Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?

Abstract Researchers have significantly explored language learners' attitudes toward ChatGPT through the lens of technology acceptance models, particularly with its development and integration into computer-assisted language learning (CALL). However, further research in this area is necessary t...

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Main Authors: Amir Reza Rahimi, Zahra Mosalli
Format: Article
Language:English
Published: SpringerOpen 2025-02-01
Series:Smart Learning Environments
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Online Access:https://doi.org/10.1186/s40561-025-00368-3
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author Amir Reza Rahimi
Zahra Mosalli
author_facet Amir Reza Rahimi
Zahra Mosalli
author_sort Amir Reza Rahimi
collection DOAJ
description Abstract Researchers have significantly explored language learners' attitudes toward ChatGPT through the lens of technology acceptance models, particularly with its development and integration into computer-assisted language learning (CALL). However, further research in this area is necessary to apply a theoretical framework with a pedagogical-oriented perspective. Therefore, in this study, the researchers utilized students' approaches to the learning environment (SAL) and extended it by incorporating a multilevel perspective that encompasses contextual, individual, and ChatGPT-related factors. Accordingly, the researchers integrated ChatGPT into their language syllabus and guided learners in three universities in Ardabil City to use ChatGPT during the academic year 2023–2024. In the end, 214 participants answered the study questionnaire. The result of the partial least squares modeling (PLS-SEM), and Importance performance map analysis (IPMA) showed that ChatGPT leadership, where the university executive provides the atmosphere for the norms of ChatGPT integration, could significantly shape language learners’ organizing approach to using it in their daily academic schedule. Additionally, personalization and anthropomorphism were among the significant ChatGPT-related factors that shaped learners’ deep approach to using ChatGPT as a source for meaningful, cross-referenced CALL tool. However, low feedback reliability, privacy concerns, and the ChatGPT's perceived value contributed to language learners' surface approach to minimizing its use as a ChaGPT-related factor. On the basis of these findings, the study introduces a new conceptual framework for CALL and artificial intelligence language learning (AILL) and suggests that ChatGPT leadership should be promoted at a macro-contextual level that might cover other micro-contextual, personal, and ChatGPT-related factors, including ChatGPT's price-value, personalization, and language learners' motivation, which are important elements to shape learners' approaches to CHAGPTALL.
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spelling doaj-art-b566d1df8e034896ba2610111cf5bdcc2025-02-09T12:59:22ZengSpringerOpenSmart Learning Environments2196-70912025-02-0112112410.1186/s40561-025-00368-3Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?Amir Reza Rahimi0Zahra Mosalli1University of ValenciaAlZahra UniversityAbstract Researchers have significantly explored language learners' attitudes toward ChatGPT through the lens of technology acceptance models, particularly with its development and integration into computer-assisted language learning (CALL). However, further research in this area is necessary to apply a theoretical framework with a pedagogical-oriented perspective. Therefore, in this study, the researchers utilized students' approaches to the learning environment (SAL) and extended it by incorporating a multilevel perspective that encompasses contextual, individual, and ChatGPT-related factors. Accordingly, the researchers integrated ChatGPT into their language syllabus and guided learners in three universities in Ardabil City to use ChatGPT during the academic year 2023–2024. In the end, 214 participants answered the study questionnaire. The result of the partial least squares modeling (PLS-SEM), and Importance performance map analysis (IPMA) showed that ChatGPT leadership, where the university executive provides the atmosphere for the norms of ChatGPT integration, could significantly shape language learners’ organizing approach to using it in their daily academic schedule. Additionally, personalization and anthropomorphism were among the significant ChatGPT-related factors that shaped learners’ deep approach to using ChatGPT as a source for meaningful, cross-referenced CALL tool. However, low feedback reliability, privacy concerns, and the ChatGPT's perceived value contributed to language learners' surface approach to minimizing its use as a ChaGPT-related factor. On the basis of these findings, the study introduces a new conceptual framework for CALL and artificial intelligence language learning (AILL) and suggests that ChatGPT leadership should be promoted at a macro-contextual level that might cover other micro-contextual, personal, and ChatGPT-related factors, including ChatGPT's price-value, personalization, and language learners' motivation, which are important elements to shape learners' approaches to CHAGPTALL.https://doi.org/10.1186/s40561-025-00368-3Artificial intelligence language learning (AILL)Artificial intelligence in educationChatGPT in EducationStudents’ Approaches to learning environment (SAL)Computer Assisted Language Learning (CALL)
spellingShingle Amir Reza Rahimi
Zahra Mosalli
Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?
Smart Learning Environments
Artificial intelligence language learning (AILL)
Artificial intelligence in education
ChatGPT in Education
Students’ Approaches to learning environment (SAL)
Computer Assisted Language Learning (CALL)
title Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?
title_full Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?
title_fullStr Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?
title_full_unstemmed Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?
title_short Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?
title_sort language learners surface deep and organizing approaches to chatgpt assisted language learning what contextual individual and chatgpt related factors contribute
topic Artificial intelligence language learning (AILL)
Artificial intelligence in education
ChatGPT in Education
Students’ Approaches to learning environment (SAL)
Computer Assisted Language Learning (CALL)
url https://doi.org/10.1186/s40561-025-00368-3
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