AI-Driven Arabic Grammatical Error Correction for Inshā’ Writing: A Systematic Review of Current Models and Pedagogical Directions
DOI:
https://doi.org/10.58524/oler.v6i2.1116Keywords:
Arabic Grammar Error Correction, Artificial Intelligence, Arabic Writing, Inshā’ Learning, Systematic Literature ReviewAbstract
Artificial intelligence (AI) has expanded opportunities for supporting Arabic writing through automated grammatical error correction (GEC). However, existing research remains fragmented, with limited synthesis connecting learner error patterns, AI-based correction models, and their pedagogical implications for Inshā’ instruction. This study systematically reviews AI-based Arabic GEC research to identify dominant nahwu–ṣarf error patterns, evaluate the evolution of AI correction models, and examine their educational implications for non-native Arabic learners. Following the PRISMA framework, eleven studies published between 2020 and 2025 were synthesized using qualitative thematic analysis and VOSviewer-assisted keyword mapping. The review identifies recurrent learner difficulties in agreement, definiteness, morphological ambiguity, syntactic dependencies, orthographic variation, and diacritical accuracy. Arabic GEC has progressed from rule-based and hybrid approaches to Transformer- and large language model (LLM)-based systems that provide more context-sensitive corrections. Nevertheless, current models remain constrained by challenges related to i‘rāb, learner-specific error profiles, explainable grammatical feedback, and limited annotated corpora. The review proposes a human-in-the-loop pedagogical framework integrating AI-generated feedback with teacher validation to foster formative assessment, learner autonomy, and reflective revision in Arabic writing instruction
References
Abdelaal, A., Medhat, A. A., Elsayad, M., Foad, M., Khaled, S., Tamer, A., & Medhat, W. (2024). Text correction for modern standard Arabic. Procedia Computer Science, 244, 371–377. https://doi.org/10.1016/j.procs.2024.10.211
Alhafni, B., Inoue, G., Khairallah, C., & Habash, N. (2023). Advancements in Arabic grammatical error detection and correction : An empirical investigation. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 6430–6448. https://doi.org/10.18653/v1/2023.emnlp-main.396
Alrehili, A., & Alhothali, A. (2025). Tibyan corpus: Balanced and comprehensive error coverage corpus using ChatGPT for Arabic grammatical error correction. PeerJ Computer Science, 11, e2724. https://doi.org/10.7717/peerj-cs.2724
Althafir, Z., & Ghnemat, R. (2022). A Hybrid approach for auto-correcting grammatical errors generated by non-native Arabic speakers. 2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA), 1–6. https://doi.org/10.1109/ETCEA57049.2022.10009874
Antoun, W., Baly, F., & Hajj, H. (2020). AraBERT : Transformer-based model for Arabic language understanding. Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, May, 9–15.
Antoun, W., Baly, F., & Hajj, H. (2021). AraELECTRA: Pre-training text discriminators for Arabic language understanding. Proceedings of the Sixth Arabic Natural Language Processing Workshop, 191–195.
Belkebir, R., & Habash, N. (2021). Automatic error type annotation for Arabic. Proceedings of the 25th Conference on Computational Natural Language Learning, 596–606. https://doi.org/10.18653/v1/2021.conll-1.47
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cano, P. M. A., Catiggay, J. M. L., Florida, J. M. M., Pua, J. M. R., & Samonte, M. J. C. (2024). Natural language processing of grammar checker tools for academic writing : A systematic literature review. Journal Electrical Systems, 20(7), 1151–1156. https://doi.org/10.52783/jes.3611
Corder, S. P. (1967). The significance of learner’s errors. International Review of Applied Linguistics in Language Teaching, 5(1–4), 161–170. https://doi.org/doi:10.1515/iral.1967.5.1-4.161
Ellis, R. (2008). The study of second language acquisition. Oxford University Press.
ElSabagh, A. A., Azab, S. S., & Hefny, H. A. (2025). A comprehensive survey on Arabic text augmentation: Approaches, challenges, and applications. Neural Computing and Applications, 37(10), 7015–7048. https://doi.org/10.1007/s00521-025-11020-z
Essa, N., El-Gayar, M., & El-Daydamony, E. (2025). Arabic grammar correction for Arabic text summaries. Mansoura Journal for Computer and Information Sciences, 20. https://doi.org/10.21608/mjcis.2025.353920.1009
Ferris, D. (2011). Treatment of error in second language student writing. Bibliovault OAI Repository, the University of Chicago Press. https://doi.org/10.3998/mpub.2173290
Fitria, T. N. (2021). “ Grammarly ” as AI -powered English writing assistant : Students ’ alternative for English writing. Metathesis: Journal of English Language, Literature, and Teaching, 5(1), 65–78. https://doi.org/10.31002/metathesis.v5i1.3519
Hady, Y., Azkiyah, S. N., Muhbib, M., & Maswani, M. (2026). Artificial intelligence-based Arabic language learning: A systematic study of the development and challenges of pedagogical innovation. Online Learning in Educational Research (OLER), 6(1), 49–63. https://doi.org/10.58524/oler.v6i1.976
Hanandeh, A., Ayasrah, S., Kofahi, I., & Qudah, S. (2024). Artificial Intelligence in Arabic linguistic landscape : Opportunities , challenges , and future directions. TEM Journal., 13(4), 3137–3145. https://doi.org/10.18421/TEM134-48
Hyland, K., & Hyland, F. (2006). Feedback on second language students’ writing. Language Teaching, 39(2), 83–101. https://doi.org/10.1017/S0261444806003399
Karatay, Y., & Karatay, L. (2024). Automated writing evaluation use in second language classrooms: A research synthesis. System, 123, 103332. https://doi.org/10.1016/j.system.2024.103332
Kitchenham, B. (2004). Procedures for performing systematic reviews. In Keele, UK, Keele Univ. (Vol. 33).
Kwon, S. Y., Bhatia, G., Nagoud, E. M. B., & Abdul-Mageed, M. (2022). ChatGPT for Arabic grammatical error correction. ArchivePrefix, 23(8).
Madi, N., & Al-Khalifa, H. (2020). Error detection for Arabic text using neural sequence labeling. In Applied Sciences (Vol. 10, Issue 15, p. 5279). https://doi.org/10.3390/app10155279
Mansoor, K. R., Mousavi, K., & Shokri, A. (2025). Investigating the effects of AI and teacher-based explicit correction on learner autonomy and grammatical accuracy. Forum for Linguistic Studies, 07(08), 498–510. https://doi.org/10.30564/fls.v7i8.9643
Moatez, E., Nagoudi, B., & Abdul-mageed, M. (2022). AraT5 : Text-to-text transformers for Arabic language generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1, 628–647. https://doi.org/10.18653/v1/2022.acl-long.47
Musyafa, A., Gao, Y., Solyman, A., Khan, S., Cai, W., & Khan, M. F. (2024). Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario. PeerJ Computer Science, 10, e2122. https://doi.org/10.7717/peerj-cs.2122
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(1), 71. https://doi.org/10.1136/bmj.n71
Putri, R. K. A., Kusaeri, K., & Suparto, S. (2025). Deep learning in automated essay scoring for Islamic education: A systematic review. Online Learning in Educational Research (OLER), 5(2), 319–338. https://doi.org/10.58524/oler.v5i2.753
Rusmansyah, Syahmani, Erika, F., Kusuma, A. E., & TienTien, L. (2025). Integrating AR–AI–STEAM for 6C skill development in wetland learning: Trends and knowledge mapping from 2019–2025. Online Learning in Educational Research, 5(2), 457–477. https://doi.org/10.58524/oler.v5i2.945
Shaalan, K. F. (2005). Arabic gramcheck: A grammar checker for Arabic. Software: Practice and Experience, 35(7), 643–665. https://doi.org/10.1002/spe.653
Solyman, A., Zhenyu, W., Qian, T., Elhag, A. A. M., Toseef, M., & Aleibeid, Z. (2021). Synthetic data with neural machine translation for automatic correction in arabic grammar. Egyptian Informatics Journal, 22(3), 303–315. https://doi.org/10.1016/j.eij.2020.12.001
Syafe’i, I., Nada, N., Fauziah, P., & Azizah, Z. (2022). Tahlīl al-akhtā’ al-sharfiyyah wa al-nahwiyya fī al-kitāb al-‘arabiyyah li dars al-insyā. Tadris Al- ‘ Arabiyyah, 1(1), 54–73. https://doi.org/10.15575/ta.v1i1.17383
Tang, L., & Mahmoud, Q. H. (2021). A survey of machine learning-based solutions for phishing website detection. Machine Learning and Knowledge Extraction, 3(3), 672–694. https://doi.org/10.3390/make3030034
Weng, X., & Chiu, T. K. F. (2023). Instructional design and learning outcomes of intelligent computer assisted language learning: Systematic review in the field. Computers and Education: Artificial Intelligence, 4, 100117. https://doi.org/10.1016/j.caeai.2022.100117
Xiao, Yu, & Watson, Maria. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971
Zainuddin, Z., Misbah, M., Mastuang, M., Qamariah, Q., Zaidi, M., Amiruddin, B., Farahwahidah, N., & Rahman, A. (2025). Mapping a decade of research on artificial intelligence and augmented reality in physics education: A bibliometric analysis (2016–2025). Online Learning in Educational Research, 5(2), 537–557. https://doi.org/10.58524/oler.v5i2.901
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Abdurahman Abdurahman, Siti Nurul Azkiyah, Muhbib Abdul Wahab, Maswani Maswani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Online Learning in Educational Research is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with Online Learning in Educational Research agree to the following terms:
Copyright Retention: Authors retain the copyright of their work without any restrictions.
Publishing Rights: Authors retain the right to publish and distribute their work without any restrictions.
License Agreement: By publishing with Online Learning in Educational Research, authors agree that their work will be licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA). This license allows others to share and adapt the work, provided that appropriate credit is given, any changes are indicated, and the new creations are licensed under the same terms.
