TLDR: A recent study reveals that human-like features such as anthropomorphism and likability significantly impact Chinese university students’ sustained use of Generative AI tools. Perceived intelligence and AI compatibility also play crucial roles, mediated by utilitarian and hedonic values. The research highlights that fear of AI can negatively moderate these effects, underscoring the importance of thoughtful AI design and addressing user concerns for long-term engagement.
A comprehensive study conducted among Chinese university students sheds light on the critical factors influencing their sustained adoption of Generative AI (GenAI) tools. The research, grounded in the Heuristic-Systematic Model and Means-End-Chain Theory, surveyed 823 students initially and 712 in a follow-up, revealing that human-like attributes and perceived utility are key drivers.
The study identified several heuristic and systematic factors that positively impact students’ intention to continue using GenAI. Among the heuristic factors, anthropomorphism (the attribution of human characteristics or behavior to AI) and likability emerged as significant influences. This suggests that when GenAI tools exhibit more human-like traits or are perceived as more agreeable, students are more inclined to integrate them into their routines. Systematic factors, including perceived intelligence and AI compatibility, also demonstrated a positive correlation with sustained usage intentions.
Furthermore, the research highlighted the mediating roles of utilitarian and hedonic values. Utilitarian values, which relate to the perceived usefulness and practical benefits of GenAI, and hedonic values, concerning the enjoyment and pleasure derived from using the tools, both serve as crucial links between the AI’s attributes and students’ behavioral intentions. This dual role of value perceptions underscores that both practical benefits and user experience are vital for driving continued engagement.
An important finding from the study is the negative moderating effect of the ‘fear of AI’. The research indicates that students’ apprehension or fear regarding AI can weaken the direct positive effects of anthropomorphism, likability, perceived intelligence, and AI compatibility on their intention to use GenAI. This emphasizes the necessity for developers and educators to address user concerns and build trust to foster long-term adoption.
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The findings offer valuable theoretical and practical insights into human-AI interaction. They stress the importance of designing GenAI attributes thoughtfully to enhance both its utility and user experience. By understanding and mitigating factors like the fear of AI, educational institutions and AI developers can promote greater engagement and more effective integration of these powerful tools in learning environments. The study contributes significantly to the literature by combining heuristic and systematic cues with user value perceptions, clarifying the mechanisms that drive the adoption and continued use of Generative AI.
