<![CDATA[Advances in artificial intelligence (AI) technology have created new opportunities for educational transformation, particularly in enhancing student motivation and reducing boredom in high school history learning. This study aims to examine the structural relationships among Teachers’ AI Competence (TAC), Multidimensional Work Motivation (MWMS), Students’ Learning Agility (SLA), Student Engagement (SE), and Boredom in Learning (BIL) within AI-supported history education. Using a quantitative approach, data were collected from 101 public high school students in Jambi Province and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that teachers’ AI competence positively and significantly influenced students’ learning agility and engagement. In addition, student engagement was found to significantly reduce boredom in learning, indicating that students who were more actively involved in classroom activities experienced lower levels of boredom. The proposed model demonstrated substantial explanatory power in predicting student engagement and boredom outcomes. These findings highlight the importance of strengthening teachers’ AI-related competencies to foster more adaptive, engaging, and meaningful learning experiences. This study contributes to the growing body of literature on AI integration in secondary education by providing empirical evidence from history learning contexts and offering practical implications for the design of innovative and student-centered instructional practices.]]>
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