EFL learners’ motivation and acceptance of using large language models in English academic writing: an extension of the UTAUT model
Large language models (LLMs), represented by ChatGPT, are one of the most significant technological breakthroughs in generative AI and have begun to be applied in EFL writing instruction. The advent of LLMs presents both opportunities and challenges for EFL learners, underscoring the importance of e...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1514545/full |
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| Summary: | Large language models (LLMs), represented by ChatGPT, are one of the most significant technological breakthroughs in generative AI and have begun to be applied in EFL writing instruction. The advent of LLMs presents both opportunities and challenges for EFL learners, underscoring the importance of empirical evidence on their motivation and acceptance of using LLMs in learning English academic writing. This study recruited 238 participants who had completed one semester of training in using LLMs for business-related English academic writing. Participants answered question items based on the L2 Motivational Self System and the Unified Theory of Acceptance and Use of Technology (UTAUT). Partial least squares structural equation modeling (PLS-SEM) was employed to examine the structural relationships between the variables of motivation, region, previous learning experience, and the UTAUT model. Additionally, the moderating effect of motivation on the relationship between the four UTAUT determinants, behavioral intention, and use behavior was tested. The results show that performance expectancy and social influence significantly affect learners’ behavioral intention to use LLMs. Moreover, motivation proved to be a key factor in shaping both behavioral intention and actual use behavior, highlighting its crucial role in the adoption of technology for learning English academic writing. |
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| ISSN: | 1664-1078 |