An investigation of the variables predicting pre-service teachers' education 5.0 digital learning technologies self-efficacy

This study aimed to examine pre-service teachers' self-efficacy regarding Education 5.0 digital learning technologies and identify the variables predicting self-efficacy group membership. Employing a quantitative correlational survey design, the study involved 373 pre-service teachers enrolled at a public university. Data were collected using a Personal Information Form and the Education 5.0 Digital Learning Technologies Self-Efficacy Scale and analyzed using K-means cluster analysis, the chi-square test of independence, and binary logistic regression analysis. The cluster analysis classified participants into low (n = 200, M = 78.44) and high (n = 173, M = 100.46) self-efficacy groups. Gender, year of study, and generative artificial intelligence (AI) use were identified as significant predictors of self-efficacy group membership. Male pre-service teachers, fourth-year students, and generative AI users were more likely to belong to the high-self-efficacy group. In contrast, the duration of internet and technological device use did not significantly predict group membership. Based on these findings, it is recommended that teacher education programs incorporate practice-based digital learning activities and AI literacy training.