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Background <p>The integration of artificial intelligence (AI) in nursing education is crucial for enhancing technological adaptability. However, the relationship between nursing students’ AI self-efficacy, attitudes, and educational level (associate vs. bachelor’s degrees) remains underexplored. Previous studies predominantly rely on linear models, which inadequately capture the complex dynamics of these factors.</p> Methods <p>This multicenter, cross-sectional study was conducted from February to March 2025 across 93 medical institutions in 13 provinces in China. A total of 1,113 nursing students (748 with associate degrees and 365 with bachelor’s degrees) participated. Data were collected using the Artificial Intelligence Self-Efficacy Scale (AISES) and the General Attitudes toward AI Scale (GAAIS). A network model was constructed through LASSO-regularized partial correlation analysis to assess node strength, bridge strength, and predictability. Differences between the two educational groups were analyzed using the Network Comparison Test.</p> Results <p>The 24-node network revealed 156 valid connections (56.5%). AI_4 (AI tone matches humans) emerged as the central hub (strength = 2.145), while AI_1 (AI interaction vivid) demonstrated the strongest bridging (bridge strength = 3.255). The Network Comparison Test indicated no significant difference in global network structure (M = 0.255, <i>P</i> = 0.476) or global strength (S = 0.407, <i>P</i> = 0.287) between educational groups. However, significant local differences were found: the bachelor’s degree group showed stronger connections related to technical transparency (E = 0.255), suggesting a more integrated understanding of AI systems. Conversely, the associate degree group exhibited a significant negative association between programming confidence and risk anxiety (E = 0.087), indicating a potential competence-anxiety tension that warrants targeted support.</p> Conclusions <p>The AI self-efficacy and attitudes of nursing students form a “cognitive-affective-skill” network, with anthropomorphic interactions serving as a key factor. While the cross-sectional design precludes causal inference, these findings suggest the need for stratified educational strategies: enhancing safety simulations for associate degree students and integrating technical principles for bachelor’s degree students. Such targeted interventions could foster technological empowerment and promote educational equity.</p>

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Artificial intelligence self-efficacy and attitudes among nursing students: a multicenter network analysis of educational stratification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月26日

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  • Qin Zeng,
  • Jun Zhu,
  • Yuji Wang,
  • Shaoyu Su,
  • Yan Huang

摘要

Background

The integration of artificial intelligence (AI) in nursing education is crucial for enhancing technological adaptability. However, the relationship between nursing students’ AI self-efficacy, attitudes, and educational level (associate vs. bachelor’s degrees) remains underexplored. Previous studies predominantly rely on linear models, which inadequately capture the complex dynamics of these factors.

Methods

This multicenter, cross-sectional study was conducted from February to March 2025 across 93 medical institutions in 13 provinces in China. A total of 1,113 nursing students (748 with associate degrees and 365 with bachelor’s degrees) participated. Data were collected using the Artificial Intelligence Self-Efficacy Scale (AISES) and the General Attitudes toward AI Scale (GAAIS). A network model was constructed through LASSO-regularized partial correlation analysis to assess node strength, bridge strength, and predictability. Differences between the two educational groups were analyzed using the Network Comparison Test.

Results

The 24-node network revealed 156 valid connections (56.5%). AI_4 (AI tone matches humans) emerged as the central hub (strength = 2.145), while AI_1 (AI interaction vivid) demonstrated the strongest bridging (bridge strength = 3.255). The Network Comparison Test indicated no significant difference in global network structure (M = 0.255, P = 0.476) or global strength (S = 0.407, P = 0.287) between educational groups. However, significant local differences were found: the bachelor’s degree group showed stronger connections related to technical transparency (E = 0.255), suggesting a more integrated understanding of AI systems. Conversely, the associate degree group exhibited a significant negative association between programming confidence and risk anxiety (E = 0.087), indicating a potential competence-anxiety tension that warrants targeted support.

Conclusions

The AI self-efficacy and attitudes of nursing students form a “cognitive-affective-skill” network, with anthropomorphic interactions serving as a key factor. While the cross-sectional design precludes causal inference, these findings suggest the need for stratified educational strategies: enhancing safety simulations for associate degree students and integrating technical principles for bachelor’s degree students. Such targeted interventions could foster technological empowerment and promote educational equity.