Artificial intelligence integration in instructional leadership: Impact on teacher innovation and job satisfaction

Abstract

This study explores the impact of AI-integrated instructional leadership on teacher innovation and job satisfaction in underdeveloped regions of China. Based on the Job Demands-Resources (JD-R) model, AI-integrated leadership is conceptualized as a job resource that offers timely feedback, reduces workload, and supports innovation. Data were collected from 366 junior high school teachers and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that AI-integrated leadership significantly enhances both teacher innovation and job satisfaction, with teacher innovation partially mediating this relationship. These findings extend the JD-R framework by positioning AI-supported leadership as a technology-mediated resource that promotes professional growth and psychological well-being. The study provides practical insights for school leaders and policymakers in resource-constrained settings, suggesting that the strategic application of AI can alleviate teacher burden, foster innovative practices, and improve overall job satisfaction.