Impact of  an Artificial Intelligence in Language Learning - A survey

Dr. D. Antony Arul Raj Arul Raj; K. V. Rukmani Rukmani; Kiruthika C Rukmani; Praveen M Rukmani; Anandhachitan A Rukmani

doi:10.32628/CSEIT2410218

Authors

Dr. D. Antony Arul Raj Associate Professor Cum ANO, Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Dr. K. V. Rukmani Associate Professor & Head, Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Kiruthika C Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Praveen M Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Anandhachitan A Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author

DOI:

https://doi.org/10.32628/CSEIT2410218

Keywords:

Artificial intelligence, Natural Language Processing, Hybrid teaching, Machine Learning, Education, Deep Learning

Abstract

Bully Scan, an artificial intelligence system for identifying offensive language on social media, is proposed in "A Natural Language Processing and Machine Learning-Based Framework to Automatically Identify Cyberbullying. This paradigm, which aims to reduce the negative impacts of cyberbullying and encourage healthy online interactions, is a critical step in using AI for social well-being. The paper, "Research and Practice of Hybrid Teaching Based on AI technology for Foreign Language Translation," offers a novel strategy for teaching foreign languages through the incorporation of AI. The project investigates a hybrid teaching approach that combines AI-powered language translation tools with conventional classroom training. This method seeks to improve accuracy and efficiency of language learning by providing real-time translation support. Through the use of AI technologies, such as machine learning and natural language processing, the system offers students helpful translation assistance, enhancing their educational experience. The study demonstrates encouraging outcomes in terms of raising students' proficiency and effectiveness in translation in a blended learning setting.

The paper "Modular Design of English Pronunciation Level Evaluation System Based on Deep Learning Algorithm" offers a novel method for determining pronunciation levels in English by utilizing deep learning algorithms. The study uses techniques like support vector machines and BP neural networks to address the problem of computational intensity in language teaching technologies. Through the application of machine deep learning, the system seeks to improve the precision and efficacy of pronunciation level assessments, providing insightful information for the development of theories for foreign language instruction in the rapidly changing field of artificial intelligence. The study's modular design approach offers a viable foundation for enhancing pronunciation assessment in language instruction.

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References

X. He, Y. Lin, Z. Hu, X. Xu, R. Xu and W. Xiang, "AI Chinese sign language recognition interactive system based on audio-visual integration," 2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE), Changchun, China, 2023, pp. 962-968, doi: 10.1109/ICEACE60673.2023.10442295. DOI: https://doi.org/10.1109/ICEACE60673.2023.10442295

S. Ravimaram, J. N. Kumar S, A. Sathish, S. Vatchala, R. Rawat and M. R. T. F, "Robust Transfer Learning Based Modelling for Accelerating the Learning of Ai in the Field of NLP," 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023, pp. 1026-1030, doi: 10.1109/ICACCS57279.2023.10112829. DOI: https://doi.org/10.1109/ICACCS57279.2023.10112829

K. C. Sarker, M. M. Rahman and A. Siam, "Anglo-Bangla Language-Based AI Chatbot for Bangladeshi University Admission System," 2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI), Shanghai, China, 2023, pp. 42-46, doi: 10.1109/CCCAI59026.2023.00016. DOI: https://doi.org/10.1109/CCCAI59026.2023.00016

S. Kusal, S. Patil, J. Choudrie, K. Kotecha, S. Mishra and A. Abraham, "AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions," in IEEE Access, vol. 10, pp. 92337-92356, 2022, doi: 10.1109/ACCESS.2022.3201144. DOI: https://doi.org/10.1109/ACCESS.2022.3201144

J. R’Baiti, R. Faizi, Y. Hmamouche and A. E. F. Seghrouchni, "A transformer-based architecture for the automatic detection of clickbait for Arabic headlines," 2023 5th International Conference on Natural Language Processing (ICNLP), Guangzhou, China, 2023, pp. 248-252, doi: 10.1109/ICNLP58431.2023.00052. DOI: https://doi.org/10.1109/ICNLP58431.2023.00052

K. Singh, S. S. Grover and R. K. Kumar, "Cyber Security Vulnerability Detection Using Natural Language Processing," 2022 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2022, pp. 174-178, doi: 10.1109/AIIoT54504.2022.9817336. DOI: https://doi.org/10.1109/AIIoT54504.2022.9817336

S. Shrimali, "A Natural Language Processing and Machine Learning-Based Framework to Automatically Identify Cyberbullying and Hate Speech in Real-Time," 2022 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 2022, pp. 1-5, doi: 10.1109/URTC56832.2022.10002243. DOI: https://doi.org/10.1109/URTC56832.2022.10002243

S. Shao, S. Alharir, S. Hariri, P. Satam, S. Shiri and A. Mbarki, "AI-based Arabic Language and Speech Tutor," 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), Abu Dhabi, United Arab Emirates, 2022, pp. 1-8, doi: 10.1109/AICCSA56895.2022.10017924. DOI: https://doi.org/10.1109/AICCSA56895.2022.10017924

J. Zhu and J. Van Brummelen, "Teaching Students About Conversational AI Using Convo, a Conversational Programming Agent," 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), St Louis, MO, USA, 2021, pp. 1-5, doi: 10.1109/VL/HCC51201.2021.9576290. DOI: https://doi.org/10.1109/VL/HCC51201.2021.9576290

J. Wu, A. Polyak, Y. Taigman, J. Fong, P. Agrawal and Q. He, "Multilingual Text-To-Speech Training Using Cross Language Voice Conversion And Self-Supervised Learning Of Speech Representations," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 2022, pp. 8017-8021, doi: 10.1109/ICASSP43922.2022.9746282. DOI: https://doi.org/10.1109/ICASSP43922.2022.9746282

W. Wei, M. Lun, L. Yong-An and Q. Qianqian, "An Analysis of AI Technology Assisted English Teaching Based on the Noticing Hypothesis," 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE), Dali, China, 2021, pp. 158-162, doi: 10.1109/ICAIE53562.2021.00040. DOI: https://doi.org/10.1109/ICAIE53562.2021.00040

W. Cui, Z. Xue and K. -P. Thai, "Performance Comparison of an AI-Based Adaptive Learning System in China," 2018 Chinese Automation Congress (CAC), Xi'an, China, 2018, pp. 3170-3175, doi: 10.1109/CAC.2018.8623327. DOI: https://doi.org/10.1109/CAC.2018.8623327

B. Li and M. Peng, "The Evaluation of a Blended Teaching Mode Based on an AI Language Learning Platform," 2021 2nd International Conference on Information Science and Education (ICISE-IE), Chongqing, China, 2021, pp. 1437-1440, doi: 10.1109/ICISE-IE53922.2021.00320. DOI: https://doi.org/10.1109/ICISE-IE53922.2021.00320

M. Luo and L. Cheng, "Exploration of Interactive Foreign Language Teaching Mode Based on Artificial Intelligence," 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), Chongqing, China, 2020, pp. 285-290, doi: 10.1109/CVIDL51233.2020.00-84. DOI: https://doi.org/10.1109/CVIDL51233.2020.00-84

S. Li, "Research on the Exploration and Reflection of Foreign Language Teaching Based on “Artificial Intelligence + Education” in the Big Data Era," 2021 2nd International Conference on Big Data Economy and Information Management (BDEIM), Sanya, China, 2021, pp. 354-357, doi: 10.1109/BDEIM55082.2021.00078. DOI: https://doi.org/10.1109/BDEIM55082.2021.00078

S. Chabot, J. Drozdal, M. Peveler, Y. Zhou, H. Su and J. Braasch, "A Collaborative, Immersive Language Learning Environment Using Augmented Panoramic Imagery," 2020 6th International Conference of the Immersive Learning Research Network (iLRN), San Luis Obispo, CA, USA, 2020, pp. 225-229, doi: 10.23919/iLRN47897.2020.9155140. DOI: https://doi.org/10.23919/iLRN47897.2020.9155140

T. Liu, E. Kim, X. Li, T. Yuizono, Y. Nagai and Y. Lu, "Research and Practice of Hybrid Teaching Based on AI technology for Foreign Language Translation," 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China, 2020, pp. 664-668, doi: 10.1109/ICCEA50009.2020.00145. DOI: https://doi.org/10.1109/ICCEA50009.2020.00145

R. M. Samant, M. R. Bachute, S. Gite and K. Kotecha, "Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions," in IEEE Access, vol. 10, pp. 17078-17097, 2022, doi: 10.1109/ACCESS.2022.3149798. DOI: https://doi.org/10.1109/ACCESS.2022.3149798