NATURAL LANGUAGE PROCESSING (NLP) APPLICATIONS IN LANGUAGE TEACHING
Keywords:
Natural Language Processing (NLP), Intelligent Tutoring Systems (ITS), language learning applications, Text-to-Speech (TTS), Speech Recognition Systems (SRS), Virtual Reality (VR), Augmented Reality (AR), digital literacy.Abstract
This article explores the applications of Natural Language Processing (NLP) in language teaching, examining its role in automated language assessment, intelligent tutoring systems, language learning applications, and text-to-speech/speech recognition systems. Through in-depth analysis, it highlights NLP's benefits such as enhanced personalization, immediate feedback, and adaptability, addressing challenges in language teaching like scalability and engagement. The integration of NLP with educational methods fosters dynamic learning environments, nurturing linguistic proficiency and cultural understanding. Future strides in NLP, coupled with virtual reality integration, promise transformative shifts in language education, offering immersive, interactive, and personalized learning experiences. However, ethical considerations such as bias mitigation and data privacy require attention. Collaborative efforts among educators, policymakers, and developers are crucial for responsible and inclusive NLP deployment in language teaching, empowering learners with global communication skills and cultural fluency in an interconnected world.
References
K. Evanini, S. Xie, and K. Zechner, “Prompt-based Content Scoring for Automated Spoken Language Assessment,” 2013. Accessed: Apr. 12, 2024. [Online]. Available: https://aclanthology.org/W13-1721.pdf
K. VanLEHN, “The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems,” Educational Psychologist, vol. 46, no. 4, pp. 197–221, Oct. 2011, doi: https://doi.org/10.1080/00461520.2011.611369
C. R. Heil, J. S. Wu, J. J. Lee, and T. Schmidt, “A Review of Mobile Language Learning Applications: Trends, Challenges, and Opportunities,” The EuroCALL Review, vol. 24, no. 2, p. 32, Sep. 2016, doi: https://doi.org/10.4995/eurocall.2016.6402
M. S. Keezhatta, “The Impact of Neuro-Linguistic Programming on English Language Teaching: Perceptions of NLP-Trained English Teachers,” International Journal of English Linguistics, vol. 9, no. 6, p. 454, Nov. 2019, doi: https://doi.org/10.5539/ijel.v9n6p454
C. Parra Escartín, W. Reijers, T. Lynn, J. Moorkens, A. Way, and C.-H. Liu, “Ethical considerations in NLP shared tasks,” doras.dcu.ie, Apr. 04, 2017. https://doras.dcu.ie/23231/
U. Kamath, J. Liu, and J. Whitaker, Deep Learning for NLP and Speech Recognition. Cham: Springer International Publishing, 2019. doi: https://doi.org/10.1007/978-3-030-14596-5
V. Shah, “Advancements in Deep Learning for Natural Language Processing in Software Applications,” INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, vol. 4, no. 3, pp. 45–56, Sep. 2020, Available: http://ijcst.com.pk/index.php/IJCST/article/view/373
G. Lampropoulos, E. Keramopoulos, K. Diamantaras, and G. Evangelidis, “Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses,” Education Sciences, vol. 12, no. 11, p. 798, Nov. 2022, doi: https://doi.org/10.3390/educsci12110798
S. Meera and S. Geerthik, “Natural Language Processing,” Artificial Intelligent Techniques for Wireless Communication and Networking, pp. 139–153, Feb. 2022, doi: https://doi.org/10.1002/9781119821809.ch10
J. Liddicoat and A. Scarino, Intercultural Language Teaching and Learning. John Wiley & Sons, 2013. Accessed: Apr. 12, 2024. [Online]. Available: https://books.google.com/books?hl=ru&lr=&id=cb6WbN-le-AC&oi=fnd&pg=PR8&dq=Language+teaching+plays+a+crucial+role+in+facilitating+communication+and+fostering+cultural+understanding+in+a+globalized+world+&ots=7ee _j4EP4u&sig=DiipBMFs4m5_FkEFg8jXVOiWmMc
