Exploring Chatbot AI in improving vocational students’ English pronunciation
DOI:
https://doi.org/10.54855/acoj.231429Keywords:
Chatbot AI, vocational education, English pronunciation, quasi-experimental, A1 levelAbstract
Thanks to technological development, there has been a remarkable leap in the application of artificial intelligence, especially in education. This paper examines the effectiveness of Chatbot Mission Fluent, an AI chatbot, in improving English pronunciation among vocational students in a Hanoi college. Sixty vocational students participate in an A1 English course hosted in a quasi-experimental research design. After the course, participants were interviewed and asked to complete a survey questionnaire to collect their feedback on the AI Chatbot. The experimental group showed notably better English pronunciation than the control group. This research aims to address the knowledge gap regarding the use of AI chatbots as a tool in vocational education. Through this approach, the potential of AI chatbots in improving English pronunciation is carefully explored and emphasized among vocational students. However, this paper also noticed some difficulties in applying and monetarily supporting Missionfluent throughout the process. Overall, this study focuses on the significance of incorporating innovative technologies into language learning programs and highlights the beneficial potential of AI Chatbots’ application in improving vocational students' English pronunciation while acknowledging AI Chatbots’ drawbacks which were discovered in the procedure.References
Anggraini, A. (2022). Improving students’pronunciation skill using elsa speak application. Journey: Journal of English Language and Pedagogy, 5(1), 135-141.
Aswaty, P., & Indari, A. (2022). The Effect of Using Elsa (English Language Speech Assistant) Speak Application on Students’ Speaking Ability for the Eleventh Grade of Mas Darul Al Muhajirin in the Academic Year 2021/2022. Serunai: Jurnal Ilmiah Ilmu Pendidikan, 8(1), 18–23. https://doi.org/10.37755/sjip.v8i1.616.
Bajorek, J. P. (2017). L2 pronunciation in CALL: The unrealized potential of Rosetta stone, Duolingo, Babbel, and mango languages. Issues and Trends in Educational Technology, 5(1), 24-51.
Bin-Hady, W. R. A., Al-Kadi, A., Hazaea, A., & Ali, J. K. M. (2023). Exploring the dimensions of ChatGPT in English language learning: A global perspective. Library Hi Tech.
Brown, A. (2014). Understanding and Teaching English Pronunciation: A Teacher's Course Book. Routledge.
Brown, H. D. (2014). Principles of language learning and teaching (6th ed.). Pearson Education.
Brown, L. (2017). Integrating Technology in English Language Teaching: A Guide for Practitioners. Routledge.
Carey, Michael. (2002). “An L1-specific CALL pedagogy for the instruction of pronunciation with Korean learners of English.” (Unpublished PhD Dissertation), Macquarie: Macquaire University.
Celce-Murcia, M, D Brinton., & J. Goodwin. (1996). Teaching pronunciation: A reference for teachers of English to speakers of other languages. Cambridge: Cambridge: University Press.
Celce-Murcia, M., & Olshtain, E. (2000). Discourse and context in language teaching: A guide for language teachers. Cambridge University Press.
Celce-Murcia, M., Brinton, D., & Goodwin, J. M. (2010). Teaching Pronunciation: A Course Book and Reference Guide (2nd ed.). Cambridge University Press.
Charniak, E., & McDermott, D. V. (1985). Introduction to Artificial Intelligence. Addison-Wesley.
Chen, C.F. (2007). Computer-assisted language learning and teaching. Retrieved, May 20, 2009, from http://www.nkfust.edu.tw/˜emchen/CALL/.
Chen, X., Zou, D., Xie, H., & Cheng, G. (2021). Twenty years of personalized language learning: Topic modeling and knowledge mapping. Educational Technology & Society, 24(1), 205–222. https://www.jstor.org/stable/26977868.
Cook, V. (1999). Going beyond the native speaker in language teaching. TESOL Quarterly, 33(2), 185-209.
Derwing, T. M., & Munro, M. J. (2015). Pronunciation Fundamentals: Evidence-Based Perspectives for L2 Teaching and Research. Amsterdam: John Benjamins Publishing Company.
Darsih, Endang, Marwinto Wihadi, and Agie Hanggara. "Using ELSA App in Speaking Classes: Students’ Voices ." UNISET, 2021: 3-4.
Dörnyei, Z. (2009). The L2 motivational self-system. In Z. Dörnyei & E. Ushioda (Eds.), Motivation, language identity and the L2 self (pp. 9-42). Bristol, England: Multilingual Matters.
Gimson, A. C. (1989). English Pronunciation Practice. Edward Arnold Publishers.
Goh, C. CM, & Burns, A. (2012). Teaching speaking: A holistic approach. New York: Cambridge University Press.
Haryadi, S. H. (2020). Integrating “English Pronunciation” App Into Pronunciation Teaching: How It Affects Students" Participation and Learning. Participation and Learning.
Hung, T. M. (2015). The impact of using smartphone apps on learners' pronunciation accuracy. English Language Teaching, 8(8), 1-8.
Johnson, R., & Smith, A. (2020). The Role of AI in Language Learning: Current Applications and Future Directions. Journal of Educational Technology, 43(2), 189-203.
Jones, M., et al. (2019). Enhancing Language Learning through Technology: A Research-Based Framework for the Integration of Technology into Language Learning. Language Learning & Technology, 23(3), 1-16.
Kim, N. Y., Cha, Y., & Kim, H.- S. (2019). Future English learning: Chatbots and artificial intelligence. Multimedia Assisted Language Learning, 22(3), 32–53. https://doi.org/10.15702/mall.2019.22.3.32
Ladefoged, P., & Johnson, K. (2011). A Course In Phonetics (6th ed.). Wadsworth Publishing.
Le, P. N., Vu, H. M. L., & Tran, M. N. (2021). Improving EFL Students’ Intonation In-Text Using Shadowing Technique with the Implementation of Google Text-to-Speech. AsiaCALL Online Journal, 13(1), 93-121. Retrieved from https://asiacall.info/acoj/index.php/journal/article/view/102
Lee, S., & Wong, P. (2019). The Use of Chatbot in Language Learning: A Systematic Review. Computers & Education, 141, 103610.
Levis, J. M., Sonsaat, S., Link, S., Barriuso, T. A., & Soto, C. (2016). Native and nonnative teachers of L2 pronunciation: Effects on learner performance. TESOL Quarterly, 50(4), 894-931.
Levy, M. (2016). CALL dimensions: Options and issues in computer-assisted language learning. Routledge.
Liu, C., Liu, W., Huang, J., & Liu, J. (2019). Evaluating the effectiveness of a chatbot-based intelligent tutoring system for enhancing EFL learners' English pronunciation. Computers & Education, 140, 103611.
Liu, Q., Yang, Y., Zhang, Y., Zhu, M., & Yu, L. (2019). Automatic Generation of Training Data for Chatbot with Deep Generative Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3732-3742).
Luger, G. F. (1993). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (3rd ed.). Pearson Education.
Nilsson, N. J. (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers.
Nguyen, T. G. M. (2021). Some Common Ways for Students to Improve Pronunciation during Covid-19 Pandemic. AsiaCALL Online Journal, 12(5), 129-136. Retrieved from https://www.asiacall.info/acoj/index.php/journal/article/view/99
Rajadurai, J. (2001, July). An investigation of the effectiveness of teaching pronunciation to Malaysian TESL students. In Forum, 39(3), 10- 15. Retrieved from http://exchangees.state.gov/forum/vols/vol39/no3/p10.htm
Roach, P. (2009). English Phonetics and Phonology: A Practical Course (4th ed.). Cambridge University Press.
Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson.
Saragih, E., Tabrani, N., & Muthmainnah, N. (2021). The Use of Digital Feedback on ELSA Speak in Learning Pronunciation for Seventh Grade of Junior High School. JEELL (Journal of English Education, Linguistics and Literature), 8(1), 133–145. https://doi.org/doi.org/10.32682/jeell.v8i1.1965
Smith, J. (2018). The Impact of Technology on English Language Teaching and Learning. TESOL Journal, 9(1), 1-17.
Smith, J., Johnson, A., & Lee, S. (2020). Comparing the effectiveness of a chatbot-based pronunciation training system with traditional classroom instruction for English learners. Journal of Language Learning, 45(2), 123-145.
Smith, K., & Johnson, R. (2021). Exploring the Potential of Chatbot AI in Improving English Pronunciation: A Vocational Education Perspective. Journal of Language Education and Technology, 18(2), 45-62.
Thomson, R. I., & Derwing, T. M. (2015). The effectiveness of L2 pronunciation instruction: A narrative review. Applied Linguistics, 36(3), 326-344
Wang, Y., & Vasquez, C. (2019). Enhancing English language learning through digital storytelling. Language Learning & Technology, 23(3), 1-18.
Yang, H., Kim, H., Lee, J., & Shin, D. (2022). Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL FirstView, 1–17. https://doi.org/10.1017/S0958344022000039
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