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A comprehensive review of the application of NLP technology in language learning
- 1 Wuhan University
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Abstract
The rapid development of Natural Language Processing (NLP) technology has provided new perspectives and tools for the acquisition of second languages. As our world becomes increasingly interconnected, the role of NLP in facilitating language learning has become more prominent. This paper reviews the multifaceted applications of NLP technology in language learning, including auxiliary teaching functions such as reading assistance, writing feedback, oral interaction, and personalized learning. These applications have significantly enhanced language learners' abilities in vocabulary acquisition, grammatical application, pronunciation accuracy, and reading comprehension through real-time feedback, enhanced interactivity, and promotion of cultural understanding. Despite the immense potential of NLP technology in second language learning, challenges such as technical accuracy, cultural adaptability, and data privacy exist. This paper proposes strategies, such as ensuring technical accuracy, curating diverse datasets and safeguarding data privacy to address these challenges. Looking forward, the future development of NLP technology in second language education holds great promise. As NLP continues to evolve, it is expected to contribute to more personalized, effective, and accessible language learning solutions. This paper aims to provide valuable references and insights for educators, technology developers, and policymakers, enabling them to harness the transformative potential of NLP in language education and to navigate the future of educational technology with confidence and foresight.
Keywords
Natural language processing, second language acquisition, technological application, educational innovation
[1]. Rohit Kumar Yadav, Aanchal Madaan & Janu. (2024). Comprehensive analysis of natural language processing. Global Journal of Engineering and Technology Advances,19(1), pp. 083-090.
[2]. Bax, S. (2011). Normalisation revisited: The effective use of technology in language education. International Journal of Computer-Assisted Language Learning and Teaching, 1(2), 1-15.
[3]. Zheng, P., Liang, X., Huang, G., & Liu, X. (2016). Mapping the field of communication technology research in Asia: Content analysis and text mining of SSCI journal articles 1995-2014. Asian Journal of Communication, 26(6), 511-531.
[4]. Jiacing Liang, Gwojen Hwang, Meirong Alice Chen, Darmawansah (2021): Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach, Interactive Learning Environments, DOI: 10.1080/10494820.2021.1958348.
[5]. Lo, J. J., Wang, H. M., & Yeh, S. W. (2004). Effects of confidence scores and remedial instruction on prepositions learning in adaptive hypermedia. Computers & Education, 42(1), 45–63.
[6]. Toprak-Yıldız, T. E. (2024). Natural Language Processing Applications in Language Assessment. Advances in Educational Technologies and Instructional Design, pp.216-234 .
[7]. Detmar Meurers. (2012). Natural language processing and language learning. The Encyclopedia of Applied Linguistics.
[8]. Mahmoud Azab, Ahmed Salama, Kemal Oflazer, Hideki Shima, Jun Araki, and Teruko Mitamura. (2013a). An English reading tool as a nlp showcase. In The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations. Asian Federation of Natural Language Processing, Nagoya, Japan, pp.5-8.
[9]. Detmar Meurers, Ramon Ziai, et al. (2010). Enhancing authentic web pages for language learners. Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, pp.10-18.
[10]. Maria Chinkina and Detmar Meurers. (2016) Linguistically aware information retrieval: providing input enrichment for second language learners. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, San Diego, CA.
[11]. Leonardo Zilio, Rodrigo Wilkens, C´edrick Fairon. (2017) Proceedings of Recent Advances in Natural Language Processing, Varna, Bulgaria, Sep 4-6, pp.839-846.
[12]. Detmar Meurers. (2012) Natural language processing and language learning. The Encyclopedia of Applied Linguistics.
[13]. George, A. S., & George, A. H. (2023). A Review of ChatGPT AI’s Impact on Several Business Sectors. Partners Universal International Innovation Journal, 1(1), 9-23.
[14]. FX. Risang Baskara, FX. Mukarto. (2023) Exploring the Implications of ChatGPT for Language Learning in Higher Education. Indonesian Journal of English Language Teaching and Applied Linguistics, Vol. 7(2).
[15]. McNamara, D. S., Crossley, S. A., Roscoe, R. D., Allen, L. K., & Dai, J. (2015). A Hierarchical classification approach to automated essay scoring. Assessing Writing, 23, 35-59.
[16]. Alexopoulou, T., Michel, M., Murakami, A., & Meurers, D. (2017). Task effects on linguistic complexity and accuracy: A Large‐scale learner corpus analysis employing natural language processing techniques. Language Learning, 67(S1), 180-208.
[17]. Kyle, K., & Crossley, S. A. (2018). Measuring syntactic complexity in L2 writing using fine‐grained clausal and phrasal indices. The Modern Language Journal, 102(2), 333-349.
[18]. Vajjala, S. (2018). Automated assessment of non-native learner essays: Investigating the role of linguistic features. International Journal of Artificial Intelligence in Education, 28(1), 79-105.
[19]. Lee, K., Kwon, O. W., Kim, Y. K., & Lee, Y. (2015). A Hybrid approach for correcting grammatical errors. Critical CALL- Proceedings of the 2015 EUROCALL Conference, Padova, Italy. Research-publishing.net, pp. 362-367.
[20]. Lin, C. C., Liu, G. Z., & Wang, T. I. (2017). Development and usability test of an e-learning tool for engineering graduates to develop academic writing in English: A Case study. Educational Technology & Society, 20(4), 148-161.
[21]. Ayedoun, E., Hayashi, Y., & Seta, K. (2019). Adding communicative and affective strategies to an embodied conversational agent to enhance second language learners’ willingness to communicate. International Journal of Artificial Intelligence in Education, 29(1), 29-57.
[22]. Sherry Ruan, Liwei Jiang, Qianyao Xu et al. (2021) EnglishBot: An AI-Powered Conversational System for Second Language Learning. IUI’21, April 14-17, College Station, TX, USA.
[23]. Li, W. & Mohamad, M.(2023). An Efficient Probabilistic Deep Learning Model for the Oral Proficiency Assessment of Student Speech Recognition and Classification. International Journal on Recent and Innovation Trends in Computing and Communication, pp. 411-424 .
[24]. Chen, C. M., Hsu, S. H., Li, Y. L., & Peng, C. J. (2006). Personalized intelligent m-learning system for supporting effective English learning. In 2006 IEEE International Conference on Systems, Man and Cybernetics, Vol. 6, IEEE, pp. 4898-4903.
[25]. Chao, C. J., Lin, H. K., Huang, T. C., Hsu, K. C. & Hsieh, C. Y. (2012). The Application of affective tutoring systems (ATS) in enhancing learners’ motivation. Workshop Proceedings of the 20th International Conference on Computers in Education (ICCE), Asia-Pacific Society for Computers in Education, pp. 58-66.
[26]. Pandarova, I., Schmidt, T., Hartig, J., Boubekki, A., Jones, R. D., & Brefeld, U. (2019). Predicting the difficulty of exercise items for dynamic difficulty adaptation in adaptive language tutoring. International Journal of Artificial Intelligence in Education, 29(3), 342-367.
[27]. R. Vasile, N. Niraula, and R. Banjade. (2015) DeepTutor: an effective, online intelligent tutoring system that promotes deep learning, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, vol. 29, no. 1.
[28]. FX. Risang Baskara, FX. Mukarto. (2023) Exploring the Implications of ChatGPT for Language Learning in Higher Education, Indonesian Journal of English Language Teaching and Applied Linguistics, Vol. 7(2).
Cite this article
Peng,J. (2024). A comprehensive review of the application of NLP technology in language learning. Applied and Computational Engineering,92,163-168.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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