COMPARATIVE ANALYSIS OF LLM-BASED AI ASSISTANTS FOR EFL WRITING INSTRUCTION: CHATGPT, CLAUDE, GEMINI, AND DEEPSEEK
DOI:
https://doi.org/10.31851/wdz59c93Keywords:
Artificial Intelligence, Educational Technology, English Language Teaching, Language Explanation, Writing InstructionAbstract
This study investigates the comparative capabilities of four prominent LLM-based AI assistants (ChatGPT, Claude, Gemini, and DeepSeek) in supporting English as a Foreign Language (EFL) writing instruction, particularly in providing explanations of key linguistic concepts. Employing a three-phase research design, the study examines how these AI tools assist EFL teachers in explaining grammar, vocabulary usage, sentence structure, and cohesion/coherence. In Phase 1, standardized prompts were developed based on Khan’s prompt engineering framework to ensure consistency across AI interactions. Phase 2 involved submitting these prompts to each AI system under controlled conditions using their free versions to reflect common educational constraints. In Phase 3, the AI-generated responses were analyzed thematically using Braun and Clarke’s framework, focusing on clarity, conceptual depth, organization, and instructional value. The findings reveal distinct pedagogical profiles: ChatGPT offers structured and practice-oriented explanations, Claude emphasizes conceptual depth and academic reasoning, Gemini employs accessible and engaging explanatory strategies, and DeepSeek provides concise explanations suitable for quick reference. This study provides practical guidance for EFL teachers in selecting AI tools based on instructional context and learner needs.
References
Adetayo, A. J., Aborisade, M. O., & Sanni, B. A. (2024). Microsoft Copilot and Anthropic Claude AI in education and library service. Library Hi Tech News. https://doi.org/10.1108/LHTN-01-2024-0002
Almohawes, M. (2024). Second language acquisition theories and how they contribute to language learning. World Journal of English Language, 14(3), 181. https://doi.org/10.5430/wjel.v14n3p181
Alnasib, B. N. M., & Alharbi, N. S. (2024). Challenges and motivation: Assessing Gemini’s impact on undergraduate EFL students in classroom settings. World Journal of English Language, 14(5), 501. https://doi.org/10.5430/wjel.v14n5p501
Barrera Castro, G. P., Chiappe, A., Becerra Rodriguez, D. F., & Sepulveda, F. G. (2024). Harnessing AI for education 4.0: Drivers of personalized learning. Electronic Journal of E-Learning, 22(5), 01–14. https://doi.org/10.34190/ejel.22.5.3467
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, 2023(1). https://doi.org/10.56297/BKAM1691/WIEO1749
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. In Sage Publications (p. 320). https://books.google.se/books?id=Hr11DwAAQBAJ&hl=sv
Centa Strahovnik, M. (2023). Identiteta in pogovorni sistemi umetne inteligence. Bogoslovni Vestnik, 83(4). https://doi.org/10.34291/BV2023/04/Centa
Cherukunnath, D., & Singh, A. P. (2022). Exploring cognitive processes of knowledge acquisition to upgrade academic practices. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.682628
Combs, K., Lu, H., & Bihl, T. J. (2023). Transfer learning and analogical inference: A critical comparison of algorithms, methods, and applications. Algorithms, 16(3), 146. https://doi.org/10.3390/a16030146
Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A. (2024). Integration of generative artificial intelligence in higher education: Best practices. Education Sciences, 15(1), 32. https://doi.org/10.3390/educsci15010032
Evmenova, A. S., Regan, K., Mergen, R., & Hrisseh, R. (2024). Improving writing feedback for struggling writers: Generative AI to the rescue? TechTrends, 68(4), 790–802. https://doi.org/10.1007/s11528-024-00965-y
Extance, A. (2023). ChatGPT has entered the classroom: How LLMs could transform education. Nature, 623(7987), 474–477. https://doi.org/10.1038/d41586-023-03507-3
Gao, L. (Xuehui), López-Pérez, M. E., Melero-Polo, I., & Trifu, A. (2024). Ask ChatGPT first! Transforming learning experiences in the age of artificial intelligence. Studies in Higher Education, 49(12), 2772–2796. https://doi.org/10.1080/03075079.2024.2323571
Hamman Ortiz, L., Santiago Schwarz, V., Hamm‐Rodríguez, M., & Gort, M. (2023). Engaging teachers in genre‐based pedagogy for writing arguments: A case study of shifts in practice and understanding. TESOL Quarterly, 57(2), 402–432. https://doi.org/10.1002/tesq.3156
Herman, H., Rafiek, M., Agustina, T., Saddhono, K., Malabar, S., Saputra, N., & Purba, R. (2023). Exploring the metafunctions to improve EFL learners’ writing ability in the perspective of systemic functional linguistics. Research Journal in Advanced Humanities, 4(2). https://doi.org/10.58256/rjah.v4i2.1195
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
Huang, F., Peng, D., & Teo, T. (2025). AI affordances and EFL learners’ speaking engagement: The moderating roles of gender and learner type. European Journal of Education, 60(1). https://doi.org/10.1111/ejed.70041
Jiang, Z., Xu, Z., Pan, Z., He, J., & Xie, K. (2023). Exploring the role of artificial intelligence in facilitating assessment of writing performance in second language learning. Languages, 8(4), 247. https://doi.org/10.3390/languages8040247
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., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kayalı, B., Yavuz, M., Balat, Ş., & Çalışan, M. (2023). Investigation of student experiences with ChatGPT-Supported online learning applications in higher education. Australasian Journal of Educational Technology, 39(5), 20–39. https://doi.org/10.14742/ajet.8915
Khan, I. (2024). The quick guide to prompt engineering: Generative AI tips and tricks for ChatGPT, Bard, Dall-E, and Midjourney. Wiley.
Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069–6104. https://doi.org/10.1007/s10639-021-10831-6
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
Konet, A., Thomas, I., Gartlehner, G., Kahwati, L., Hilscher, R., Kugley, S., Crotty, K., Viswanathan, M., & Chew, R. (2024). Performance of two large language models for data extraction in evidence synthesis. Research Synthesis Methods, 15(5), 818–824. https://doi.org/10.1002/jrsm.1732
Korzynski, P., Mazurek, G., Krzypkowska, P., & Kurasinski, A. (2023). Artificial intelligence prompt engineering as a new digital competence: Analysis of generative AI technologies such as ChatGPT. Entrepreneurial Business and Economics Review, 11(3), 25–37. https://doi.org/10.15678/EBER.2023.110302
Krajka, J., & Olszak, I. (2024). “AI, will you help?” How learners use Artificial Intelligence when writing. XLinguae, 17(1), 34–48. https://doi.org/10.18355/XL.2024.17.01.03
Lang, G., Triantoro, T., & Sharp, Ja. (2024). Large language models as ai-powered educational assistants: Comparing GPT-4 and Gemini for writing teaching cases. Journal of Information Systems Education, 35(3), 390–407. https://doi.org/10.62273/YCIJ6454
Lee, D., Kim, H., & Sung, S.-H. (2023). Development research on an AI English learning support system to facilitate learner-generated-context-based learning. Educational Technology Research and Development, 71(2), 629–666. https://doi.org/10.1007/s11423-022-10172-2
Lin, Z. (2023). Why and how to embrace AI such as ChatGPT in your academic life. Royal Society Open Science, 10(8). https://doi.org/10.1098/rsos.230658
Liu, Z.-M., Hwang, G.-J., Chen, C.-Q., Chen, X.-D., & Ye, X.-D. (2024). Integrating large language models into EFL writing instruction: Effects on performance, self-regulated learning strategies, and motivation. Computer Assisted Language Learning, 1–25. https://doi.org/10.1080/09588221.2024.2389923
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5), 570–581. https://doi.org/10.1002/asi.24750
Makridakis, S., Petropoulos, F., & Kang, Y. (2023). Large language models: Their success and impact. Forecasting, 5(3), 536–549. https://doi.org/10.3390/forecast5030030
Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., Darwis, A., & Marzuki. (2023). Exploring Artificial Intelligence in Academic Essay: Higher Education Student’s Perspective. International Journal of Educational Research Open, 5(October), 100296. https://doi.org/10.1016/j.ijedro.2023.100296
Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Computers and Education: Artificial Intelligence, 6, 100199. https://doi.org/10.1016/j.caeai.2023.100199
Mizumoto, A., Shintani, N., Sasaki, M., & Teng, M. F. (2024). Testing the viability of ChatGPT as a companion in L2 writing accuracy assessment. Research Methods in Applied Linguistics, 3(2), 100116. https://doi.org/10.1016/j.rmal.2024.100116
Munn, L., & Henrickson, L. (2024). Tell me a story: A framework for critically investigating AI language models. Learning, Media and Technology, 1–17. https://doi.org/10.1080/17439884.2024.2327024
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
Pack, A., & Maloney, J. (2024). Using artificial intelligence in TESOL: Some ethical and pedagogical considerations. TESOL Quarterly, 58(2), 1007–1018. https://doi.org/10.1002/tesq.3320
Pavlova, A., Gerazov, B., & Barreiro, A. (2024). Large language models and OpenLogos: An educational case scenario. Open Research Europe, 4, 110. https://doi.org/10.12688/openreseurope.17605.1
Pleasants, J., Gui, X., Krutka, D. G., Logan, C., & Heath, M. K. (2024). Coming to critical technology consciousness: A phenomenological study of educators. Learning, Media and Technology, 1–14. https://doi.org/10.1080/17439884.2024.2438925
Praphan, P. W., & Praphan, K. (2023). AI technologies in the ESL/EFL writing classroom: The villain or the champion? Journal of Second Language Writing, 62, 101072. https://doi.org/10.1016/j.jslw.2023.101072
Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), 11524. https://doi.org/10.3390/su151511524
Seo, K., Yoo, M., Dodson, S., & Jin, S. H. (2024). Augmented teachers: K–12 teachers’ needs for artificial intelligence’s complementary role in personalized learning. Journal of Research on Technology in Education, 0(0), 1–18. https://doi.org/10.1080/15391523.2024.2330525
Tariq, U. (2025). Nexus of essay writing and computer-assisted language learning (CALL) in English language classroom. Interactive Technology and Smart Education, 22(1), 103–133. https://doi.org/10.1108/ITSE-12-2023-0246
Triberti, S., Di Fuccio, R., Scuotto, C., Marsico, E., & Limone, P. (2024). “Better than my professor?” How to develop artificial intelligence tools for higher education. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1329605
Tsai, M. L., Ong, C. W., & Chen, C. L. (2023). Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT. Education for Chemical Engineers, 44(April), 71–95. https://doi.org/10.1016/j.ece.2023.05.001
Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1), 15. https://doi.org/10.1186/s41239-024-00448-3
Watanabe, A. (2024). Have courage to use your own mind, with or without AI: The relevance of Kant’s enlightenment to higher education in the age of artificial intelligence. Electronic Journal of E-Learning, 00–00. https://doi.org/10.34190/ejel.21.5.3229
Williyan, A., Fitriati, S. W., Pratama, H., & Sakhiyya, Z. (2024). AI as co-creator: Exploring Indonesian EFL teachers’ collaboration with AI in content development. Teaching English With Technology, 24(2), 5–21. https://doi.org/10.56297/vaca6841/LRDX3699/RZOH5366
Xu, J., & Li, J. (2024). Effects of AI affordances on student engagement in EFL classrooms: A structural equation modelling and latent profile analysis. European Journal of Education, 59(4). https://doi.org/10.1111/ejed.12808
Yavuz, F., Çelik, Ö., & Yavaş Çelik, G. (2025). Utilizing large language models for EFL essay grading: An examination of reliability and validity in rubric‐based assessments. British Journal of Educational Technology, 56(1), 150–166. https://doi.org/10.1111/bjet.13494
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Aldha Williyan, Muhammad Aulia Taufiqi, Irma Ratna Ningsih, Muhammad Ilyas, Eka Pujiastuti
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Notice
Authors who publish with this journal agree to the following terms:
In order to assure the highest standards for published articles, a peer review policy is applied. In pursue of the compliance with academic standards, all parties involved in the publishing process (the authors, the editors and the editorial board and the reviewers) agree to meet the responsibilities stated below in accordance to the Journal publication ethics and malpractice statement.
Duties of Authors:
- The author(s) warrant that the submitted article is an original work, which has not been previously published, and that they have obtained an agreement from any co-author(s) prior to the manuscript’s submission;
- The author(s) should not submit articles describing essentially the same research to more than one journal;
- The authors(s) make certain that the manuscript meets the terms of the Manuscript Submission Guideline regarding appropriate academic citation and that no copyright infringement occurs;
- The authors(s) should inform the editors about any conflict of interests and report any errors they subsequently, discover in their manuscript.
Duties of Editors and the Editorial Board:
- The editors, together with the editorial board, are responsible for deciding upon the publication or rejection of the submitted manuscripts based only on their originality, significance, and relevance to the domains of the journal;
- The editors evaluate the manuscripts compliance with academic criteria, the domains of the journal and the guidelines;
- The editors must at all times respect the confidentiality of any information pertaining to the submitted manuscripts;
- The editors assign the review of each manuscript to two reviewers chosen according to their domains of expertise. The editors must take into account any conflict of interest reported by the authors and the reviewers.
- The editors must ensure that the comments and recommendations of the reviewers are sent to the author(s) in due time and that the manuscripts are returned to the editors, who take the final decision to publish them or not.
Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
