Tian Weiqi, Ge Jingshen, Zhao Yu, Zheng Xu
College of Foreign Languages, Xinjiang University, Urumqi, China.
College of Liberal Arts, Journalism and Communication, Ocean University of China, Shandong, China.
Front Psychol. 2024 Feb 7;15:1268549. doi: 10.3389/fpsyg.2024.1268549. eCollection 2024.
This study is centered on investigating the acceptance and utilization of AI Chatbot technology among graduate students in China and its implications for higher education. Employing a fusion of the UTAUT (Unified Theory of Acceptance and Use of Technology) model and the ECM (Expectation-Confirmation Model), the research seeks to pinpoint the pivotal factors influencing students' attitudes, satisfaction, and behavioral intentions regarding AI Chatbots. The study constructs a model comprising seven substantial predictors aimed at precisely foreseeing users' intentions and behavior with AI Chatbots. Collected from 373 students enrolled in various universities across China, the self-reported data is subject to analysis using the partial-least squares method of structural equation modeling to confirm the model's reliability and validity. The findings validate seven out of the eleven proposed hypotheses, underscoring the influential role of ECM constructs, particularly "Confirmation" and "Satisfaction," outweighing the impact of UTAUT constructs on users' behavior. Specifically, users' perceived confirmation significantly influences their satisfaction and subsequent intention to continue using AI Chatbots. Additionally, "Personal innovativeness" emerges as a critical determinant shaping users' behavioral intention. This research emphasizes the need for further exploration of AI tool adoption in educational settings and encourages continued investigation of their potential in teaching and learning environments.
本研究聚焦于调查中国研究生对人工智能聊天机器人技术的接受与应用情况及其对高等教育的影响。该研究采用技术接受与使用统一理论(UTAUT)模型和期望确认模型(ECM)的融合,旨在找出影响学生对人工智能聊天机器人的态度、满意度和行为意图的关键因素。研究构建了一个包含七个重要预测变量的模型,旨在精确预测用户对人工智能聊天机器人的意图和行为。通过对来自中国各高校的373名学生收集的自报数据,采用结构方程模型的偏最小二乘法进行分析,以确认模型的可靠性和有效性。研究结果验证了所提出的11个假设中的7个,强调了ECM结构,特别是“确认”和“满意度”的影响作用超过了UTAUT结构对用户行为的影响。具体而言,用户感知到的确认显著影响他们的满意度以及后续继续使用人工智能聊天机器人的意图。此外,“个人创新性”成为塑造用户行为意图的关键决定因素。本研究强调需要进一步探索教育环境中人工智能工具的采用情况,并鼓励继续研究它们在教学和学习环境中的潜力。