Towards sustainable AI adoption in higher education: understanding students’ acceptance through benefits, risks, and social influence

The rapid integration of artificial intelligence (AI) into higher education is reshaping conventional teaching practices by enabling adaptive instruction, personalized learning support, and data-informed pedagogical decision-making. In China, where digital transformation has become central to educational modernization, universities are increasingly adopting AI-enabled tools to enhance teaching and learning. Despite these opportunities, concerns remain regarding students’ behavioral and psychological responses to AI, including data privacy, over-reliance on automated systems, and the potential weakening of independent learning capacity. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study examines how students’ perceived benefits, weaknesses, and risks interact with performance expectancy, effort expectancy, social influence, and facilitating conditions to shape their acceptance of AI-supported sustainable teaching models. Based on 458 valid survey responses analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings indicate that perceived benefits positively influence all UTAUT constructs and overall acceptance. Social influence emerges as the strongest predictor, suggesting that peer endorsement and institutional support play a crucial role not only in facilitating students’ initial acceptance of AI but also in promoting continued engagement with AI-enabled teaching models. By contrast, perceived risks is negatively associated with effort expectancy and facilitating conditions, reflecting students’ concerns regarding data security and system reliability. Perceived weaknesses, however, do not significantly reduce acceptance, suggesting that students tend to prioritize the practical value of AI over its potential limitations. These findings highlight that sustainable AI adoption in higher education depends on enhancing students’ perceived value of AI, cultivating supportive social environments, and addressing risk-related concerns. Such mechanisms can facilitate continuous AI engagement and provide a foundation for the long-term integration of AI into higher education systems. This study extends the UTAUT framework and offers practical implications for developing ethical, adaptive, and sustainable AI-supported teaching systems.