Beyond Attitude: Beyond Attitude: Unveiling the Structural Determinants of Teacher Performance in the AI Era (A PLS-SEM Approach)

Abstract

Background: The rapid spread of AI tools in education has moved the conversation from simply adopting technology to examining how it actually transforms what teachers do in the classroom. Yet most studies have tackled this question by looking at one variable at a time, leaving the structural pathways that connect readiness to real instructional outcomes largely unexplored. Aims: This study aims to investigate the structural relationships between teachers' technological readiness, specifically digital literacy, self-efficacy, and attitudes toward AI, and their actual instructional performance, while exploring the mediating role of AI competence. Methods: Survey responses from 220 teachers drawn from across the Indonesian archipelago were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4, proceeding through a two-stage measurement and structural assessment. Results: Teacher Digital Literacy (TDL), Attitude Towards Using AI Tools (ATT), and Self-Efficacy with Technology (SET) each proved to be significant predictors of TAC (p < 0.001). Of all the variables tested, TAC turned out to be the strongest direct predictor of TP (β = 0.540). A more striking finding was that ATT carried no meaningful direct influence on TP (β = 0.009, p = 0.870); instead, its entire effect flowed through TAC, a pattern that satisfies the conditions for full mediation. Multi-Group Analysis further confirmed that these relationships held regardless of how long a teacher had been in the classroom.Conclusion: What these results collectively suggest is that enthusiasm alone cannot sustain instructional quality in the AI era; it is technical mastery in AI Competence that converts a teacher’s positive outlook into measurable performance gains.