Readiness of Arabic Language Teachers to Integrate Large Language Models (LLMs) in their Teaching Practices: Challenges and Opportunities - Harmanto Raharjo; Naih Nurjanah; Ghufran Akbar; Mulyadi Mulyadi; Uus Ruswandi; Mohamad Erihadiana; Aulia Rahmi (Arabiyatuna: Jurnal Bahasa Arab, 2026)
This study examines Arabic language teachers’ readiness to integrate Large Language Models (LLMs) into instructional practices, identifies key challenges, and explores strategic opportunities for pedagogical use. A qualitative library research design was applied using descriptive-analytical and comparative methods based on peer-reviewed literature (2019–2025) on artificial intelligence, language education, and teacher readiness. Findings indicate that teacher readiness remains at an early, uneven stage, shaped by a socio-technical configuration comprising digital literacy, pedagogical competence, psychological disposition, and institutional support. From a TPACK perspective, technological knowledge is less developed than pedagogical and content knowledge, limiting effective integration of AI in Arabic language learning. Psychological barriers such as technological anxiety and low self-efficacy, along with unequal infrastructure and policy support, further constrain adoption. However, gradual exposure to AI tools fosters experiential learning and incremental development of readiness. Despite challenges, LLMs offer opportunities for improved learning effectiveness, personalized instruction, the development of higher-order thinking, and the integration of Islamic educational values. The study conceptualizes teacher readiness as a socio-technical and developmental construct. It highlights the need for AI literacy development, curriculum redesign, and institutional policy support to ensure ethical and sustainable integration of LLMs in Arabic language education.