The Impact of Artificial Intelligence and Machine Learning on Linguistic Accuracy, Fluency, and Self-Direction Among Advanced EFL Students
DOI:
https://doi.org/10.17507/tpls.1506.27Keywords:
Artificial Intelligence (AI), Machine Learning (ML), linguistic accuracy, self-direction, linguistic educationAbstract
Linguistic capacity has been stimulated by artificial intelligence and automated learning among those who speak English as a foreign language while being proficient learners. In this research study, the influences of machine learning as well as artificial intelligence technologies on fluency and autonomy have been investigated. This study was conducted by establishing control and experimental groups, with a total of 120 participants selected. Over twelve weeks, a shift of digitally supported learning was utilized. The experimental group utilized technology-enhanced adaptive grammar tools, translation software, and artificial intelligence-driven feedback systems. On the other hand, the control group complied with conventional learning methods with no technological interventions. Educational videos and assignments have been provided to both groups by instructors during their sessions. The study results revealed a significant improvement in students’ writing fluency after the intervention. Nearly all participants in the experimental group achieved a higher level of fluency; however, the area of automation exhibited only a marginal difference. This suggests that both control and experimental learning groups experienced some improvement in learning. However, learners’ autonomy and Linguistic precision reached new heights in the experimental group. In conclusion, the revolution that AI and ML usher in terms of self-directed learning for EFL teaching is immense.
References
Aggarwal, C. C. (2021). Machine learning: The inductive view. In Artificial Intelligence (pp. 167–210). Springer International Publishing. https://doi.org/10.1007/978-3-030-72357-6_6
Akerkar, R. (2018). Machine learning. In Artificial Intelligence for Business (pp. 19–32). Springer International Publishing. https://doi.org/10.1007/978-3-319-97436-1_2. SpringerLink.
Alastuey, M. C. B. (2011). Perceived benefits and drawbacks of synchronous voice-based computer-mediated communication in the foreign language classroom. Computer Assisted Language Learning, 24(5), 419–432. https://doi.org/10.1080/09588221.2011.574639
Anderson, J. N., Davidson, N., Morton, H., & Jack, M. A. (2008). Language learning with interactive virtual agent scenarios and speech recognition: Lessons learned. Computer Animation and Virtual Worlds, 19(5), 605–619. https://doi.org/10.1002/cav.265
Backer, E., & Gorter, D. (2012). Translanguaging in the digital era: The interaction between language and technology. Journal of Language, Identity & Education, 11(3), 151-165. https://doi.org/10.1080/15348458.2012.699104
Brown, G., & Yule, G. (1983). Teaching the spoken language. Cambridge University Press.
Chen, C., & Lee, T. (2011). Emotion recognition and communication for reducing second language speaking anxiety in a web-based one-to-one synchronous learning environment. British Journal of Educational Technology, 42(3), 417–440. https://doi.org/10.1111/j.1467-8535.2009.01035.x
Chinnery, G. M. (2006). Emerging technologies: Going to the MALL: Mobile-assisted language learning. Language Learning & Technology, 10(1), 9–16.
Chiu, T.-L., Liou, H.-C., & Yeh, Y. (2007). A study of web-based oral activities enhanced by automatic speech recognition for EFL college learning. Computer Assisted Language Learning, 20(3), 209–233.
Chowdhary, K. R. (2020). Machine learning. In Fundamentals of Artificial Intelligence (pp. 375–413). Springer India. https://doi.org/10.1007/978-81-322-3972-7_13
Cochrane, T. (2014). Critical success factors for transforming pedagogy with mobile Web 2.0. British Journal of Educational Technology, 45(1), 65–82. http://doi.org/10.1111/j.1467-8535.2012.01384.x
Comas-Quinn, A., Mardomingo, R., & Valentine, C. (2009). Mobile blogs in language learning: Making the most of informal and situated learning opportunities. ReCALL, 21(1), 96–112.
Demouy, V., & Kukulska-Hulme, A. (2010). On the spot: Using mobile devices for listening and speaking practice on a French language program. Open Learning, 25(3), 217–232.
Derwing, T. M., Munro, M. J., & Carbonaro, M. (2000). Does popular speech recognition software work with ESL speech? TESOL Quarterly, 34(3), 592–603.
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10.1038/nature14541
Hincks, R. (2002). Speech recognition for language teaching and evaluation: A study of existing commercial products. In Proceedings of the Seventh International Conference on Spoken Language Processing (ICSLP) (pp. 733–736).
Hsu, L. (2012). English as a foreign language learner’s perception of mobile-assisted language learning: A cross-national study. Computer Assisted Language Learning, 26(3), 197–213.
Hwang, W.-Y., & Chen, H. S. L. (2013). Users’ familiar situational contexts facilitate the practice of EFL in elementary schools with mobile devices. Computer Assisted Language Learning, 26(2), 101–125.
Jakhar, D., & Kaur, I. (2020). Artificial intelligence, machine learning and deep learning: Definitions and differences. Clinical and Experimental Dermatology, 45(1), 131–132. https://doi.org/10.1111/ced.14029. OUP Academic
Jung, H.-K. (2011). The correction of learner’s English pronunciation errors through speech recognition reading program. Multimedia-Assisted Language Learning, 14(3), 291–314.
Kukulska-Hulme, A. (2006). Mobile language learning now and in the future. In From vision to practice: Language learning and IT. Swedish Net University.
Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: Can mobile devices support collaborative practice in speaking and listening? ReCALL, 20(3), 271–289.
Lee, R. S. T. (2020). Machine learning. In Artificial Intelligence in Daily Life (pp. 41–70). Springer Singapore. https://doi.org/10.1007/978-981-15-7695-9_3
Lys, F. (2013). The development of advanced learner oral proficiency using iPads. Language Learning & Technology, 17(3), 94–116.
Michalewicz, Z. (1992). Machine learning. In Artificial Intelligence (pp. 215–229). Springer. https://doi.org/10.1007/978-3-662-02830-8_13
Morton, H., & Jack, M. (2010). Speech interactive computer-assisted language learning: A cross-cultural evaluation. Computer Assisted Language Learning, 23(4), 295–319. https://doi.org/10.1080/09588221.2010.493524
Neri, A., Mich, O., Gerosa, M., & Giuliani, D. (2008). The effectiveness of computer assisted pronunciation training for foreign language learning by children. Computer Assisted Language Learning, 21(5), 393–408.
Oberg, A., & Daniels, P. (2013). Analysis of the effect a student-centered mobile learning instructional method has on language acquisition. Computer Assisted Language Learning, 26(2), 177–196.
Pennycook, A. (2018). Language and mobility: Global flows and local complexity. Routledge.
Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Prentice Hall.
Taulli, T. (2019). Machine learning. In Artificial Intelligence Basics (pp. 39–67). https://doi.org/10.1007/978-1-4842-5028-0_3. SpringerLink
Vermeulen, A. F. (2019). Industrialized artificial intelligence. In Industrial Machine Learning (pp. 533–556). https://doi.org/10.1007/978-1-4842-5316-8_14
Zou, B. (2013). Teachers’ support in using computers for developing students’ listening and speaking skills in pre-sessional English courses. Computer Assisted Language Learning, 26(1), 83–99. https://doi.org/10.1080/09588221.2011.631143
