AI Integration in Higher Education: A Software Engineering Perspective on Readiness, Anxiety and Innovations

This paper presents an empirical model that examines how teacher support for the adoption of artificial intelligence (AI) affects critical psychological and contextual factors in Chinese higher education. Using survey data and partial least squares structural equation modeling (PLS-SEM), this study examines the relationships between teacher support, digital literacy (DL), AI-related anxiety, cultural orientation and infrastructural readiness, as well as their combined effects on sustainable educational innovation. The results indicate that teacher support enhances students’ DL but simultaneously increases AI anxiety (AIA). AIA exerts a significant influence on cultural attitudes toward technology (CAT), which, together with DL, strongly predicts contributions to sustainable innovation. Moderation analysis reveals that technological infrastructure strengthens the effect of digital leadership, while higher language proficiency (LP) mitigates the negative consequences of AIA. The proposed model demonstrates strong explanatory power ([Formula: see text] up to 0.46) and predictive accuracy ([Formula: see text]). This study contributes to the literature on AI and knowledge engineering in education by providing a validated framework that integrates cognitive, cultural and infrastructural variables. The findings offer actionable insights to universities and policymakers seeking to develop robust, context-sensitive strategies for AI integration that address both technical readiness and user confidence.