Generative AI dependency and self-perceived clinical decision-making in nursing students: A three-wave longitudinal study of metacognitive awareness as a mediator.
Aug 2026· Nurse Education Today· Vol 168, pp.
107347
· 0 citations· 41 references
Medicine
TL;DR
Generative artificial intelligence dependency was prospectively associated with students' self-perceived clinical decision-making through metacognitive awareness, and nursing educators may benefit from pairing artificial intelligence use with metacognitive scaffolding, verification routines, and reflective assessment.
Abstract
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.
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
The findings reveal 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 and moderated mediation analysis indicated that cognitive load partially mediated the relationship between AI use and critical thinking.
Arooj Arshad, Ayoob Lone, Loveena Arickswamy et al.· Frontiers in Psychology· 0 citations
Initial evidence is provided that the NAIRS is a valid and reliable instrument for assessing nursing students' readiness for artificial intelligence across knowledge/awareness, willingness to use AI, self-efficacy, and ethical awareness domains and may be useful for educational needs assessment and curriculum planning in nursing education.
Sumeyye Akçoban, Gülay Koca, S. Berşe· BMC Nursing· 0 citations
Nursing curricula may benefit from structured AI education that integrates guided GenAI practice, case-based learning, and faculty feedback that integrates guided GenAI practice, case-based learning, and faculty feedback.
Shinhi Han, H. Kang, P. Gimber et al.· Nursing Reports· 0 citations
While critical thinking disposition, career awareness, and innovative skills are associated with clinical decision-making scores, these cross-sectional findings suggest that educational programs may consider exploring simulation-based training and cognitive strategies to support student decision-making in complex clinical scenarios.
Pınar Arpacı, Melda Başer Seçer· BMC Medical Education· 0 citations
Historically grounded narratives are increasingly incorporated into values-oriented higher education, yet limited empirical research has examined how university students’ appraisals of public health development history are associated with their affective responses and stated learning demands. Guided by the Cognition–Affect–Conation (CAC) framework as a conceptual organizing framework, this study examined the cross-sectional associations among Educational Cognition, Educational Emotion, and Public Health Learning Aspirations (PHLA) within Ideological and Political Education (IPE) in Chinese universities. PHLA was used as an umbrella term for two related forms of stated educational demand: Demand for Educational Resources (DER) and Demand for Educational Content (DEC).
A cross-sectional online survey was administered in September 2025 to 1,142 university students recruited through stratified cluster sampling; 92.2% attended institutions in Eastern China. From a self-developed 33-item questionnaire, 24 substantive Likert-type items measuring Educational Cognition, Educational Emotion, DER, and DEC on five-point scales were analyzed. Descriptive statistics, Spearman rank-order correlations, and bootstrap cross-sectional indirect-association models were estimated using PROCESS Macro Model 4 for SPSS with 5,000 resamples.
All four study variables had means above 4.0, with Educational Emotion showing the highest mean (
M
= 4.528, SD = 0.589). The variables were positively intercorrelated (
ρ
= 0.649–0.826, all
p
< 0.01), supporting H1. In the specified cross-sectional regression models, Educational Cognition was positively associated with Educational Emotion (
β
= 0.65), DER (
β
= 0.56), and DEC (
β
= 0.45; all
p
< 0.001), supporting H2. The indirect association involving Educational Emotion was statistically different from zero for DER (estimate = 0.20, 95% bootstrap CI [0.15, 0.23]; 26% of the total association) and DEC (estimate = 0.28, 95% bootstrap CI [0.23, 0.33]; 39% of the total association). These estimates came from separate outcome models and were not directly compared.
The findings describe the co-occurrence of educational appraisal, affective-motivational response, and stated learning demand, a pattern compatible with the CAC framework but not evidence of temporal mediation or causality. By distinguishing substantive content demand from resource-related demand, the study provides a preliminary basis for examining how public health development history is appraised within value-laden education. The findings may inform the development of historical content and supporting resources in IPE and medical humanities curricula. Interpretation remains limited by self-report measurement, possible socially desirable responding, operational overlap among the study-defined constructs, and the concentration of the sample in Eastern China.
Shi-Yun Zhang· Frontiers in Public Health· 0 citations
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