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Improving English semantic learning outcomes through AI chatbot-based ARCS approach.

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Title: Improving English semantic learning outcomes through AI chatbot-based ARCS approach.
Authors: Chen, Mei-Rong Alice1 (AUTHOR) mralice@scu.edu.tw
Source: Interactive Learning Environments. Aug2025, Vol. 33 Issue 6, p3909-3924. 16p.
Subjects: Semantics, Motivation (Psychology), Limited English-proficient students, Educational outcomes, Teaching methods, Chatbots, Interactive learning
Abstract: Comprehending abstract ideas in English semantics courses challenges English as a Foreign Language (EFL) learners. Teacher-centered methods fail to engage learners, decreasing motivation and contextual understanding. While the ARCS (Attention, Relevance, Confidence, Satisfaction) model emphasizes motivation, the conventional ARCS (C-ARCS) approach lacks interactivity. To address these limitations, this study proposed an AI chatbot-based ARCS (AIC-ARCS) approach to enhance interaction in English semantics learning. The AIC-ARCS approach integrates annotation and learner explanations of semantic concepts to an AI chatbot. A pre-test-post-test quasi-experimental design evaluated this approach. Fifty English-major undergraduates participated: twenty-five students used the AIC-ARCS approach, while twenty-five employed C-ARCS with peer discussions. Results showed the AIC-ARCS approach significantly improved semantic achievement and self-efficacy while reducing learning anxiety compared to C-ARCS. These findings suggest AI chatbots in ARCS-based frameworks create more engaging learning environments. This study highlights the potential of AI chatbot-enhanced pedagogies in transforming EFL instruction, particularly in English semantics. Future research should explore broader applications of AI chatbots and their long-term impact on learning. [ABSTRACT FROM AUTHOR]
Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd 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.)
Database: Engineering Source
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  Data: Improving English semantic learning outcomes through AI chatbot-based ARCS approach.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Mei-Rong+Alice%22">Chen, Mei-Rong Alice</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mralice@scu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Interactive+Learning+Environments%22">Interactive Learning Environments</searchLink>. Aug2025, Vol. 33 Issue 6, p3909-3924. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation+%28Psychology%29%22">Motivation (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Limited+English-proficient+students%22">Limited English-proficient students</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+outcomes%22">Educational outcomes</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+methods%22">Teaching methods</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Interactive+learning%22">Interactive learning</searchLink>
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  Data: Comprehending abstract ideas in English semantics courses challenges English as a Foreign Language (EFL) learners. Teacher-centered methods fail to engage learners, decreasing motivation and contextual understanding. While the ARCS (Attention, Relevance, Confidence, Satisfaction) model emphasizes motivation, the conventional ARCS (C-ARCS) approach lacks interactivity. To address these limitations, this study proposed an AI chatbot-based ARCS (AIC-ARCS) approach to enhance interaction in English semantics learning. The AIC-ARCS approach integrates annotation and learner explanations of semantic concepts to an AI chatbot. A pre-test-post-test quasi-experimental design evaluated this approach. Fifty English-major undergraduates participated: twenty-five students used the AIC-ARCS approach, while twenty-five employed C-ARCS with peer discussions. Results showed the AIC-ARCS approach significantly improved semantic achievement and self-efficacy while reducing learning anxiety compared to C-ARCS. These findings suggest AI chatbots in ARCS-based frameworks create more engaging learning environments. This study highlights the potential of AI chatbot-enhanced pedagogies in transforming EFL instruction, particularly in English semantics. Future research should explore broader applications of AI chatbots and their long-term impact on learning. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd 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.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10494820.2025.2454443
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      – Code: eng
        Text: English
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      – SubjectFull: Semantics
        Type: general
      – SubjectFull: Motivation (Psychology)
        Type: general
      – SubjectFull: Limited English-proficient students
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      – SubjectFull: Educational outcomes
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      – SubjectFull: Teaching methods
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      – SubjectFull: Chatbots
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      – SubjectFull: Interactive learning
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              M: 08
              Text: Aug2025
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              Y: 2025
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