INTEGRATING DEEP LEARNING THEORIES IN MODERN ENGLISH LANGUAGE TEACHING.

Abstract

The rapid advancement of deep learning has significantly influenced various educational domains, including English Language Teaching (ELT). This study explores the integration of deep learning theories into modern ELT practices, emphasizing their potential to enhance language acquisition, personalization, and instructional effectiveness. Deep learning models, such as neural networks and natural language processing systems, enable adaptive learning environments that respond to learners’ individual needs, proficiency levels, and learning styles. By analyzing recent theoretical frameworks and practical applications, this paper highlights how deep learning supports automated feedback, intelligent tutoring systems, speech recognition, and data-driven assessment in English language classrooms. Furthermore, the study discusses the pedagogical implications of integrating deep learning with communicative and learner-centered approaches, as well as the challenges related to ethics, data privacy, and teacher readiness. The findings suggest that when aligned with sound pedagogical principles, deep learning technologies can transform traditional ELT into a more interactive, efficient, and inclusive learning experience. This research contributes to a deeper understanding of how artificial intelligence–driven theories can be meaningfully applied to support teachers and learners in the evolving landscape of English language education.