Factors Influencing Learner Attitudes Towards ChatGPT-Assisted Language Learning in Higher Education

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 Although this study is limited by some aspects (e.g., the outdated version of ChatGPT-3 or ChatGPT-3.5), it holds substantial implications for future practice and research.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.

Keywords:

• Learner attitudes
• ChatGPT
• language learning
• factors
• higher education

Authors' contributions

Qianqian Cai: Methodology, Data curation, Formal analysis, Resources, Investigation, Software, Validation, Roles/Writing – original draft, Writing – review & editing; Yupeng Lin: Data curation, Writing – review & editing; Zhonggen Yu: Conceptualization, Supervision, and Funding acquisition.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Availability of data and material

We make sure that all data and materials support our published claims and comply with field standards.

Data availability statement

The data that support the findings of this study are openly available in [OSF] at [https://osf.io/5d9te/?view_only=f73f253d38f643588ea31a73bdd6376b].

Ethics approval statement

The study was approved by the institutional review board of Beijing Language and Culture University. All researchers can provide written informed consent.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work is supported by the [Key Research and Application Project of the Key Laboratory of Key Technologies for Localization Language Services of the State Administration of Press and Publication, “Research on Localization and Intelligent Language Education Technology for the ‘Belt and Road Initiative”] under Grant [Number CSLS 20230012]; and [Special fund of Beijing Co-construction Project-Research and reform of the "Undergraduate Teaching Reform and Innovation Project” of Beijing higher education in 2020-innovative “multilingual +” excellent talent training system] under Grant [Number 202010032003].

Notes on contributors

Qianqian Cai

Qianqian Cai, is presently a doctoral student majoring in applied linguistics of foreign languages at Faculty of Foreign Studies, Beijing Language and Culture University, China. She has written over 10 first-authored articles about technology-enhanced (language) education, which are in consideration for publication in reputable international journals.

Yupeng Lin

Yupeng Lin, is presently a postgraduate majoring in linguistic studies and applied linguistics of foreign languages at Faculty of Foreign Studies, Beijing Language and Culture University, China. He has written over 20 academic articles about technology-enhanced (language) education and published five first-authored papers in reputable international journals.

Zhonggen Yu

Zhonggen Yu, Professor (distinguished) and Ph.D. Supervisor in Faculty of International Studies, Beijing Language and Culture University, China. He is a research fellow of several academic institutions. He has published over 180 articles about technology-enhanced (language) education in distinguished journals based on rich teaching and research experiences.