Learning Adaptation in Artificial Intelligence‐Enabled Nursing Education: A Network Analysis of Artificial Intelligence Literacy, Self‐Regulated Learning and Academic Resilience

AIMS: To examine the interconnections among artificial intelligence literacy, self-regulated learning strategies, and academic resilience in undergraduate nursing students, and to conduct an exploratory comparison of network characteristics between students with and without clinical practice experience. DESIGN: A cross-sectional survey. METHODS: Using convenience sampling, 1784 undergraduate nursing students were recruited from five universities in Hunan Province, China, between March and May 2026. Participants completed a demographic questionnaire and three validated scales. Network analysis estimated node strength, bridge strength, and predictability. Bootstrap procedures assessed network accuracy and stability, and the Network Comparison Test was used to conduct an exploratory comparison between students with and without clinical practice experience. RESULTS: The 10-node network contained 35 non-zero edges and had a mean predictability of 62.6%. Artificial intelligence evaluation showed the highest node strength, followed jointly by monitoring learning strategies and cognitive maturity. Evaluating and revising learning strategies showed the highest bridge strength, followed by cognitive maturity. In the exploratory subgroup analysis, the overall network structure differed between students with and without clinical practice experience, whereas global strength did not. CONCLUSIONS: Artificial intelligence evaluation, monitoring learning strategies and cognitive maturity showed relatively high node strength, whereas evaluating and revising learning strategies and cognitive maturity showed relatively high cross-domain bridge strength. IMPLICATIONS FOR THE PROFESSION: Findings may inform nursing curricula integrating artificial intelligence literacy, learning regulation, and academic adaptation. IMPACT: This study addressed fragmented understanding of these capacities. It identified dimensions with relatively high node strength and bridge strength, providing exploratory evidence for future nursing education research. REPORTING METHOD: This study was reported in accordance with the STROBE statement. NO PATIENT OR PUBLIC CONTRIBUTION: This study did not involve patients, service users, caregivers, or members of the public.

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Journal
Journal of Advanced Nursing
Published
2026-10-15
DOI
https://doi.org/10.1111/jan.70784
Primary Topic
Healthcare Education and Workforce Issues
Type
article
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article

Learning Adaptation in Artificial Intelligence‐Enabled Nursing Education: A Network Analysis of Artificial Intelligence Literacy, Self‐Regulated Learning and Academic Resilience

Xiao Zemei, Dan Chen, Xiahui Xiao, Danyan Li et al.
Journal of Advanced Nursing
Healthcare Education and Workforce Issues
article

Learning Adaptation in Artificial Intelligence‐Enabled Nursing Education: A Network Analysis of Artificial Intelligence Literacy, Self‐Regulated Learning and Academic Resilience

Xiao Zemei, Dan Chen, Xiahui Xiao, Danyan Li, Xueqing Gong
article en

Abstract

AIMS: To examine the interconnections among artificial intelligence literacy, self-regulated learning strategies, and academic resilience in undergraduate nursing students, and to conduct an exploratory comparison of network characteristics between students with and without clinical practice experience. DESIGN: A cross-sectional survey. METHODS: Using convenience sampling, 1784 undergraduate nursing students were recruited from five universities in Hunan Province, China, between March and May 2026. Participants completed a demographic questionnaire and three validated scales. Network analysis estimated node strength, bridge strength, and predictability. Bootstrap procedures assessed network accuracy and stability, and the Network Comparison Test was used to conduct an exploratory comparison between students with and without clinical practice experience. RESULTS: The 10-node network contained 35 non-zero edges and had a mean predictability of 62.6%. Artificial intelligence evaluation showed the highest node strength, followed jointly by monitoring learning strategies and cognitive maturity. Evaluating and revising learning strategies showed the highest bridge strength, followed by cognitive maturity. In the exploratory subgroup analysis, the overall network structure differed between students with and without clinical practice experience, whereas global strength did not. CONCLUSIONS: Artificial intelligence evaluation, monitoring learning strategies and cognitive maturity showed relatively high node strength, whereas evaluating and revising learning strategies and cognitive maturity showed relatively high cross-domain bridge strength. IMPLICATIONS FOR THE PROFESSION: Findings may inform nursing curricula integrating artificial intelligence literacy, learning regulation, and academic adaptation. IMPACT: This study addressed fragmented understanding of these capacities. It identified dimensions with relatively high node strength and bridge strength, providing exploratory evidence for future nursing education research. REPORTING METHOD: This study was reported in accordance with the STROBE statement. NO PATIENT OR PUBLIC CONTRIBUTION: This study did not involve patients, service users, caregivers, or members of the public.

Journal of Advanced Nursing
Changsha Medical University (CN), Hunan Normal University (CN)
Openalex Percentile: Top 8%
Healthcare Education and Workforce Issues
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