Rekonstruksi Teori Nazm Al-Jurjani dalam Natural Language Processing Sebagai Model Asesmen Otomatis Kitabah Arab

Muhammad Zaidar

doi:10.59996/cendib.v4i1.1269

Authors

Muhammad Zaidar Institut Agama Islam Negeri Pontianak

DOI:

https://doi.org/10.59996/cendib.v4i1.1269

Keywords:

Nazm Theory, Natural Language Processing, Automated Assessment, Kitabah Assessment, Educational Technology

Abstract

Al-Jurjānī’s Naẓm theory, as presented in his work Dalā’il al-I‘jāz, can serve as a conceptual foundation for artificial intelligence used in assessing Arabic writing skills (Kitābah). This theory can be adapted because modern Natural Language Processing (NLP) models developed in the Western tradition tend to be rigid (low-context). These models are only capable of correcting surface-level spelling without grasping the depth of meaning, literary beauty, and the real-world situations in which Arabic is used. Through a literature review method, this research identifies three main principles of Naẓm theory that align closely with contemporary computational technologies: al-Tabā‘iyyah, which corresponds to dependency parsing systems; Muqtaḍā al-Ḥāl, which aligns with dynamic contextual embeddings; and ma‘nā al-ma‘nā, which can be applied to detect figurative stylistic devices through semantic role labeling. The integration of these three principles makes it possible to create an automated essay scoring system that is humane, fair, and objective. This research demonstrates that classical Islamic linguistics possesses sophisticated logic capable of complementing the limitations of today’s modern technology.

References

Abd al-Qahir al-Jurjani. (1992). Kitab Dala’il al-I’jaz oleh (Mahmud Muhammad Syakir (ed.)). Dar al-Madani (Jeddah/Kairo).

Aksoy, M. (2025). Whose morality do they speak ? Unraveling cultural bias in multilingual language models. 12(June).

Akyüz, Y. (2023). Arap Dilinde Muktezây-ı Hâl ve Önvarsayım İlişkisi The Relationship Between Presupposition and Muqtada al-Hal in Arabic Language. https://doi.org/10.5152/ilted.2023.23313

Al-dadi, M. A. (2025). The Linguistic Meaning Between Traditional Rhetorical Frameworks and Artificial Intelligence Models : A Post humanist Critique of Computational Semantics. 3576, 1080–1087.

Al-Mubassyir, M., Naf’an, A., & Ulum, B. (2024). Teori Naẓm Perspektif Abdul Qahir al-Jurjani: Posisinya dalam Keilmuan Bahasa Arab dan Spesifikasinya di Antara Teori-Teori Sebelumnya. Uktub: Journal of Arabic Studies, 4, 54–72. https://doi.org/10.32678/uktub.v4i1.11081

Al-Thubaity, A., Khan, M., Al-Mazrua, M., & Al-Mousa, M. (2013). New Language Resources for Arabic: Corpus Containing More Than Two Million Words and a Corpus Processing Tool. 2013 International Conference on Asian Language Processing, 67–70. https://doi.org/10.1109/IALP.2013.21

Al Sallab, A., Hajj, H., Badaro, G., Baly, R., El Hajj, W., & Bashir Shaban, K. (2015). Deep Learning Models for Sentiment Analysis in {A}rabic. In N. Habash, S. Vogel, & K. Darwish (Eds.), Proceedings of the Second Workshop on {A}rabic Natural Language Processing (pp. 9–17). Association for Computational Linguistics. https://doi.org/10.18653/v1/W15-3202

Alayba, A. M. (2025). Arabic Natural Language Processing ( NLP ): A Comprehensive Review of Challenges , Techniques , and Emerging Trends.

Alqahtani, A. (2020). Automated Arabic Essay Evaluation. 181–190.

Azmi, A. M., Al-Qabbany, A. O., & Hussain, A. (2019). Computational and natural language processing based studies of hadith literature: a survey. Artificial Intelligence Review, 52(2), 1369–1414. https://doi.org/10.1007/s10462-019-09692-w

Bessghaier, M., Biswas, R., Dhouib, A., & Zaghouani, W. (2025). MarsadLab at TAQEEM 2025 : Prompt-Aware Lexicon-Enhanced Transformer for Arabic Automated Essay Scoring. 998–1002.

Darwish, K., Habash, N., Abbas, M., Al-khalifa, H., Al-natsheh, H. T., El-beltagy, S. R., Bouamor, H., Bouzoubaa, K., Cavalli-sforza, V., & El-hajj, W. (2020). arXiv : 2011 . 12631v3 [ cs . CL ] 27 Sep 2021 A Panoramic Survey of Natural Language Processing in the Arab World. 1–34.

Elsayed, Y., Nabil, E., Torki, M., Faizullah, S., & Khalafallah, A. (2025). ZaQQ: A New Arabic Dataset for Automatic Essay Scoring via a Novel Human–AI Collaborative Framework. Data, 10(9). https://doi.org/10.3390/data10090148

Eronen, J., Ptaszynski, M., Masui, F., Arata, M., Leliwa, G., & Wroczynski, M. (2022). Transfer language selection for zero-shot cross-lingual abusive language detection. Information Processing & Management, 59(4), 102981. https://doi.org/https://doi.org/10.1016/j.ipm.2022.102981

Farro, M. (2025). “The Influence of Psychological Triggers on Consumer Decision-making in the Indian Market.” Examining Psychologial Triggers like FOMO, Nudging, Social Proof, Loss Aversion and the influence of Digital Marketing in Consumer Decision making in Urban cities like Mumbai, Thane and Pune. https://doi.org/10.13140/RG.2.2.31236.85125

Ghazawi, R. N. (n.d.). This electronic thesis or dissertation has been downloaded from the University of Bristol Research Automatic Essay Scoring in Arabic : Department of Computer Science.

Guellil, I., Saâdane, H., Azouaou, F., Gueni, B., & Nouvel, D. (2019). Arabic natural language processing : An overview. Journal of King Saud University - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2019.02.006

Husaini, B., Ulhaq, A., & Mugindari, S. (2025). Al Mi ’ yar : Jurnal Ilmiah Pembelajaran Bahasa Arab dan Kebahasaaraban Inovasi Instrumen Penilaian Proyek Berbasis Teori Transaction Terhadap Mah ā rah Kit ā bah Pada Buku Ajar Bahasa Arab. 8(2), 680–687.

Hussain, J., Mediani, M., Behr, M., Cheragui, M. A., Stüker, S., & Waibel, A. (2020). {G}erman-{A}rabic Speech-to-Speech Translation for Psychiatric Diagnosis. In I. Zitouni, M. Abdul-Mageed, H. Bouamor, F. Bougares, M. El-Haj, N. Tomeh, & W. Zaghouani (Eds.), Proceedings of the Fifth Arabic Natural Language Processing Workshop (pp. 1–11). Association for Computational Linguistics. https://aclanthology.org/2020.wanlp-1.1/

Meccawy, M., Bayazed, A. A., Al-abdullah, B., & Algamdi, H. (2023). Automatic Essay Scoring for Arabic Short Answer Questions using Text Mining Techniques. 14(6), 768–775.

Mohammed, A. (2026). The Impact of Context on Meaning Construction in Al-Jurjani ’ s Thought in Light of Pragmatic Linguistics. 771–786.

Munawarah, M., & Zulkiflih, Z. (2021). Pembelajaran Keterampilan Menulis (Maharah al-Kitabah) dalam Bahasa Arab. Loghat Arabi : Jurnal Bahasa Arab Dan Pendidikan Bahasa Arab, 1, 22. https://doi.org/10.36915/la.v1i2.15

Nizar Y. Habash. (2010). Introduction to Arabic Natural Language Processing.

Risch, J., Hager, P., & Krestel, R. (2021). Multifaceted Domain-Specific Document Embeddings. In A. Sil & X. V. Lin (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations (pp. 78–83). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-demos.9

Sallab, A. A. Al, Baly, R., & Hajj, H. (2015). Deep Learning Models for Sentiment Analysis in Arabic. 9–17.

Sayed, M., Eltanbouly, S., Bashendy, M., & Elsayed, T. (2025). Feature Engineering is not Dead : A Step Towards State of the Art for Arabic Automated Essay Scoring. 4, 231–245.

Schneijderberg, C., Wieczorek, O., & Steinhardt, I. (n.d.). THE HANDBOOK OF QUALITATIVE AND QUANTITATIVE CONTENT. Routledge.

Skrynnikova, I. V. (2024). INTERPRETING METAPHORICAL LANGUAGE : A CHALLENGE TO ARTIFICIAL INTELLIGENCE. 23(5).

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/https://doi.org/10.1016/j.jbusres.2019.07.039

Sultan, U. I. N., Hasanuddin, M., Al-mubassyir, M., Wahab, A., & Ulum, B. (2024). Uktub : Journal of Arabic Studies Teori Naẓm Perspektif Abdul Qahir al -Jurjani : Posisinya dalam Keilmuan Bahasa Arab dan Spesifikasinya di Antara Teori-Teori Sebelumnya. 4(1), 54–72.