Reimagining Language Acquisition in the Age of Artificial Intelligence through Sociolinguistic and Semiotic Perspectives
DOI:
https://doi.org/10.71435/Keywords:
Artificial Intelligence, Language Acquisition, Sociolinguistics, SemioticsAbstract
Purpose: This study explores the transformative impact of Artificial Intelligence (AI) on language acquisition, focusing on the integration of AI tools in language learning through sociolinguistic and semiotic perspectives. The aim is to assess AI’s influence on learner engagement, teacher-student rapport, linguistic diversity, and cultural representation.
Subjects and Methods: A mixed-methods approach was employed, combining quantitative surveys and experiments with qualitative sociolinguistic and semiotic analyses. Surveys measured the effectiveness of AI tools in language learning, while content analysis and interviews provided deeper insights into cultural and contextual cues embedded in AI-generated texts.
Results: The results demonstrate that AI tools significantly enhance learner engagement and communication willingness, offering personalized and interactive learning experiences. However, challenges regarding linguistic diversity and bias in AI models remain, with gaps in representing regional dialects and non-standard language forms. Semiotic analysis revealed that AI still struggles to incorporate cultural and contextual nuances, which are essential for meaningful communication. Additionally, biases in AI models, including gender and racial bias, were detected, emphasizing the need for diversified training data and bias mitigation strategies. AI’s role in shaping language change was also noted, with AI tools influencing the emergence of new linguistic forms and expressions.
Conclusions: AI has the potential to revolutionize language acquisition, but its development must address challenges related to linguistic diversity, bias, and cultural representation. The study advocates for a more inclusive and contextually sensitive approach to AI integration in language education to ensure equitable, meaningful, and diverse learning outcomes.
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References
AbuSahyon, A. S. A. E., Alzyoud, A., Alshorman, O., & Al-Absi, B. (2023). AI-driven technology and chatbots as tools for enhancing English language learning in the context of second language acquisition: A review study. International Journal of Membrane Science and Technology, 10(1), 1209-1223.
Almadhady, A. A. (2023). A Comprehensive Exploration of Applied Linguistics: An In-Depth Analysis. Journal of Tikrit University for Humanities, 30(12, 2), 160-184. https://doi.org/10.25130/jtuh.30.12.2.2023.18
Baskara, F. R. (2023). Ai-driven dynamics: ChatGPT transforming elt teacher-student interactions. Lensa: Kajian Kebahasaan, Kesusastraan, dan Budaya, 13(2), 261-275.
Bhattacharya, P., Prasad, V. K., Verma, A., Gupta, D., Sapsomboon, A., Viriyasitavat, W., & Dhiman, G. (2024). Demystifying chatgpt: An in-depth survey of openai’s robust large language models. Archives of Computational Methods in Engineering, 31(8), 4557-4600. https://doi.org/10.1007/s11831-024-10115-5
Chen, Y. (2024, May). Enhancing language acquisition: The role of AI in facilitating effective language learning. In 2024 3rd International Conference on Humanities, Wisdom Education and Service Management (HWESM 2024) (pp. 593-600). Atlantis Press. https://doi.org/10.2991/978-2-38476-253-8_71
Creely, E. (2024). Exploring the role of generative AI in enhancing language learning: Opportunities and challenges. International Journal of Changes in Education, 1(3), 158-167. https://doi.org/10.47852/bonviewIJCE42022495
Davoodi, A. (2024). EQUAL AI: A framework for enhancing equity, quality, understanding and accessibility in liberal arts through AI for multilingual learners. Language, Technology, and Social Media, 2(2), 178-203. https://doi.org/10.70211/ltsm.v2i2.139
Gatt, A., & Krahmer, E. (2018). Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, 61, 65-170. https://doi.org/10.1613/jair.5477
Hussain, S. M., Satti, S. M. J., & Khan, Z. (2024). AI-Powered Personalized Learning: Advancing Language Education in the Digital Era. Journal of Social Signs Review, 2(4), 730-740.
Jafarnia, A., Hariri, H., & Parvizi, G. R. (2023). Unlocking the potential: Exploring the multifaceted impact of artificial intelligence integration in language learning. Language Education and Technology, 3(2).
Kovalenko, I., & Baranivska, N. (2024). Integrating Artificial Intelligence in English Language Teaching: Exploring the potential and challenges of AI tools in enhancing language learning outcomes and personalized education. Європейські соціо-правові та гуманітарні студії, (1), 86-95. https://doi.org/10.61345/2734-8873.2024.1.9
Nanduri, D. K. (2024). Exploring the Role of Generative Artificial Intelligence in Culturally Relevant Storytelling for Native Language Learning Among Children (Master's thesis, University of Maryland, College Park).
Nunan, D. (2022). The changing landscape of English language teaching and learning. In Handbook of practical second language teaching and learning (pp. 3-23). Routledge. https://doi.org/10.4324/9781003106609
Saddhono, K., Suhita, R., Istanti, W., Kusmiatun, A., Kusumaningsih, D., & Sukmono, I. K. (2024, November). AI-Powered Language Learning: Enhancing Literacy in the Digital Age. In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 856-861). IEEE. https://doi.org/10.1109/AECE62803.2024.10911149
Salfin, S., Kurniadi, P., & Erwin, E. (2024). Language Development in the Digital Age, A Literature Review on the Influence of Technology on Human Communication. Sciences du Nord Humanities and Social Sciences, 1(01), 01-07. https://doi.org/10.58812/0yhk5d80
Sarnovska, N., Rybinska, J., & Mykhailichenko, Y. (2024). Enhancing university remote language learning through innovative applications of artificial intelligence technologies amidst global challenges. Teaching Languages at Higher Educational Establishments at the Present Stage. Intersubject Relations, (44), 151-165. https://doi.org/10.26565/2073-4379-2024-44-10
Solak, E. (2024). Revolutionizing language learning: How ChatGPT and AI Are changing the way we learn languages. International Journal of Technology in Education, 7(2), 353-372.
Tan, T. H. (2024, December). Rule-Based vs. AI-Driven: Comparing PolyAQG Framework and Generative AI Models. In Proceedings of the 2024 8th International Conference on Natural Language Processing and Information Retrieval (pp. 298-303). https://doi.org/10.1145/3711542.3711583
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE access, 9, 110558-110572. https://doi.org/10.1109/ACCESS.2021.3097962
Thellefsen, M. M. (2024). Signifying unity: exploring the interplay of semiotics, universalism and pluralism in information science. Journal of Documentation, 80(4), 922-938. https://doi.org/10.1108/JD-07-2023-0144
Topsakal, O., & Topsakal, E. (2022). Framework for a foreign language teaching software for children utilizing AR, voicebots and ChatGPT (large language models). The Journal of Cognitive Systems, 7(2), 33-38. https://doi.org/10.52876/jcs.1227392
Xia, Y., Shin, S. Y., & Kim, J. C. (2024). Cross-cultural intelligent language learning system (CILS): Leveraging AI to facilitate language learning strategies in cross-cultural communication. Applied Sciences, 14(13), 5651. https://doi.org/10.3390/app14135651
Yadav, D. S. (2024). Navigating the landscape of AI integration in education: opportunities, challenges, and ethical considerations for harnessing the potential of artificial intelligence (AI) for teaching and learning. BSSS Journal of Computer, 15(1), 38-48. https://doi.org/10.51767/jc1503
Zhang, Y., & Dong, C. (2024). Exploring the digital transformation of generative ai-assisted foreign language education: A socio-technical systems perspective based on mixed-methods. Systems, 12(11), 462. https://doi.org/10.3390/systems12110462
Zhi, R., & Wang, Y. (2024). On the relationship between EFL students' attitudes toward artificial intelligence, teachers' immediacy and teacher-student rapport, and their willingness to communicate. System, 124, 103341. https://doi.org/10.1016/j.system.2024.103341
Zhong, T., Yang, Z., Liu, Z., Zhang, R., Liu, Y., Sun, H., ... & Liu, T. (2024). Opportunities and challenges of large language models for low-resource languages in humanities research. arXiv preprint arXiv:2412.04497. https://doi.org/10.48550/arXiv.2412.04497
