education. It is commonly operationalized through the ADPIE (Assessment, Diagnosis, Planning, Implementation, and Evaluation) model, which guides nurses in identifying patient needs, developing care plans, implementing interventions, and evaluating outcomes in a structured and iterative manner. Although the nursing process is fundamental to nursing education and practice, some students may experience difficulties in applying ADPIE effectively, particularly the aspects of clinical reasoning and reflective practice.
METHODS
This was a quasi-experimental study conducted to compare the effects of a Generative Artificial Intelligence (GenAI)-assisted nursing process approach with traditional instruction on nursing students' evidence-based critical thinking, learning self-efficacy, and self-reported clinical reasoning. Moreover, the study integrated the intervention into a second-year nursing course (i.e., nursing process via obstetric nursing internship) as part of formal curriculum activities.
RESULTS
Students who participated in the GenAI-assisted nursing process approach demonstrated higher levels of evidence-based critical thinking, improved learning self-efficacy, and enhanced self-reported clinical reasoning ability compared with those in the traditional instruction group. The GenAI system provided adaptive feedback, real-time scaffolding, and iterative case refinement beyond traditional case-based learning.
CONCLUSIONS
Integrating GenAI into the ADPIE framework within nursing education may enhance students' critical thinking, learning self-efficacy, and self-reported clinical reasoning by supporting structured and reflective learning processes, offering a promising pedagogical approach for nursing education.
BACKGROUND
Artificial intelligence (AI) has emerged as a transformative, fundamentally reshaping how we access and apply knowledge. Achievement motivation (AM) remains a critical component of students' academic and personal development. Examining the association between nursing students' dependence on AI and their AM is a pertinent area of research, particularly in light of the growing incorporation of AI within educational settings.
AIM OF THE STUDY
To explore the association between undergraduate nursing students' dependence on artificial intelligence and achievement motivation.
METHODS
A multicenter descriptive correlational study was conducted across five nursing faculties affiliated with Port-Said, Damietta, Alexandria, Mansoura, and Sohag universities, involving 1412 nursing students. Data were collected using a Personal and Academic Characteristics Questionnaire, the Achievement Motivation Scale (AMS), and the Dependence on Artificial Intelligence Scale (DIA). Data were analyzed using descriptive statistics, Pearson correlation, and simple linear regression.
RESULTS
Nursing students demonstrated a moderate level of dependence on AI (M = 19.46, SD = 11.33) and a moderate level of AM (M = 6.32, SD = 2.13). A statistically significant moderate negative association was found between AI dependence and AM (r = -0.389, p < .001). This indicates that increased dependence on AI is associated with lower achievement motivation. Regression analysis indicated that AI dependence accounted for 19.6% of the variance in AM (R2 = 0.196), while the remaining variance is likely influenced by other unmeasured factors.
CONCLUSION
Nursing students exhibited moderate AI dependence and achievement motivation. The findings indicate a significant negative association between AI dependence and achievement motivation, emphasizing the need to consider students' technology usage patterns concerning academic motivation.
IMPLICATIONS FOR PRACTICE
Educational programs should promote informed and balanced use of AI by clarifying its benefits and limitations as a learning support tool. Learning strategies should also be formulated to foster independent thinking, self-regulation, and goal-directed behavior to support students' achievement motivation.
Aya M. Nasr, Amira Mostafa Soliman, Nourhan Essam Hendawi et al.· Nurse Education Today· 0 citations
Introduction: The integration of Evidence-Based Practice (EBP) into nursing education faces challenges in linking theory to clinical application in complex family health contexts. Students struggle with efficiently accessing, appraising, and applying evidence influenced by sociocultural factors. Artificial intelligence (AI) offers transformative potential but requires pedagogical design to foster critical thinking and ethical use beyond technical skills.Method: An action research with mixed methods was conducted with 100 nursing students. The intervention had four phases: participatory family diagnosis, AI-assisted evidence retrieval and validation, community educational workshops design and execution, and multi-level evaluation.Results: A significant shift in AI use from basic to strategic, with a 40% reduction in literature search time. Qualitative data revealed enhanced critical awareness and ethical reasoning, while quantitative results indicated 90% of students improved critical appraisal skills and 70% felt more confident in evidence-based decisions. The project impacted 100 families, with 90% trusting evidence-based recommendations.Conclusions: Integrating AI in experiential pedagogies like Design Thinking and Service-Learning effectively develops nursing competencies, ensuring technology adoption supports context-sensitive family health learning outcomes.
Maria Graciela Villalba-Condori, Carla Cuya-Zevallos· Publicaciones· 0 citations