The Associations of Emotional Intelligence, AI Self-Efficacy, and AI Literacy Among Nursing Undergraduates Under the NUR.S.E.S. Framework: A Network Analysis
ABSTRACT
Background:
With the rapid development of generative artificial intelligence and its deep integration into nursing education, nursing students' AI literacy has become a critical competency for their professional development. However, the mechanisms underlying the interactions among emotional intelligence, AI self-efficacy, and AI literacy in shaping comprehensive AI literacy remain unclear.
Objective:
Based on the NUR.S.E.S. framework and using network analysis methods, this study systematically explored the complex relational network among emotional intelligence, AI self-efficacy, and AI literacy among undergraduate nursing students. It identified core determinants and key bridging nodes within this network, providing evidence for targeted educational interventions.
Methods:
A cross-sectional survey design was utilized, with 982 undergraduate nursing students from a university conveniently sampled in September 2025 as research subjects. Assessments were conducted using the Emotional Intelligence Scale, the AI Self-Efficacy Scale, and the AI Literacy Scale. Using R4.5.1, we constructed a Gaussian graph model, calculated centrality metrics such as node and bridge strength, and assessed network stability using the Bootstrap method.
Results:
Network analysis showed that emotion regulation (strength centrality = 1.355) and evaluative ability (strength centrality = 1.323) were the most influential core driving nodes in the network. Emotional perception (bridge strength = 0.427) and comfort with AI (bridge strength = 0.242) are the most critical bridge nodes, effectively connecting emotional intelligence with AI technology systems. Simultaneously, the network architecture reveals the key mediating role of AI self-efficacy, effectively linking emotional intelligence (particularly emotional perception as a bridging factor) with higher levels of AI literacy.
Conclusions:
Cultivating AI literacy among undergraduate nursing students is a system that deeply integrates emotional, cognitive, and technical confidence. Emotional intelligence serves as the basis, while AI self-efficacy acts as the key mediator. Educational interventions should focus on enhancing emotional awareness and application skills while increasing comfort in interacting with AI. Through integrated systems combining emotional education and technical training, they effectively cultivate students' comprehensive AI literacy. Clinical Trial: Ethics Committee of Xjing University
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