心理学
技术接受模型
内在动机
结构方程建模
背景(考古学)
验证性因素分析
社会心理学
可用性
应用心理学
计算机科学
古生物学
人机交互
机器学习
生物
作者
Chung Yee Lai,Kwok Yip Cheung,Chee Seng Chan
标识
DOI:10.1016/j.caeai.2023.100178
摘要
ChatGPT, a powerful artificial intelligence chatbot, has great potential for active learning because of its ability to generate instant responses to academic inquiries and foster spontaneous interactions. This exploratory study investigated the roles of intrinsic motivation and factors of the technology acceptance model that influence ChatGPT acceptance for active learning among undergraduates in Hong Kong. Using a structural equation modeling approach, we examined the extended technology acceptance model in the context of higher education. Using self-report questionnaires, we obtained useful responses from 473 undergraduate students in Hong Kong in July 2023. The reliability and validity of the data were measured using confirmatory factor analysis, followed by path analysis to investigate the hypotheses in the proposed model. We identified intrinsic motivation as the strongest motivator for ChatGPT use intention. Consistent with the prior literature on technology acceptance, perceived usefulness was found to be a strong predictor of behavioral intention. In contrast to extant research, the findings indicate no significant relationship between perceived ease of use and behavioral intention. Neither perceived usefulness nor perceived ease of use were significant mediators in the relationship between intrinsic motivation and behavioral intention. These findings highlight the significant effect of intrinsic motivation on ChatGPT acceptance in supporting students' active learning. They also provide inspiration for ChatGPT developers and educationalists regarding the importance of intrinsic and extrinsic motivation (perceived usefulness) in promoting the broader acceptance of chatbots in the educational context. Efforts should be made to improve students' positive subjective experiences and the response quality of ChatGPT.
科研通智能强力驱动
Strongly Powered by AbleSci AI