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Advances in Computer and Communication

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ArticleOpen Access http://dx.doi.org/10.26855/acc.2025.07.008

The Function of Deep Learning in Intelligent Support for Second Language Learners

Yuefei Duan1, Yajing Zhang1, Yuxuan Li2, Canli Zhang1,*

1Luoyang Institute of Science and Technology, Luoyang 471023, Henan, China.

2The university of Manchester, Manchester M13 9PL, UK.

*Corresponding author: Canli Zhang

Published: August 21,2025

Abstract

Artificial Intelligence (AI) is transforming Second Language Acquisition (SLA) by enabling tailored, effective learning experiences. Technologies like adaptive learning systems, intelligent tutoring systems, and NLP tools provide customized instruction and immediate feedback, moving beyond conventional methods. Within deep learning frameworks, AI can analyze student behaviors, systematically gather data, and help educators understand learner performance, leading to more informed teaching decisions and enhanced efficiency. Traditional language education, heavily reliant on teachers and textbooks, often places the explanatory burden on instructors. This can limit learners' practical language application opportunities, potentially creating passive environments that reduce participation, initiative, and motivation. Conversely, AI-driven personalized and adaptive models efficiently address individual learner needs, accommodating distinct styles and paces. This provides significant SLA support, markedly improving outcomes. Furthermore, integrating AI with gamification in blended learning settings enhances student motivation and engagement, thereby leading to improved outcomes in SLA.

Keywords

Deep learning; SLA; Intelligent tutoring systems

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How to cite this paper

The Function of Deep Learning in Intelligent Support for Second Language Learners

How to cite this paper: Yuefei Duan, Yajing Zhang, Yuxuan Li, Canli Zhang. (2025) The Function of Deep Learning in Intelligent Support for Second Language Learners. Advances in Computer and Communication6(3), 151-156.

DOI: http://dx.doi.org/10.26855/acc.2025.07.008