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Modelling College Students' Acceptance to Use Generative Artificial Intelligence for Second Language Learning: A Theory of Planned Behaviour Perspective.

  • Published In: European Journal of Education, 2025, v. 60, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ma, Yuxia 3 of 3

Abstract

The benefits of Generative Artificial Intelligence (GenAI) in enhancing second language (L2) learning are well established. However, these advantages can only be realised if learners are willing to adopt the technology. This study, grounded in the Theory of Planned Behaviour (TPB), investigated the factors influencing the behavioural intention to use GenAI among 337 Chinese college L2 learners using five validated scales. A Structural Equation Modelling (SEM) approach with Amos 24 yielded several key findings. Notably, demographic factors encompassing gender and age did not significantly affect the TPB components. Subjective norm and attitude were found to have a positive and significant impact on behavioural intention, while perceived behavioural control did not demonstrate a significant effect. Furthermore, GenAI literacy emerged as a significant predictor of behavioural intention, both directly and indirectly through its influence on attitude. Collectively, these variables accounted for 51.6% of the variance in behavioural intention. The study also discusses the theoretical and pedagogical implications and offers suggestions for future research. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:European Journal of Education. 2025/03, Vol. 60, Issue 1, p1
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:0141-8211
  • DOI:10.1111/ejed.12923
  • Accession Number:183654411
  • Copyright Statement:Copyright of European Journal of Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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