The rapid development of artificial intelligence (AI) and machine learning has introduced significant changes in various fields, including the Arabic language education sector. This study focuses on exploring the challenges and applications of AI in Arabic language learning, particularly in the context of machine learning technologies. The primary problem addressed in this research is the difficulty in developing effective machine learning models that accurately understand and process the Arabic language, given its complexity, diverse dialects, and intricate grammar rules. The main objective of this research is to analyze the current applications of AI in Arabic language learning, assess the challenges associated with these technologies, and identify potential improvements. The study employs a qualitative approach, reviewing existing literature, case studies, and practical applications of AI in Arabic language education. The results indicate that AI technologies such as natural language processing, automated translation, and speech recognition have significantly contributed to enhancing Arabic language learning tools. However, the study also reveals challenges related to the complexity of Arabic grammar, regional dialects, and the lack of sufficient digital resources and standardized linguistic data. This research contributes to the field by providing insights into the integration of AI in Arabic language education, highlighting both the advancements and the obstacles that need to be overcome. The study suggests that with continued investment in AI technologies and collaboration between linguistic experts and tech developers, the future of Arabic language learning can be greatly enhanced, offering more efficient and personalized learning experiences for students
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