The Application of Deep Learning in the Context of English as A Foreign Language (EFL) Learning
Keywords: deep learning, English as a Foreign Language, computer-assisted language learning, language pedagogy
Abstract
The integration of deep learning technologies in English as a Foreign Language (EFL) education has shown great potential to transform language instruction through personalization and adaptability. This systematic literature review analyzes 47 empirical studies published between 2015 and 2024, focusing on the effectiveness, implementation challenges, and future research directions of deep learning in EFL contexts. Five key application areas emerged: intelligent tutoring systems, automated assessment tools, personalized learning environments, natural language processing applications, and multimodal learning tools. These applications demonstrated significant improvements in vocabulary retention, reading comprehension, pronunciation accuracy, and grammar acquisition. However, challenges were identified across technical (e.g., data requirements, system integration), pedagogical (e.g., teacher training, curriculum alignment), and contextual dimensions (e.g., cultural relevance, data privacy, equity). Emerging trends include explainable AI, cross-linguistic adaptation, and integration with learning sciences. The review emphasizes the need to move beyond techno-centric approaches and instead adopt pedagogically grounded, culturally responsive, and ethically aware frameworks. Deep learning should be viewed as a collaborative tool to enhance, not replace, teacher expertise. Successful implementation requires institutional support, teacher algorithmic literacy, and policies that promote equitable access.
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