In EDS ansehen

Assessing the Impact of Microsoft Copilot and ChatGPT on EFL Learners' Interactional Metadiscourse in Argumentative Writing

Gespeichert in:
Bibliographische Detailangaben
Titel: Assessing the Impact of Microsoft Copilot and ChatGPT on EFL Learners' Interactional Metadiscourse in Argumentative Writing
Autoren: Rajab Esfandiari, Omid Allaf-Akbary
Quelle: Innoeduca, Vol 11, Iss 1 (2025)
Verlagsinformationen: Malaga University, 2025.
Publikationsjahr: 2025
Schlagwörter: LC8-6691, AI-enhanced learning, Metadiscourse realization, Special aspects of education, Semi-structured interview, Education
Beschreibung: The rise of artificial intelligence (AI) chatbots has significantly transformed the educational landscape, offering numerous opportunities for innovation and change. The current study assessed the comparative effects of ChatGPT-based instruction and Microsoft Copilot in helping Iranian English-as-a-foreign language (EFL) learners identify and realize interactional metadiscourse markers (IMMs) in argumentative writing and exploring their attitudes towards these two chatbots. Grounded in the theoretical framework of IM, this study followed a convergent parallel design. The study involved 90 male and female language learners randomly assigned to three groups: ChatGPT-based group (n = 30), Microsoft Copilot group (n = 30), and a control group (n = 30). The experimental groups were provided with 10 prompts per session for the implementation of IMMs, resulting in 60 prompts across six sessions. Instruction included initial training on using the respective AI tools, followed by practice sessions focusing on identifying and using IMMs in writing. The control group received conventional instruction, which involved identifying IMMs in reading passages with the guidance of the instructor. Interview questions were designed to elicit perspectives from learners on their experiences with ChatGPT and Microsoft Copilot. The responses from the interview data concerning learners' perceptions were analyzed through thematic analysis. Results showed that the Microsoft Copilot group demonstrated superior performance compared to the other two groups in identifying IMMs in the posttest. However, a one-way analysis of covariance (ANCOVA) showed that the difference between the ChatGPT-based group and the control group was not statistically significant. Additionally, responses to semi-structured interviews indicated that all learners had a positive perception of Microsoft Copilot for employing IMMs in argumentative writing. This study contributes to the field by providing empirical evidence on the effectiveness of specific AI-driven chatbots in enhancing critical writing skills, specifically through the lens of IMMs.
Publikationsart: Article
ISSN: 2444-2925
DOI: 10.24310/ijtei.111.2025.20896
Zugangs-URL: https://doaj.org/article/9e6cc892a65e466f95a8b1d6b1710270
Rights: CC BY NC ND
Dokumentencode: edsair.doi.dedup.....a770f8ab4c0c786e768489087d6d4b4c
Datenbank: OpenAIRE
Beschreibung
Abstract:The rise of artificial intelligence (AI) chatbots has significantly transformed the educational landscape, offering numerous opportunities for innovation and change. The current study assessed the comparative effects of ChatGPT-based instruction and Microsoft Copilot in helping Iranian English-as-a-foreign language (EFL) learners identify and realize interactional metadiscourse markers (IMMs) in argumentative writing and exploring their attitudes towards these two chatbots. Grounded in the theoretical framework of IM, this study followed a convergent parallel design. The study involved 90 male and female language learners randomly assigned to three groups: ChatGPT-based group (n = 30), Microsoft Copilot group (n = 30), and a control group (n = 30). The experimental groups were provided with 10 prompts per session for the implementation of IMMs, resulting in 60 prompts across six sessions. Instruction included initial training on using the respective AI tools, followed by practice sessions focusing on identifying and using IMMs in writing. The control group received conventional instruction, which involved identifying IMMs in reading passages with the guidance of the instructor. Interview questions were designed to elicit perspectives from learners on their experiences with ChatGPT and Microsoft Copilot. The responses from the interview data concerning learners' perceptions were analyzed through thematic analysis. Results showed that the Microsoft Copilot group demonstrated superior performance compared to the other two groups in identifying IMMs in the posttest. However, a one-way analysis of covariance (ANCOVA) showed that the difference between the ChatGPT-based group and the control group was not statistically significant. Additionally, responses to semi-structured interviews indicated that all learners had a positive perception of Microsoft Copilot for employing IMMs in argumentative writing. This study contributes to the field by providing empirical evidence on the effectiveness of specific AI-driven chatbots in enhancing critical writing skills, specifically through the lens of IMMs.
ISSN:24442925
DOI:10.24310/ijtei.111.2025.20896