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AI-powered language learning: The role of NLP in grammar, spelling, and pronunciation feedback
- 1 Liaoning Petrochemical University, Liaoning, China
- 2 Hong Kong Baptist University, Hong Kong, China
- 3 Macau University of Science and Technology, Macau, China
* Author to whom correspondence should be addressed.
Abstract
The introduction of artificial intelligence (AI) and natural language processing (NLP) in language learning has transformed the practices of education. This paper analyzes the role of AI-powered tools in English language learning with specific focus on automated essay scoring (AES), speech recognition, and advanced grammar and spelling checkers. By examining the accuracy of the most effective AES systems, it is evident that automated feedback has increased the quality of writing by providing detailed, consistent, and objective feedback on submitted essays. Speech recognition is another tool that is being used to assess pronunciation and fluency of learners. The software is constantly improving, with advances in NLP and machine learning. The technology for fluency tests is becoming more accurate, and is helping to improve learner outcomes significantly. Lastly, we examine advanced grammar and spelling checkers, which are increasingly used for correcting the contextual errors of individual learners. The AI systems adapt to the user and their needs, which make the corrections more effective. This paper offers an overview of how AI is leading to personalized and effective language learning, ultimately enhancing the educational experience of students and embracing future possibilities in education technology.
Keywords
AI, NLP, English language learning, automated essay scoring, speech recognition.
[1]. Rusmiyanto, Rusmiyanto, et al. "The role of artificial intelligence (AI) in developing English language learner's communication skills." Journal on Education 6.1 (2023): 750-757.
[2]. De la Vall, Roxana Rebolledo Font, and Fabián González Araya. "Exploring the benefits and challenges of AI-language learning tools." International Journal of Social Sciences and Humanities Invention 10.01 (2023): 7569-7576.
[3]. Hockly, Nicky. "Artificial intelligence in English language teaching: The good, the bad and the ugly." Relc Journal 54.2 (2023): 445-451.
[4]. Alshumaimeri, Yousif A., and Abdulrahman K. Alshememry. "The extent of AI applications in EFL learning and teaching." IEEE Transactions on Learning Technologies (2023).
[5]. Shaikh, Sarang, et al. "Assessing the usability of ChatGPT for formal english language learning." European Journal of Investigation in Health, Psychology and Education 13.9 (2023): 1937-1960.
[6]. Warschauer, Mark, et al. "The affordances and contradictions of AI-generated text for writers of english as a second or foreign language." Journal of Second Language Writing 62 (2023).
[7]. Alawadh, Husam M., et al. "English language learning via YouTube: An NLP-based analysis of users' comments." Computers 12.2 (2023): 24.
[8]. Bharadiya, Jasmin. "A comprehensive survey of deep learning techniques natural language processing." European Journal of Technology 7.1 (2023): 58-66.
[9]. Purnama, Yulian, et al. "Neuro-Linguistic Programming as an instructional strategy to enhance Foreign Language teaching." Studies in Media and Communication 11.5 (2023): 50-59.
[10]. Chung, Sehwan, et al. "Comparing natural language processing (NLP) applications in construction and computer science using preferred reporting items for systematic reviews (PRISMA)." Automation in Construction 154 (2023): 105020.
Cite this article
Zhang,J.;Zhu,C.;Zhang,Z. (2024). AI-powered language learning: The role of NLP in grammar, spelling, and pronunciation feedback. Applied and Computational Engineering,102,18-23.
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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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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