<![CDATA[Conventional assessment of writing skills (Maharah Kitabah) often faces challenges regarding high levels of subjectivity, poor time efficiency, and delays in providing feedback to students. This study aims to analyze the application of Natural Language Processing (NLP) technology in detecting syntactic, semantic, and morphological aspects of students' Arabic compositions, while also mapping potential uses and technical obstacles. The study employs a library research method, gathering, reviewing, and analyzing various scholarly works and relevant references concerning the integration of Artificial Intelligence (AI) and NLP into Arabic language learning assessment. The findings indicate that NLP integration via automated assessment systems such as Automated Essay Scoring (AES) and the practical application Al-Qalam AI can provide real-time feedback, enhance personalization and learning efficiency, and maintain consistent assessment objectivity. However, implementation still faces technical challenges, including the morphological complexity of Arabic, the ambiguity of unvowelized text, limited corpora specific to non-native learners, and the digital competency levels of educators. Therefore, this study recommends a hybrid assessment approach that combines the strengths of NLP technology with manual teacher assessment regarding contextual and aesthetic aspects]]>
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