Exploring the Application of Artificial Intelligence in Arabic Language Learning: Perspectives of Students and Teachers - Reziq Ridho; Muhammad Rozin Perkasa; Fathul Maujud (Universitas Muhammadiyah Sidoarjo, 2025)
General Background: The integration of Artificial Intelligence (AI) in education offers new opportunities for personalized and adaptive learning, yet its application in Arabic language education remains underexplored. Specific Background: While AI has been widely studied for English and other major languages, research focusing on Arabic—particularly from user-experience perspectives—is still limited. Knowledge Gap: Few studies qualitatively examine how students and teachers perceive AI’s role, challenges, and pedagogical relevance in Arabic language learning, especially in regions with unequal digital infrastructure such as Lombok. Aims: This study investigates perceptions of students and teachers regarding the implementation of AI in Arabic language learning and identifies key factors influencing its success or limitations. Results: Findings reveal that AI enhances learning interactivity, flexibility, and basic language skills; however, cultural-contextual understanding still relies heavily on teacher interaction. Implementation challenges include insufficient digital infrastructure, limited training, and varied student digital readiness. Novelty: This study offers a holistic qualitative analysis integrating pedagogical theory, technological readiness, and socio-cultural factors in the use of AI for Arabic learning. Implications: The results highlight the need for stronger infrastructure investment, continuous teacher training, and inclusive digital literacy programs to optimize AI-supported Arabic language education. Highlights: AI improves interactivity and flexibility but lacks cultural-context depth. Training and digital literacy strongly influence successful implementation. Infrastructure gaps limit equitable access to AI-based Arabic learning. Keywords: Artificial Intelligence, Arabic Language Learning, Student–Teacher Perceptions, Digital Readiness, Educational Technology