University Students' Continued Use of Survey Data for Generative AI.xlsx

The growing adoption of generative AI (GenAI) in education underscores the importance of identifying the factors that shape university students' intentions to sustain their use of this technology. Despite its growing adoption, limited research has investigated how the attributes of GenAI shape students' sustained usage intentions. Grounded in the Heuristic-Systematic Model and Means-End-Chain Theory, this study conducted an online survey of university students in mainland China through random sampling (time1, n = 823; time2, n = 712), explores how heuristic factors (anthropomorphism and likability) and systematic factors (perceived intelligence and AI compatibility) influence students' intention to continue using GenAI, with utilitarian and hedonic values serving as mediators. The findings indicate that anthropomorphism, likability, perceived intelligence, and AI compatibility have a positive impact on students' intention to continue using GenAI. Moreover, utilitarian and hedonic values serve as mediators, linking these attributes to behavioral intentions, highlighting the dual role of value perceptions in driving continued use. The study highlights the fear of AI as a negative moderating factor that weakens the direct effects of these attributes on intention, emphasizing the critical role of individual characteristics in shaping human-AI interactions. This study enhances the literature on human-AI interaction by combining heuristic and systematic cues with user value perceptions to clarify the mechanisms driving the adoption and continued use of GenAI. The findings offer theoretical and practical insights, emphasizing the need for thoughtful design of GenAI attributes to enhance its utility and user experience while addressing concerns related to fear of AI to promote long-term engagement.