Arabic Natural Language Processing in the Era of Generative Artificial Intelligence: Opportunities, Challenges, and Future Directions for Sustainable Digital Education

Abstract

This study explores the role of Artificial Intelligence (AI) and Natural Language Processing (NLP) in advancing Arabic language education and strengthening its digital presence. Using descriptive and analytical approaches, it examines the evolution of Arabic NLP technologies and reviews major AI-driven systems developed for Arabic, including CAMeLira, SAMER, and Fanar. The study also highlights the main linguistic and technical challenges facing Arabic, particularly those related to morphology, diacritization, dialectal variation, and the limited availability of high-quality digital resources. The findings indicate that AI offers significant opportunities to improve machine translation, text analysis, speech recognition, and intelligent language learning, thereby enhancing Arabic digital content and supporting its long-term sustainability. However, achieving these goals requires the development of large annotated Arabic corpora, computational models that capture the linguistic and cultural characteristics of Arabic, and stronger interdisciplinary collaboration between linguists and computer scientists to promote sustainable digital education.