Holistic Approach to Artificial Intelligence in Arabic Language Learning: Skill Effectiveness, Implementation Models, and Conceptual Challenges through Systematic Literature Review

Abstract

This study aims to systematically synthesize the findings of previous research on the use of Artificial Intelligence (AI) in Arabic language learning, focusing on the effectiveness of improving writing, speaking, reading, and listening skills, as well as implementation trends and accompanying conceptual challenges. This study uses the Systematic Literature Review (SLR) approach with the stages of identification, screening, feasibility assessment, and inclusion of studies based on the PRISMA flow. A literature search was conducted through the Scopus database using keywords related to AI and Arabic language learning, which resulted in seven articles that met the inclusion criteria for in-depth analysis. The results of the synthesis show that AI is most effectively used on writing and listening skills through formative feedback and audio-based learning media, while on reading skills AI plays a major role in readability analysis and cognitive load mapping. Conversely, speaking skills still have relatively little empirical support despite having great development potential. The implementation of AI in Arabic language learning is carried out through various approaches, including Generative AI, NLP systems, and adaptive learning, but it is still generally partial and not holistically integrated. The study concludes that AI has a significant contribution to Arabic language learning, but requires further development that is integrated, sensitive to linguistic and cultural contexts, and supported by a clear pedagogical framework.