Factors Influencing Learner Attitudes Towards ChatGPT-Assisted Language Learning in Higher Education
Qianqian Cai, Lin Yu-peng, Zhonggen Yu
Abstract
Concerns regarding the potential risks associated with learners’ misusing ChatGPT necessitate an extensive investigation into learner attitudes towards ChatGPT-assisted language learning. This study adopts a mixed-method approach, combining structural equation modeling techniques and interviews. It aims to examine the influencing factors of learner attitudes regarding ChatGPT-assisted language learning under the extended three-tier technology use model from an interdisciplinary perspective, including the technology acceptance model, etc. The study finds that information system quality and hedonic motivation are more significant in contributing to performance expectancy and perceived satisfaction compared to self-regulation in ChatGPT-assisted language learning. Behavioral intention is a better predictor of learning effectiveness in ChatGPT-assisted language learning than perceived satisfaction and performance expectancy. This research also examines the partial or full mediating effects of behavioral intention and performance expectancy between other variables. Although this study is limited by some aspects (e.g., the outdated version of ChatGPT-3 or ChatGPT-3.5), it holds substantial implications for future practice and research. It appeals to more attention from future developers on hedonic motivation and information services of ChatGPT and from future researchers on a more comprehensive insight into influencing factors of learner attitudes towards ChatGPT-assisted language learning.