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Pre-Service Teachers’ Perceptions of AI Technological Support and School-Based Intelligent Environment Support: Model-Based Indirect Associations with Innovative Competence via AI Self-Efficacy and Self-Regulated Learning
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DOI：10.3390/bs16081378.png)
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Abstract
En 中文
As artificial intelligence (AI) becomes deeply integrated into teacher education, pre-service teachers’ innovative competence has become a cornerstone for fostering human–AI collaborative innovation and enabling intelligent educational transformation. This study used empirical data from 3003 pre-service teachers across 12 provinces in China. It employed difference tests, quantile regression, and structural equation modeling to examine the associations of AI technological support and school-based intelligent environment support with pre-service teachers’ innovative competence. Results showed that pre-service teachers’ innovative competence was at a medium-to-high level, with significant heterogeneity across individual, family, and school background variables. Both supports were positively associated with innovative competence. However, the patterns of association differed. The association between AI technological support and innovative competence generally increased across the middle-to-upper quantiles, whereas the association between school-based intelligent environment support and innovative competence was stronger at the lower-to-middle quantiles and subsequently declined. AI self-efficacy and self-regulated learning both showed significant model-based indirect associations linking the two supports with innovative competence. Specifically, under AI technological support, AI self-efficacy had a larger mediating association than self-regulated learning; under intelligent environment support, the opposite pattern emerged. This study systematically examined the associations and mediating paths of AI technological support and intelligent environment support with pre-service teachers’ innovative competence. These findings identify correlational patterns consistent with the proposed framework and provide empirical evidence for optimizing AI-empowered teacher innovation cultivation.
Keywords:
innovative competence
AI technological support
school-based intelligent environment support
AI self-efficacy
self-regulated learning
pre-service teacher
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