Uncovering personalized L2 motivation and self-regulation in ChatGPT-assisted language learning: A hybrid PLS-SEM-ANN approach

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL Computers in human behavior reports Pub Date : 2025-03-01 Epub Date: 2024-12-07 DOI:10.1016/j.chbr.2024.100539
Amir Reza Rahimi , Mahshad Sheyhkholeslami , Ali Mahmoudi Pour
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Abstract

Currently, chatbots powered by artificial intelligence (AI) have gained considerable attention due to their ability to provide personalized language learning (PLL) for learners. In this regard, recent studies have extensively explored learners' emotional aspects, such as their attitudes and acceptance of personalized language learning in chatbots. It is, however, unclear what factors might determine their cognitive behaviors in such a personalized language learning environment, particularly their self-regulation. To fill the gap, the researchers collected data from 133 Iranian EFL learners who had personalized language learning through ChatGPT in their language learning institute and answered our questionnaire that tapped on their personalized L2 motivational self-system (PEL2MSS) and their personalized self-regulation (PESRL). The researchers analyzed the empirical data using a hybrid SEM-artificial neural network (SEM-ANN), in contrast to previous literature that primarily relied on structural equation modeling (SEM). The results showed that ChatGPT significantly responded to language learners' current L2-self and their ought to L2-self to pass their obligation, and metrics to reach their goals resulted in seeking more assistance from ChatGPT and evaluating their language learning progress with it. Moreover, the sign of digital self-authenticity was also discovered by the researchers, where learners dedicated more motivation to learn language with ChatGPT in comparison with their previous language learning environments, which culminated in having more self-evaluation, goal-setting, and daily academic schedule to learn language with ChatGPT. Additionally, the ANN analysis supported the linear findings of the PLS-SEM by showing that language learners' current L2-self, digital self-authenticity, and ought to L2-self were the most significant motivational factors affecting their PESRL. Based on these findings, a new conceptual framework for the PLL was developed in the literature, and the research view was shifted from covering language learners' emotional aspects to their cognitive aspects in this environment. Thus we recommend that language teachers should avoid seeing ChatGPT as a tool that learners use for cheating; rather, it can be used as a co-teacher outside of the classroom to help students cover their present language learning needs, which might not be covered in the classroom due to the time restriction.
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在chatgpt辅助语言学习中发现个性化的第二语言动机和自我调节:一种混合PLS-SEM-ANN方法
目前,由人工智能(AI)驱动的聊天机器人因其为学习者提供个性化语言学习(PLL)的能力而受到了相当大的关注。在这方面,最近的研究广泛地探讨了学习者的情感方面,例如他们对聊天机器人中个性化语言学习的态度和接受程度。然而,在这种个性化的语言学习环境中,哪些因素可能决定他们的认知行为,尤其是他们的自我调节,目前还不清楚。为了填补这一空白,研究人员收集了133名在语言学习机构通过ChatGPT进行个性化语言学习的伊朗英语学习者的数据,并回答了我们的问卷,问卷利用了他们的个性化第二语言动机自我系统(PEL2MSS)和个性化自我调节(PESRL)。研究人员使用混合SEM-人工神经网络(SEM- ann)分析了经验数据,与之前主要依赖结构方程模型(SEM)的文献不同。结果表明,ChatGPT对语言学习者当前的l2自我和他们应该通过l2自我来完成他们的义务有显著的反应,达到目标的指标导致他们向ChatGPT寻求更多的帮助,并评估他们的语言学习进展。此外,研究人员还发现了数字自我真实性的标志,学习者使用ChatGPT学习语言的动机比以前的语言学习环境更强,最终表现为有更多的自我评价、目标设定和日常学习计划来使用ChatGPT学习语言。此外,人工神经网络分析支持PLS-SEM的线性结果,表明语言学习者的当前l2自我、数字自我真实性和应该l2自我是影响其PESRL的最显著动机因素。基于这些发现,文献中发展了一个新的语言学习的概念框架,并将研究视角从语言学习者的情感方面转移到语言学习者在这种环境下的认知方面。因此,我们建议语言教师应避免将ChatGPT视为学习者用于作弊的工具;相反,它可以作为课堂外的合作老师,帮助学生满足他们目前的语言学习需求,这些需求可能由于时间的限制而无法在课堂上得到满足。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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