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The relationship between AI self-efficacy and critical thinking among nursing students: A network analysis
Yawen Chen1, Shuang Wu1, Maoqing Zhang1
1School of Nursing, University of South China, No. 28, Changsheng West Road, Hengyang, Hunan 421001, China.
Nurse Education in Practice
|June 3, 2026
Summary
Network analysis revealed that anthropomorphic interaction is central to artificial intelligence self-efficacy (AISE) in nursing students. Perceived AI controllability is key for linking AISE to critical thinking (CT), highlighting reflective engagement.
Area of Science:
- Nursing Education
- Artificial Intelligence
- Cognitive Science
Background:
- Artificial intelligence (AI) integration in nursing education necessitates understanding individual differences in AI self-efficacy (AISE).
- Concerns exist regarding AI's impact on cognitive engagement and critical thinking (CT) due to efficiency-focused use.
Purpose of the Study:
- To characterize the internal structure of AISE in nursing students using network analysis.
- To examine the relationship between specific AISE components and critical thinking (CT).
Main Methods:
- Descriptive cross-sectional study involving 611 Chinese nursing students.
- Network analysis using LASSO-regularized partial correlation networks in R.
- Estimation of node centrality, bridge strength, and predictability via bootstrapping.
Main Results:
- The AISE network highlighted "AI tone matches humans" as central and "AI interaction process is vivid" as the strongest bridge.
- The integrated AISE-CT network identified "AI easy to control" as the primary bridge.
- Truth-Seeking demonstrated the highest predictability among CT dimensions (R² = 0.850).
Conclusions:
- Anthropomorphic interaction is a core AISE component, and perceived controllability is crucial for linking AISE to CT.
- AISE components have varied associations with CT, emphasizing critical appraisal and reflective engagement over mere efficiency.
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