<![CDATA[Despite the increasing integration of Artificial Intelligence (AI) in education, current research on its application in foreign language education (FLE), particularly in Middle Eastern English as a Foreign Language (EFL) contexts, reveals significant gaps in theoretical understanding, pedagogical implementation, and teacher preparedness. This study aims to develop a comprehensive framework for integrating AI into EFL instruction, informed by the principles of Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL). Employing a qualitative-descriptive research design, the study synthesizes insights from existing literature, expert consultations with EFL educators, and analyses of AI tools currently implemented in classrooms, including intelligent tutoring systems, conversational chatbots, automated assessment platforms, and VR/AR simulations. The framework evaluates learner-, teacher-, and system-facing affordances, examining their potential to foster learner autonomy, communicative competence, motivation, and self-directed learning, while also identifying constraints such as technological accessibility, cultural responsiveness, and teacher readiness. Findings indicate that AI tools can enhance personalized and adaptive language learning, yet their effectiveness is contingent upon structured pedagogical integration, continuous teacher training, and ethical, context-sensitive deployment. The study highlights critical research directions, including longitudinal investigations, evaluation of deep learning applications, and the development of contextually grounded AI-enhanced FLE models. This framework provides an evidence-based roadmap for sustainable, culturally responsive, and learner-centered AI integration in EFL education in the Middle East.]]>
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