Computational Arabic Morphology and Its Pedagogical Applications in Arabic Language Instruction

Abstract

Natural Language Processing (NLP) has emerged as a cornerstone of artificial intelligence, enabling computational systems to process human language across diverse technological and educational domains. Owing to its complex morphology, high degree of inflection, and non-concatenative root-and-pattern system, Arabic presents distinct computational challenges. Although significant methodological advancements have been achieved through state-of-the-art frameworks—such as CAMeL Tools, Farasa, and MADAMIRA—their integration into Arabic language pedagogy remains insufficiently explored.

This descriptive-analytical study investigates the theoretical foundations of computational Arabic morphology and evaluates its practical pedagogical applications. The findings demonstrate that automated morphological analyzers significantly enhance language learning by demystifying structural word analysis, mitigating cognitive load, and providing real-time instructional feedback, while highlighting the necessity for advanced models to address persistent contextual and morphophonemic ambiguities.