Examining the relationships between artificial intelligence literacy, AI-TPACK, job satisfaction, and well-being among teachers through structural equation modeling

This study explores the relationships between teachers' artificial intelligence literacy (AIL), AI-TPACK competencies, job satisfaction, and well-being. Survey data from 350 in-service teachers were analyzed using PLS-SEM to examine how AI-related competencies are associated with professional and psychological outcomes. The findings reveal that AIL strongly predicts AI-specific knowledge, especially AI-TCK, AI-TK, AI-TPCK, and AI-TPK, with a moderate influence on traditional pedagogical knowledge. At the job satisfaction level, AI-TCK and general pedagogical knowledge are positively associated with satisfaction, while AI-TK has a negative relationship. Mediation analyses show that AI competencies influence well-being through job satisfaction. AI-TCK and pedagogical knowledge are positively associated with well-being indirectly through job satisfaction, whereas AI-TK shows a negative indirect association along the same pathway. The study suggests that AI-related competencies are linked to teachers' work-life outcomes, and highlights job satisfaction as the key mediator between AI competence and well-being. Because the design is cross-sectional and self-reported, these relationships are interpreted as associations rather than causal effects. The study advocates for integrating AI in ways that support pedagogical development rather than just technical skills.