The rapid development of Artificial Intelligence-Generated Content (AIGC) technology has positioned AIGC-driven personalized learning as a critical pathway for advancing educational sustainability, particularly in addressing issues of inclusion, equity, and quality. However, the potential of this technology is entirely contingent on its ultimate acceptance and use by learners. This study employs a mixed-methods approach to explain the behavioral intentions of students in Chinese higher education to accept AIGC for personalized learning. The quantitative phase involved testing a model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with survey data from 928 university students, analyzed via Structural Equation Modeling (SEM). The qualitative phase consisted of in-depth, semi-structured interviews with 18 students to provide context and depth to the quantitative findings. The SEM results indicate that the UTAUT model effectively explains usage intentions, with Performance Expectancy emerging as the strongest predictor, followed by Facilitating Conditions, Social Influence, and Effort Expectancy. The qualitative findings corroborate these results and illuminate the nuanced experiences behind them, revealing a complex interplay between perceived benefits and practical challenges. This study’s novelty lies in its use of triangulated data to link technology acceptance to students’ perceptions of educational sustainability, finding that usage intention positively influences their evaluation of these outcomes. The findings not only validate UTAUT in the AIGC context but also provide profound, evidence-based implications for educators, policymakers, and developers, highlighting the necessity of balancing technological efficacy with the lived experiences and ethical concerns of students. More profoundly, the study reveals a fundamental tension between the instrumental view of UTAUT and the complex socio-technical realities of AIGC use, proposing a more critical framework for future technology acceptance research.
Xiong et al. (2026) studied this question.