AI Literacy and Convergence Competencies Among Pre-Service Teachers in Non-STEM Disciplines

Abstract

Artificial intelligence (AI) is transforming education, requiring teachers to develop competencies that support AI convergence education across subject areas. Although AI literacy is increasingly emphasized in teacher education, empirical research on AI convergence education competencies among pre-service teachers, particularly in non-STEM disciplines, remains limited. This study examined levels of AI literacy and AI convergence education competencies among pre-service teachers and analyzed differences by academic major, gender, level of AI understanding, and prior AI-related educational experience. Survey data were collected from 112 pre-service teachers in humanities, social sciences, arts, and physical education at a teacher education institution in South Korea. Two validated instruments were used to measure AI literacy and AI convergence education competencies. Data were analyzed using descriptive statistics, one-sample t-tests (test value = 3.00), independent samples t-tests, and one-way ANOVA, with the false discovery rate controlled using the Benjamini-Hochberg procedure. Most subdomains of AI literacy and AI convergence education competencies scored significantly above the scale midpoint. Data literacy and basic AI knowledge did not differ significantly from it, and programming-related competencies were significantly lower. Significant differences emerged across majors, gender, and levels of AI understanding, indicating uneven preparedness. In contrast, within AI convergence education competencies, ethics and openness did not differ significantly by prior AI-related educational experience. These findings suggest the need for systematic, discipline-sensitive teacher education curricula that strengthen both foundational AI literacy and subject-specific convergence competencies in teacher preparation.