<![CDATA[Traditional pedagogical methods in Arabic language instruction often suffer from a lack of interactivity and rigidity, failing to adapt to the individual linguistic needs of non-native speakers. This study aims to synthesize existing research on AI-driven pedagogical strategies to evaluate how these technologies enhance personalization, interactive engagement, and overall language acquisition effectiveness This review employs a systematic literature search across databases including Scopus, Google Scholar, and DOAJ, covering scholarly publications from 2016 to 2025 to identify and synthesize peer-reviewed interventions. The analysis framework utilizes AlAfnan’s Taxonomy of Educational Objectives to categorize AI impacts across cognitive, affective, and psychomotor domains. The synthesis reveals that AI integration significantly improves learner motivation and language proficiency through adaptive feedback and personalized learning platforms. Furthermore, these technologies facilitate the development of core linguistic competencies, including grammar acquisition and speaking practice, while simultaneously fostering character traits such as discipline and responsibility through technology-mediated interactions. Despite these advancements, the findings also underscore a critical reliance on human instructors for navigating the nuanced cultural-contextual complexities inherent in the Arabic language. The study underscores that while AI-driven tools provide essential support for morphological and semantic challenges, successful implementation requires a balanced integration of automated systems and human pedagogical guidance Keywords: Artificial Intelligence; Arabic Language Learning; Personalized Education; Educational Technology; Linguistic Competency.]]>
Copyrights © 2025
