Graduate Education in China Meets AI: Key Factors for Adopting AI-Generated Content Tools

Abstract

Factors influencing Chinese graduate students' adoption of AI-generated content (AIGC) tools are examined through partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The developed AIGCT-SI model incorporates key elements such as information accuracy, trust, and privacy concerns. PLS-SEM results indicate that performance expectancy, effort expectancy, facilitating conditions, and habit significantly impact students' intentions, with trust acting as a key mediator, particularly for privacy concerns and social influence. FsQCA reveals seven configurations, demonstrating how combinations of performance expectancy, effort expectancy, and facilitating conditions drive adoption. A bidirectional relationship between privacy concerns and trust is observed, with trust mitigating privacy risks in several configurations. This integrative approach highlights the complex dynamics of AIGC tool adoption and provides strategic insights for their effective use in Chinese graduate education. As the findings are based on the Chinese context, further exploration in other educational settings is encouraged to validate their broader applicability.