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Dual Mediation Mechanisms of Teachers’ AI Literacy on Their Teaching Innovation Behaviors: An Empirical Study Based on Chinese K-12 Education
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DOI：10.1007/s40299-026-01138-6.png)
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Abstract
En 中文
With Generative Artificial Intelligence (GAI) deeply integrated into education, teachers serve as the core agents bridging technology and pedagogy. Teachers’ AI literacy (TAIL) is pivotal in facilitating teaching innovation, cultivating innovative talent, and driving educational transformation. Yet, existing research remains inconclusive regarding which technology-related psychological factors underlie the systematic association between TAIL and Teachers’ Teaching Innovation Behaviors (TTIB). Based on the 3P model, this study constructed a multiple mediation model and employed structural equation modeling (SEM) to analyze questionnaire data from 528 primary and secondary school teachers in China. The results revealed that TAIL was significantly and positively associated with TTIB, GAI-related Technology Self-Efficacy (GAI-TSE), GAI-induced Technology Stress (GAI-TS), and GAI-driven Cognitive Reconstruction (GAI-CR). Additionally, the relationship between TAIL and TTIB was mediated not only by GAI-TSE and GAI-CR, but also by two serial mediation pathways: GAI-TSE→GAI-CR and GAI-TS→GAI-CR. These findings clarify the multifaceted psychological pathways linking TAIL and TTIB, highlighting the prominent role of GAI-TSE within the model. This study not only deepens our understanding of the hierarchical processing logic intrinsic to the 3P model, but also provides robust empirical evidence for the design and delivery of targeted GAI training programs and teacher support systems. Reveals dual pathways of teachers' AI literacy affecting teaching innovation (China). Breaks 3P model's parallelism flaw; highlights Emotional-Cognitive pathway effect. Deepens differentiated cognition of psych mediation roles. Proposes 3D strategies for synergizing teachers' AI literacy and teaching innovation.
Keywords:
Teachers' AI literacy
Teachers' teaching innovation behaviors
GAI-related technology self-efficacy
GAI-induced technology stress
GAI-driven cognitive reconstruction
Structural equation modeling
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