Journal of Applied Linguistics and Applied Literature: Dynamics and Advances

JALDA

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

The Role of Individual Differences and AI Chatbots in EFL Learners' Metadiscourse Realization in Expository Writing

Articles in Press

Document Type : Research Article

Authors

1 Associate Prtofessor of TEFL, Department of English Language Teaching, Faculty of Literature and Humanities, Imam Khomeini International University, Qazvin Iran

2 PhD in TEFL, Department of English Language, Faculty of Humanities, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

This study sought to explore how personality traits (extroverts and introverts) affect EFL learners' utilization of interactional metadiscourse markers (IMMs) in expository writing when they use two AI chatbots, Gemini and Microsoft Copilot. Additionally, the study analyzed learners' experiences and preferences when the learners interact with these chatbots to understand their perceptions and overall satisfaction. The participants consisted of 150 advanced language learners randomly assigned to four experimental groups: Gemini extroverted learners, Gemini introverted learners, Microsoft Copilot extroverted learners, Microsoft Copilot introverted learners, and a control group. Throughout eight sessions, the participants in the Gemini group utilized the Gemini AI platform on their computer monitors to investigate IMMs, while the Microsoft Copilot groups were exposed to IMMs through Microsoft Copilot AI companion. The control group was taught using traditional methods, which involved reading the designated instructional materials. The results of a one-way analysis of covariance (ANCOVA) procedure revealed that the introverted advanced participants in the Gemini group surpassed the other groups in the posttest focused on recognizing and identifying IMMs, with statistically significant differences among the groups. In the posttest, there was also an improvement in both Microsoft Copilot advanced introverted and extroverted learners in realizing IMMs in expository writing. The Gemini advanced extroverted group and the control group performed poorly compared to the other groups. Semi-structured interview results analyzed through MAXQDA (version 2022) suggested that Microsoft Copilot adeptly supports both advanced extroverted and introverted learners in refining their expository writing skills by facilitating metadiscourse development.

Keywords

Main Subjects

Article Title [Persian]

تفاوت های فردی و چت‌بات‌های هوش مصنوعی: نقش آن‌ها در تحقق متاخطاب در نوشتار توضیحی زبان‌آموزان انگلیسی به عنوان یک زبان خارجی

Author [Persian]

  • دکتر رجب اسفندیاری 1

1 دانشیار آموزش زبان انگلیسی، گروه زبان انگلیسی، دانشکده ادبیات و علوم انسانی، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران

2 دکتری آموزش زبان انگلیسی، گروه زبان و ادبیات انگلیسی، دانشکده ادبیات و علوم انسانی، دانشگاه محقق اردبیلی، اردبیل، ایران

Abstract [Persian]

هدف این پژوهش بررسی چگونگی تأثیر ویژگی‌های شخصیتی (برون‌گرایان و درون‌گرایان) بر استفاده از نشانگرهای متاخطاب در نویسندگی توضیحی زبان‌آموزان انگلیسی به عنوان زبان خارجی (EFL) است؛ به طور خاص در استفاده از دو چت‌بات هوش مصنوعی، جمینی و مایکروسافت کاپایلوت. علاوه بر این، در این پژوهش به تجزیه و تحلیل تجربیات و ترجیحات زبان‌آموزان در تعامل با این چت‌بات‌ها برای درک ادراکات و رضایت کلی آن‌ها پرداخته شد. شرکت‌کنندگان شامل 150 زبان‌آموز زن و مرد پیشرفته بودند که به صورت تصادفی به چهار گروه آزمایشی تقسیم شدند: زبان‌آموزان برون‌گرای جمینی، زبان‌آموزان درون‌گرای جمینی، زبان‌آموزان برون‌گرای مایکروسافت کاپایلوت، زبان‌آموزان درون‌گرای مایکروسافت کاپایلوت و یک گروه کنترل. در طول مدت درمان که هشت جلسه به طول انجامید، دو گروه جمینی از هوش مصنوعی جمینی برای بحث و بررسی نشانگرهای متاخطاب بر روی یک صفحه کامپیوتر استفاده کردند و دو گروه مایکروسافت کاپایلوت از نشانگرهای متاخطاب از طریق هوش مصنوعی مایکروسافت کاپایلوت بهره‌مند شدند. گروه کنترل روش‌های آموزش مرسوم شامل خواندن مطالب درسی مشخص را دریافت کرد. نتایج تحلیل کوواریانس یک‌طرفه نشان داد که گروه درون‌گرای جمینی در آزمون پایانی شناسایی و تحقق نشانگرهای متاخطاب نسبت به سه گروه دیگر عملکرد بهتری داشتند. با این حال، مقایسه‌های پساهاوک تفاوت‌های معناداری بین گروه‌های مختلف در شناسایی نشانگرهای متاخطاب نشان داد. علاوه بر این، در آزمون پس از درمان، پیشرفت در هر دو گروه زبان‌آموزان درون‌گرا و برون‌گرای مایکروسافت کاپایلوت در تحقق نشانگرهای متاخطاب در نویسندگی توضیحی مشاهده شد. گروه برون‌گرای جمینی و گروه کنترل نسبت به دیگر گروه‌ها عملکرد ضعیف‌تری داشتند. نتایج مصاحبه نیمه‌ساختاریافته از طریق نرم‌افزار MAXQDA (نسخه 2022) تحلیل شد. نتایج این مطالعه نشان می‌دهد که استفاده از مایکروسافت کاپایلوت به طور هم‌زمان به زبان‌آموزان درون‌گرا و برون‌گرا کمک می‌کند تا عملکرد نویسندگی توضیحی خود را از طریق تحقق متاخطاب توسعه دهند.

Keywords [Persian]

  • نوشتار توضیحی
  • جمینی
  • متاخطاب
  • مایکروسافت کاپایلوت
References
Ädel, A. (2023). Adopting a ‘move’ rather than a ‘marker’ approach to metadiscourse: A taxonomy for spoken student presentations. English for Specific Purposes, 6(69), 4-18. https://doi.org/10.1016/j.esp.2022.09.001
Agarwal, V., & Misra, S. (2025). NCERT psychology class 12. SBPD publications.
Bialkova, S. (2024). The Rise of AI User Applications: Chatbots Integration Foundations and Trends. Springer. http://dx.doi.org/10.1007/978-3-031-56471-0
Barrot, J. S. (2023). Using ChatGPT for second language writing: Pitfalls and potentials. Assessing Writing, 57(2), 100745. https://doi.org/10.1016/j.asw.2023.100745
Basu, A. (2025). AI Tools for Everyone: Your Guide to Artificial Intelligence. Springer Nature Switzerland.
Bernad-Mechó, E., & Valeiras-Jurado, J. (2023). Multimodal engagement strategies in science dissemination: A case study of TED talks and YouTube science videos. Discourse Studies, 25(6), 733-754. http://dx.doi.org/10.1177/14614456231161755
Bielza, C., & Larrañaga, P. (2020). Data-Driven Computational Neuroscience: Machine Learning and Statistical Models. Cambridge University Press. http://dx.doi.org/10.1017/9781108642989
Bonner, E., Lege, R., & Frazier, E. (2023). Large language model-based artificial intelligence in the language classroom: practical ideas for teaching.  Teaching English with Technology, 23(1), 23-41. http://dx.doi.org/10.56297/BKAM1691/WIEO1749
Bui, H. P. (2024). Applied linguistics and language education research methods: fundamentals and innovations. IGI Global.
Creswell, J. W. (2012). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research (4th ed.). Pearson.
Ding, L., & Zou, D. (2024). Automated writing evaluation systems: A systematic review of Grammarly, Pigai, and Criterion with a perspective on future directions in the age of generative artificial intelligence. Education and Information Technologies, 29(11), 14151-14203. https://doi.org/10.1007/s10639-023-12402-3
Dörnyei, Z., & Dewaele, J. (2022). Questionnaires In Second Language Research: Construction, Administration, and Processing. Routledge. http://dx.doi.org/10.4324/9781003331926
Du, J., & Daniel, B. K. (2024). Transforming language education: A systematic review of AI-powered chatbots for English as a foreign language speaking practice. Computers and Education: Artificial Intelligence, 6(3), 100-130. https://doi.org/10.1016/j.caeai.2024.100230
El-Dakhs, D. A. S., Yahya, N., & Pawlak, M. (2022). Investigating the impact of explicit and implicit instruction on the use of interactional metadiscourse markers. Asian-Pacific Journal of Second and Foreign Language Education, 7(1), 44. http://dx.doi.org/10.1186/s40862-022-00175-0
ELSenbawy, O. M., Patel, K. B., Wannakuwatte, R. A., & Thota, A. N. (2025). Use of generative large language models for patient education on common surgical conditions: a comparative analysis between ChatGPT and Google Gemini. Updates in Surgery, 28(2), 76-97. https://doi.org/10.1007/s13304-025-02074-8
Esfandiari, R., & Allaf-Akbary, O. (2024a). The role of learning-oriented language assessment in promoting interactional metadiscourse in ectenic and synoptic EFL Learners. Journal of Modern Research in English Language Studies, 11(3), 181-206. https://doi.org/10.30479/jmrels.2024.19777.2305
Esfandiari, R., Allaf-Akbary, O. (2024b). Assessing interactional metadiscourse in EFL writing through intelligent data-driven learning: the Microsoft Copilot in the spotlight. Language Testing in Asia 14(1), 51. https://doi.org/10.1186/s40468-024-00326-9
Fields, Z. (2024). Impacts of Generative AI on Creativity in Higher Education. IGI Global. https://www.igi-global.com/book/impacts-generative-creativity-higher-education/333061
Gibson, D. C., & Ifenthaler, D. (2024). Computational Learning Theories: Models for Artificial Intelligence Promoting Learning Processes. Springer Nature Switzerland. http://dx.doi.org/10.1007/978-3-031-65898-3
Hagger, S. M., Cameron, D. L., & Hamilton, K. (2020). The Handbook of behavior change. Cambridge University Press. http://dx.doi.org/10.1017/9781108677318
Hyland, K. (2019). Metadiscourse: Exploring Interaction in Writing (2nd edition). Continuum.
Hyland, K., & Jiang, F. K. (2022). Metadiscourse choices in EAP: An intra-journal study of JEAP. Journal of English for Academic Purposes, 60(2), 101-165. https://doi.org/10.1016/j.jeap.2022.101165
Izquierdo, M., & Pérez Blanco, M. (2023). Interactional metadiscourse: Building rapport and solidarity in informational-persuasive discourse. An English-Spanish case study. Journal of Pragmatics, 216(2), 106–120. https://doi.org/10.1016/j.pragma.2023.08.005
Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies, 28(12), 15873-15892. https://doi.org/10.1007/s10639-023-11834-1
Johnson, J. S., & Lim, G. S. (2009). The influence of rater language background on writing performance assessment. Language Testing, 26(4), 485-505. https://doi.org/10.1177/0265532209340186
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., & Weller, J. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.‌2023.102274
Lee, U., Jung, H., Jeon, Y., Sohn, Y., Hwang, W., Moon, J., & Kim, H. (2023). Few-shot is enough: exploring ChatGPT prompt engineering method for automatic question generation in English education. Education and Information Technologies, 29(9), 1-33. http://dx.doi.org/‌10.1007/‌s10639-023-12249-8
Liu, Q., & Cheng, W. (2025). “I'm telling you”: The use of interactional metadiscourse in Chinese live streaming commerce. Journal of pragmatics, 237, 14-29. https://doi.org/10.1016/j.pragma.2025.‌01.001
Liu, C., & Tseng, M. (2021). Paradigmatic variation in hedging and boosting: A comparative study of discussions in narrative inquiry and grounded theory research. Journal of English for Specific Purposes, 61, 1-16. http://dx.doi.org/10.1016/j.esp.2020.08.002
Lytras, D. M., Alkhaldi, A., Malik, S., Claudia Serban, A. C., & Aldosemani, T. I. (2025). The Evolution of Artificial Intelligence in Higher Education: Challenges, Risks, and Ethical Considerations. Emerald Publishing Limited. http://dx.doi.org/10.1108/9781835494868
Ma, Y., Y Jiang, F. K. (2025). Guiding and engaging the audience: Visual metadiscourse in PowerPoint slides of Three Minute Thesis presentations. Journal of English for Specific Purposes, 77(1), 56-70. https://doi.org/10.1016/j.esp.2024.10.003
McCallum, L. (2024). New takes on developing intercultural communicative competence: using AI tools in telecollaboration task design and task completion. Journal for Multicultural Education, 18(1), 153-172. https://doi.org/10.1108/JME-06-2023-0043
Minnick, C. (2025). Microsoft Copilot for Dummies. Wiley.
Mishra, B. K., & Kumar, R. (Eds.). (2020). Natural language processing in artificial intelligence. Apple Academic Press, Incorporated. https://doi.org/10.1201/9780367808495
Moorhouse, B. L., & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122(1), 103-290. http://dx.doi.org/10.1016/j.system.2024.103290
Nave, S. C., & Carducci, J. B. (Eds.). (2021). The Wiley Encyclopedia of Personality and Individual Differences, Models and Theories. Wiley.
Paltridge, B., & Prior, M.T. (Eds.). (2024). The Routledge handbook of second language acquisition and discourse (1st ed.). Routledge. https://doi.org/10.4324/9781003177579
Pan, F. (2024). AI in Language Teaching, Learning, and Assessment. IGI Global.
Panini, I. (2024). Microsoft Copilot AI: Complete Guide and Ready to Use Manual with Integration in Office 365. Amazon Digital Services LLC – Kdp.
Pentina, I., Hancock, T., & Xie, T. (2023). Exploring relationship development with social chatbots: A mixed-method study of replica. Computers in Human Behavior, 140(2), 107-127. https://doi.org/10.1016/j.chb.2022.107600
Phakiti, A. (2003). A closer look at the relationship of cognitive and metacognitive strategy use to EFL reading comprehension test performance. Language Testing, 20(1), 26-56. http://dx.doi.org/10.1191/0265532203lt243oa
Posavec, K. (2025). Implementing Personalized Learning Techniques with AI. IGI Global.
Rahman, R. (2024). Microsoft Copilot for Power Apps: Transforming App Development with AI Assistance. Apress L. P.
Ryckman, M. R. (2020). Theories of Personality. Wadsworth.
Schütze, U. (2024). Virtual Reality, Artificial Intelligence, and Language Learning the Need for Attention. John Benjamins Publishing Company. https://doi.org/10.1075/bpa.19
Shen, Q., & Tao, Y. (2021). Stance markers in English medical research articles and newspaper opinion columns: A comparative corpus-based study. PLoS One, 16(3), 247-265. https://doi.org/10.1371/journal.pone.0247981
Sherkuziyeva, N., Imamutdinovna Gabidullina, F., Ahmed Abdel-Al Ibrahim, K., & Bayat, S. (2023). The comparative effect of computerized dynamic assessment and rater mediated assessment on EFL learners’ oral proficiency, writing performance, and test anxiety. Language Testing in Asia, 13(1), 15-24. https://doi.org/10.1186/s40468-023-00227-3
Soni, M. (2024). Artificial Intelligence Tools. Study guide.
Stratton, J. (2024). Copilot for Microsoft 365: Harness the Power of Generative AI in the Microsoft Apps You Use Every Day. Apress. https://doi.org/10.1007/979-8-8688-0447-2
Su, J., & Yang, W. (2023). AI literacy curriculum and its relation to children’s perceptions of robots and attitudes towards engineering and science: An intervention study in early childhood education. Journal of Computer Assisted Learning, 5(2), 67-81. https://doi.org/10.1111/jcal.12867
Tafazoli, D. (2024). Exploring the potential of generative AI in democratizing English language education. Computers and Education: Artificial Intelligence, 7(2), 100-274. https://doi.org/10.1016/j.caeai.2024.100275
Tai, T., & Chen H. H. (2024). Improving elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions. Computers & Education, 220, Article 105112. https://doi.org/10.1016/j.compedu.2024.105112
Travers, C. J. (2022). Reflective Goal Setting: An Applied Approach to Personal and Leadership Development. Palgrave Macmillan.
Triki, N. (2024). Exemplification and reformulation in expert linguists’ writings: Elaborative metadiscourse between disciplinarity and individuality. Journal of English for Academic Purposes, 71. https://doi.org/10.1016/j.jeap.2024.101407
Wei, J. (2024). Tracking Interaction in Chinese Scholars’ Academic Writing Through the Lens of Metadiscourse. Springer Nature Singapore. https://doi.org/10.1007/978-981-97-2328-7
Weisi, H., & Zandi, M. (2024). A mixed-method concurrent transformative study of metadiscourse markers employed by L2 speakers: Does proficiency level matter? Language Testing in Asia, 14(1), 60. https://doi.org/10.1186/s40468-024-00330-z
Yang, N. (2021). Engaging readers across participants: A cross-interactant analysis of metadiscourse in letters of advice during the COVID-19 pandemic. Journal of Pragmatics, 186(2), 181-193. https://doi.org/10.1016/j.pragma.2021.10.017
Yim, I. H. Y., & Su, J. (2024). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 12(1), 93-131.  https://doi.org/10.1007/s40692-023-00304-9
Zhang, R., Zou, D., & Cheng, G. (2023). A review of chatbot-assisted learning: pedagogical approaches, implementations, factors leading to effectiveness, theories, and future directions. Interactive Learning Environments, 32(8), 4529-4557. https://doi.org/10.1080/10494820.2023.2202704
Journal of Applied Linguistics and Applied Literature: Dynamics and Advances

Articles in Press, Accepted Manuscript
Available Online from 11 September 2025
Files
History
  • Receive Date: 12 February 2025
  • Revise Date: 09 April 2025
  • Accept Date: 11 September 2025
Share
How to cite
Statistics