FACTORS ASSOCIATED WITH PRIMARY SCHOOL TEACHERS’ SELF-REPORTED COMPETENCE FOR ARTIFICIAL INTELLIGENCE INTEGRATION IN TEACHING IN HO CHI MINH CITY: AN EXPLANATORY SEQUENTIAL MIXED-METHODS STUDY

Abstract

This study examines factors associated with primary school teachers’ self-reported competence for integrating artificial intelligence (AI) into teaching in Ho Chi Minh City, Vietnam. An explanatory sequential mixed-methods design was employed to identify key statistical relationships and to further interpret teachers’ reported experiences, supports, and constraints in practice. In the quantitative phase, survey data were collected from 450 primary school teachers across 18 schools and analyzed using partial least squares structural equation modeling (PLS-SEM). In the qualitative phase, 15 teachers with contrasting profiles were purposively selected for semi-structured interviews, and the interview data were analyzed thematically. The quantitative findings showed that AI-TPACK had the strongest positive association with self-reported competence for AI integration, followed by attitudes toward AI, school support, and pedagogical beliefs, whereas technical and resource barriers were negatively associated with the outcome. The qualitative findings further indicated that teachers’ AI-related practice was shaped by time pressure, infrastructural limitations, the need for hands-on and pedagogy-linked professional development, and concerns about student overreliance on AI. Taken together, the results suggest that strengthening AI integration in primary education requires coordinated attention to teacher capacity, school-level support, and practical working conditions. The study contributes context-specific evidence from an urban Vietnamese setting and offers implications for teacher professional development, school leadership, and context-sensitive policy planning.

Keywords: artificial intelligence; primary school teachers; AI integration; AI-TPACK; explanatory sequential mixed methods; PLS-SEM; Ho Chi Minh City