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Dynamic pathways in the development of university students' AI literacy: integrating quantitative and qualitative
ZhiHui ZhuGe1,2, Chengshi Li2
1School of Marxism, Taishan University, Tai'an, China.
Introduction:
This study investigates the formative mechanisms underlying university students' artificial intelligence (AI) literacy, focusing on the complex interrelationships among external support, AI self-efficacy, AI anxiety, and AI literacy.
Methods:
An integrated framework was developed and tested using a mixed-methods approach, combining structural equation modeling (SEM) with qualitative interviews of 15 high-anxiety, high-literacy (HAHL) students.
Results:
SEM results indicated that external support positively predicted AI literacy (β = 0.163, p < 0.001) and AI self-efficacy (β = 0.454, p < 0.001), while also increasing AI anxiety (β = 0.505, p < 0.001). Both AI self-efficacy (β = 0.728, p < 0.001) and AI anxiety (β = 0.527, p < 0.001) significantly and positively influenced AI literacy, highlighting that moderate anxiety may serve as vigilance-driven motivation to foster learning. Qualitative analyses further revealed that HAHL students translate anxiety into structured engagement through cognitive appraisal, resource accumulation, and professional identity reflection.
Conclusion:
These results demonstrate the dynamic interplay of environmental, cognitive, and affective factors in shaping AI literacy, and show how qualitative insights complement quantitative SEM analysis by revealing mechanisms behind anxiety-driven engagement. The study provides evidence-based guidance for higher education institutions to enhance AI literacy through optimized support systems, fostering technological confidence, and strategically managing AI-related anxiety.
