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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 25, No. 0, pp.330-366
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Jan 2025
Received 04 Dec 2024 Revised 24 Jan 2025 Accepted 08 Mar 2025
DOI: https://doi.org/10.15738/kjell.25..202503.330

인공지능 디지털 영어교육의 현황과 전망: 자연어처리 기반 텍스트 분석

Youngkyo Oh
Lecturer, Dept. of Education, College of Education, Chonnam National University; 77, Yongbong-ro, Buk-gu, Gwangju, Korea, 61186 5young-kyo@hanmail.net
Issues and prospects of AI digital English education: Text analysis Based on Natural Language Processing


© 2025 KASELL All rights reserved
This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The study aimed to quantify the content and keyword relationships in AI-based English education texts to understand the current status of English education and gain insights into the future direction of English education. Data analysis involved a text analysis that utilized Natural Language Processing (NLP) techniques to extract meaningful content and information from large-scale text data, identifying new meanings and knowledge at the contextual level by considering the relationships between texts and words. The primary analysis methods used included keyword analysis, network text analysis, and topic modeling. The research findings are as follows. First, a frequency analysis of the collected texts revealed terms such as ‘technology,’ ‘learning,’ ‘textbooks,’ ‘big data,’ ‘application,’ ‘mathematics,’ ‘information,’ ‘students,’ and ‘edtech’ based on Term Frequency (TF) values. TF-IDF (Term Frequency-Inverse Document Frequency) values identified ‘textbooks,’ ‘learning,’ ‘big data,’ and ‘edtech.’ Additionally, N-gram analysis highlighted the term ‘development and implementation of AI digital textbooks.’ Next, through ego-network analysis for relationship exploration, words related to ‘learning,’ ‘textbooks,’ ‘technology and future trends’ were found to be connected around the term ‘English.’ Finally, topic modeling analysis using Latent Dirichlet Allocation (LDA) classified the texts into five topics: ‘AI-Based Educational Innovation,’ ‘Transition to a Digital-Based Learning Environment,’ ‘Future-Oriented Changes in the Education System,’ ‘Learner-Centered Personalized English Education,’ and ‘Technological Innovation and Services in the Education Industry.’ This study provides insights into the future direction of English education by providing educators and learners with AI-powered text analysis tools.

Keywords:

natural language processing, AI digital English education, network text analysis, topic modeling

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