Will human-like features effect the adoption of generative AI tools? A study in Chinese University students

Abstract

Introduction Adopting an information behavior perspective, this study reveals the mechanisms influencing college students' behavioral intention to use Generative Artificial Intelligence (GAI) tools. Methods Focusing on the behavioral intention of college students to utilize generative AI tools, the study employed a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model to analyze the behavioral intention of Chinese university students regarding GAI tools. Results Data were collected from 378 students and the results show that: effort expectation, performance expectation, and individual innovation positively affect the willingness to use GAI tools, whereas perceived trust and perceived risk do not. Effort expectation and performance expectation indirectly affects the behavioral intention through the mediation of personal innovativeness, while perceived humanlikeness negatively affect the willingness to use. Discussion These findings offer a valuable tool for policymakers and faculty members to understand the factors driving GAI acceptance or resistance. Thus, maximize the benefits of applying GAI tools and minimize potential risks and negative sentiment. Consequently, this understanding can facilitate the maximization of benefits derived from GAI tool application and the minimization of potential risks and negative sentiment. Such insights are crucial for the education sector to effectively embrace the transformative potential of GAI and foster the innovative capacity of students.

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