Why AI Struggles to Understand Arabic Grammar: A Techno-Pedagogical Review of Computational and Linguistic Challenges - Muhammad Fariq Heemal Attruk; Nik Hanan Mustapha (EDUCATUM: Scientific Journal of Education, 2026)
Generative artificial intelligence is increasingly used in Arabic language education, yet its performance in understanding and processing Arabic grammar remains markedly inconsistent. This narrative review examines why AI systems struggle with Arabic grammar and explores the pedagogical implications of these computational limitations. Drawing on 26 primary studies published between 2020 and 2026, the review identifies five interconnected linguistic challenges: the non-linear root-and-pattern morphology of Arabic, the routine omission of diacritics that obscures grammatical case, extensive dialectal diversity, persistent data scarcity, and the syntactic complexity of the i'rab case system. Empirical evidence shows that even advanced models perform substantially worse on morphological and syntactic tasks than on surface-level tasks, with GPT-4o achieving only 67 percent accuracy on Arabic grammar benchmarks and Arabic-specific models scoring considerably lower. The review demonstrates that the structural features of Arabic that make natural language processing difficult are precisely the features that pose risks for learners who depend on AI without critical oversight. These risks include the formation of misconceptions, overreliance on AI-generated outputs, and the erosion of critical thinking and teacher expertise. The findings suggest that effective AI integration in Arabic grammar instruction requires a Human-in-the-Loop approach, targeted teacher training, and the development of critical AI literacy among learners