Artificial Intelligence Applications in Teaching Arabic to Non-Native Speakers: Opportunities, Challenges, and Future Directions

This paper examines the applications of artificial intelligence in teaching Arabic to non-native speakers, focusing on educational opportunities, linguistic, pedagogical, and ethical challenges, and future directions. It is grounded in recent developments in generative artificial intelligence, natural language processing, adaptive learning systems, and conversational agents, all of which are reshaping language learning environments. The study adopts a descriptive-analytical review approach by synthesizing recent literature on AI in education, AI in language learning, and Arabic language learning for non-native speakers. The paper argues that AI offers significant potential for personalized learning, intensive language practice, immediate feedback, and the development of listening, speaking, reading, and writing skills. However, its effective use in Arabic language education depends on the quality of Arabic datasets, the ability of AI models to handle Arabic morphology, syntax, semantics, and dialectal variation, teachers’ readiness, institutional infrastructure, and clear ethical and academic policies. The paper recommends an integrative model in which AI supports rather than replaces teachers, while balancing technological innovation with pedagogical control and the linguistic-cultural specificity of Arabic.

Artificial intelligence; generative AI; teaching Arabic to non-native speakers; natural language processing; adaptive learning; language education.

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