Danheui Kang, Sunju Park, Journal of Internet Computing and Services, Vol. 26, No. 6, pp. 129-137, Dec. 2025
10.7472/jksii.2025.26.6.129, Full Text:  HTML
Keywords: AI Convergence education, elementary teachers' perceptions, teaching competence, teaching effectiveness, self-development

Abstract

Artificial intelligence (AI) is causing huge paradigm shifts in society, culture, and economics beyond the realm of technology. These changes are also having a profound impact on the education sector, where teachers are learning about AI-infused education and reorganizing and implementing lessons in schools in a new educational paradigm. Researchhas shown that teacher understanding and attitudes are one of the key factors when introducing new teaching methods into the classroom. Having a clear understanding and positive attitude towards AI is an essential condition for the successful adoption of AI-integrated education. In this study, 193 elementary school teachers were surveyed to collect data, and SPSS Statistics 29 was used to analyze the frequency and descriptive statistics of the main variables, and hierarchical regression analysis was used to evaluate the relationship between awareness of AI convergence education, teaching competence, and teaching effectiveness. The results of the study showed that elementary school teachers' perceptions of AI convergence education had a significant positive effect on both teaching competence and teaching effectiveness, even when controlling for background and self-development variables. Furthermore, the more active the teachers' participation in AI convergence education and related self-development, the more their teaching competence and teaching effectiveness tended to improve. This suggests that positive perceptions of AI-integrated education and participation in continuous learning may be key factors in enhancing teaching competence and educational outcomes. This study provides theoretical evidence that elementary school teachers' perceptions of AI convergence education affect their teaching quality and activation. Beyond highlighting the importance of teacher training, the findings also call for more systematic institutional support—such as integrating AI convergence modules into pre-service teacher education, establishing continuous in-service AI learning programs, and creating collaborative school–university networks to share best practices. These strategies can enhance both the policy relevance and practical impact of AI convergence education by ensuring that teachers receive sustainable and structured support for AI integration in classrooms.

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