A Structural Model of Factors Affecting Primary School Teachers’ AI Competence in Northern Mountainous Vietnam
Keywords:
Artificial intelligence (AI); primary school teachers; digital pedagogical competence; technology integration in teaching; generalized structured component analysisAbstract
In the context of artificial intelligence (AI) increasingly shaping education, this study develops and validates a structural model of factors that influence primary school teachers’ capacity to apply AI in teaching. Data were collected from 624 teachers in the mountainous northern region of Vietnam. The study employed generalized structured component analysis, a variance-based variant of structural equation modeling, to evaluate the proposed model. The model included six key components: knowledge, skills, ethical awareness, reflective thinking, attitude, and application behavior. The results show that knowledge serves as a foundational factor, directly influencing both skills and attitude. Skills and reflective thinking act as mediators that foster pedagogical competence, which subsequently leads to application behavior. A positive attitude emerged as the strongest predictor of AI usage in teaching. In contrast, ethical awareness contributes to overall competence but does not directly affect application behavior. These findings provide important implications for teacher training in the AI era. In particular, the findings highlight the need to strengthen foundational knowledge, integrate skill development into practice, promote reflective environments, and embed ethical considerations into technology education.
https://doi.org/10.26803/ijlter.24.10.27
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