ARTIFICIAL INTELLIGENCE READINESS, TEACHER DIGITAL COMPETENCE, AND STUDENT LEARNING ENGAGEMENT: A MULTIPLE REGRESSION ANALYSIS

Abstract

The integration of artificial intelligence (AI) into education places new demands on both institutions and teachers, yet little is known about how AI readiness and teacher digital competence jointly shape the extent to which students engage with learning. Drawing on the technology acceptance literature and the digital competence framework for educators, this study examines the effects of AI readiness and teacher digital competence on student learning engagement. A cross-sectional, quantitative survey design was used, and data from 400 respondents were analyzed using descriptive statistics, reliability analysis, Pearson correlation, and multiple regression. All scales demonstrated excellent internal consistency (Cronbach's alpha = .884 to .912; overall scale = .926). The regression model was statistically significant, F(2, 397) = 156.00, p < .001, and explained 44.0% of the variance in student learning engagement. Both AI readiness (β = .287, p < .001) and teacher digital competence (β = .457, p < .001) were significant positive predictors, with teacher digital competence emerging as the stronger predictor. AI readiness was also strongly correlated with teacher digital competence (r = .580), and no multicollinearity concern was detected (VIF = 1.51). All four hypotheses were supported. These findings indicate that technological readiness alone is insufficient: teachers' digital competence is the more powerful correlate of student engagement. Implications for teacher professional development, institutional AI strategy, and future research are discussed.