Arabic for AI Purposes: Promoting AI as a Cognitive Partner in Arabic Text Analysis
Keywords:
Artificial Intelligence; Cognitive Partner; Arabic Text Analysis; Nahwu Learning; Arabic Language Education.Abstract
The rapid development of Artificial Intelligence (AI) has created new opportunities for Arabic language education, particularly in supporting Arabic text analysis based on syntactic principles (nahwu). However, the increasing use of AI also raises pedagogical concerns regarding learners’ dependence on automated analysis and the potential decline of independent grammatical reasoning. This study aims to formulate an implementation model of AI as a cognitive partner in Arabic text analysis learning. The research employed a qualitative case study design using task-based analysis involving manual grammatical analysis, AI-assisted analysis, and interactional data between learners and AI. Data were analyzed through thematic analysis guided by the Cognitive Partnership Theory framework. The findings reveal that AI functions not merely as an automated analytical tool but as a cognitive partner that facilitates learners’ grammatical reasoning through interaction, comparison, reflection, and conceptual reconstruction. The study proposes a five-stage implementation model consisting of input provision, human initial analysis, AI-assisted analysis, cognitive comparison and evaluation, and cognitive revision. This model demonstrates that effective AI integration should prioritize learners’ independent grammatical reasoning before introducing AI-supported analysis, thereby promoting reflective learning rather than passive reliance on automated outputs. The study contributes to Arabic language education by extending AI utilization from a technology-oriented perspective to a pedagogically grounded implementation framework that supports cognitive engagement, analytical autonomy, and sustainable grammatical competence in Arabic syntax learning
References
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. National Academy Press.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Halabi, N. (2020). Arabic dependency treebank grounded in traditional Arabic grammar. Journal of King Saud University - Computer and Information Sciences, 32(9), 1004–1015.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Holmes, W., Tuomi, I., & Cukurova, M. (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
Ismail, A., Alghamdi, M., & Alharbi, A. (2025). Transformer-based Arabic grammatical error correction using large language models. Expert Systems with Applications.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Kasneci, G., et al. (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
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL Institute of Education Press.
Nashir, M., Alotaibi, S., & Alghamdi, F. (2025). Noor: A hybrid NLP framework for Classical Arabic language processing. Natural Language Engineering.
Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed Cognitions: Psychological and Educational Considerations (pp. 47–87). Cambridge University Press.
Saadiyeh, M., Al-Khalifa, H., & Alrashed, M. (2025). Evaluating syntactic parsers for Arabic language processing: Challenges and future directions. Journal of King Saud University - Computer and Information Sciences.
Salomon, G. (Ed.). (1993). Distributed Cognitions: Psychological and Educational Considerations. Cambridge University Press.
Stake, R. E. (1995). The Art of Case Study Research. Sage Publications.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using AI in higher education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x
UNESCO. (2023). Guidance for Generative AI in Education and Research. UNESCO.
Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.
Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ana Durrotul Hikmah (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Tanfidziya is licensed under a Creative Commons Attribution 4.0 International License
.
