Implementation of Interactive Artificial Intelligence Voice in Speech Shadowing Method to Improve English Speaking Ability in High School Students: Strategy Analysis and Challenges
DOI:
https://doi.org/10.24256/ideas.v13i1.5906Abstract
This study aims to analyze the implementation of Interactive Artificial Intelligence (AI) Voice in the Speech Shadowing method to improve English speaking skills in high school students. The main objectives of this study are to identify implementation strategies, challenges faced, and the effectiveness of using AI Voice in learning to speak English. The method used in this study is a qualitative approach with a multi-site case study type, which was carried out in two A-accredited private schools in Malang City. Data were collected through interviews, observations, and documentation involving teachers and students as key informants. The data analysis technique used is the interactive model of Miles, Huberman, and Saldana, which allows researchers to categorize, organize, and draw conclusions from the data obtained. The results of the study indicate that the integration of AI Voice in the Speech Shadowing method can increase the interactivity of learning and provide direct feedback that accelerates students' mastery of speaking skills. The use of AI Voice allows students to practice with native speakers virtually, which is very effective in improving pronunciation, intonation, and fluency. However, the challenges faced in implementing this technology include limited device infrastructure, curriculum readiness, and student acceptance of new technology. However, overall, the integration of AI Voice has been shown to have a positive impact on improving students' speaking skills. Based on these findings, it is recommended that schools explore the use of technology in language learning in more depth, by providing training to teachers and ensuring the availability of adequate devices. Further research is also needed to examine the potential and challenges of implementing AI Voice in various educational contexts, in order to improve the quality of English learning at a broader level.
References
AbuSahyon, A. (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. https://doi.org/10.15379/ijmst.v10i1.2829
Adank, P., Hagoort, P., & Bekkering, H. (2010). Imitation improves language comprehension. Psychological Science, 21(12), 1903-1909. https://doi.org/10.1177/0956797610389192
Akongoh, R. (2021). Teacher-based assessment of speaking in Cameroonian secondary schools: the impact of teacher training. Journal of English Language Teaching and Applied Linguistics, 3(2), 01-11. https://doi.org/10.32996/jeltal.2021.3.2.1
Amin, M. (2023). Ai and chat gpt in language teaching: enhancing efl classroom support and transforming assessment techniques. International Journal of Higher Education Pedagogies, 4(4), 1-15. https://doi.org/10.33422/ijhep.v4i4.554
Anwar, M. (2023). Ai-powered Arabic language education in the era of society 5.0. Iaic Transactions on Sustainable Digital Innovation (Itsdi), 5(1), 50-57. https://doi.org/10.34306/itsdi.v5i1.607
Avcı, Z., O’Dwyer, L., & Lawson, J. (2019). Designing effective professional development for technology integration in schools. Journal of Computer Assisted Learning, 36(2), 160-177. https://doi.org/10.1111/jcal.12394
Azizah, L. (2023). The adoption of e-learning media to enhance English skills in magelang orphanage. ICTCED, 1(1), 96-104. https://doi.org/10.18196/ictced.v1i1.14
Batanero, J., Graván, P., Reyes-Rebollo, M., & Rueda, M. (2021). Impact of educational technology on teacher stress and anxiety: a literature review. International Journal of Environmental Research and Public Health, 18(2), 548. https://doi.org/10.3390/ijerph18020548
Bani, M., & Masruddin, M. (2021). Development of Android-based harmonic oscillation pocket book for senior high school students. JOTSE: Journal of Technology and Science Education, 11(1), 93-103.
Chisega-Negrilă, A. (2023). The new revolution in language learning: the power of artificial intelligence and education 4.0. Bulletin of Carol I National Defence University, 12(2), 16-27. https://doi.org/10.53477/2284-9378-23-17
Chouhan, D. (2023). A study on artificial intelligence in education. International Journal of Engineering Technology and Management Sciences, 7(1), 449-456. https://doi.org/10.46647/ijetms.2023.v07i01.063
Cracco, E., Bardi, L., Desmet, C., Genschow, O., Rigoni, D., Coster, L., … & Braß, M. (2018). Automatic imitation: a meta-analysis. Psychological Bulletin, 144(5), 453-500. https://doi.org/10.1037/bul0000143
Drozdova, P., Hout, R., & Scharenborg, O. (2017). L2 voice recognition: the role of speaker-, listener-, and stimulus-related factors. The Journal of the Acoustical Society of America, 142(5), 3058-3068. https://doi.org/10.1121/1.5010169
Eden, C. (2024). A review of ai-driven pedagogical strategies for equitable access to science education. Magna Scientia Advanced Research and Reviews, 10(2), 044-054. https://doi.org/10.30574/msarr.2024.10.2.0043
Enzelina, Y. (2023). Exploring English language education major university lecturers’ and students’ perceptions of ai-based applications in post-pandemic learning. Salee Study of Applied Linguistics and English Education, 4(2), 487-502. https://doi.org/10.35961/salee.v4i2.843
Ezzaim, A. (2023). Enhancing academic outcomes through an adaptive learning framework utilizing a novel machine learning-based performance prediction method. Data & Metadata, 2, 164. https://doi.org/10.56294/dm2023164
Foote, J. and McDonough, K. (2017). Using shadowing with mobile technology to improve l2 pronunciation. Journal of Second Language Pronunciation, 3(1), 34-56. https://doi.org/10.1075/jslp.3.1.02foo
Ghorashi, N. (2023). Ai-powered chatbots in medical education: potential applications and implications. Cureus. https://doi.org/10.7759/cureus.43271
Gyawali, Y. (2022). Artificial intelligence in english language teaching: navigating the future with emerging perspectives. Journal of Language and Linguistics in Society, (26), 21-27. https://doi.org/10.55529/jlls.26.21.27
Gyawali, Y. (2022). Artificial intelligence in english language teaching: navigating the future with emerging perspectives. Journal of Language and Linguistics in Society, (26), 21-27. https://doi.org/10.55529/jlls.26.21.27
Hamada, Y. (2015). Shadowing: who benefits and how? uncovering a booming efl teaching technique for listening comprehension. Language Teaching Research, 20(1), 35-52. https://doi.org/10.1177/1362168815597504
Hamada, Y. (2018). Shadowing for pronunciation development: haptic-shadowing and ipa-shadowing. The Journal of Asiatefl, 15(1), 167-183. https://doi.org/10.18823/asiatefl.2018.15.1.11.167
Hockly, N. (2023). Artificial intelligence in English language teaching: the good, the bad and the ugly. Relc Journal, 54(2), 445-451. https://doi.org/10.1177/00336882231168504
Hoshina, Y., Yada, K., Maki, H., Yoshino, T., Takaiso, H., & Akiyama, M. (2022). Medical english education in japan: developing a curriculum to motivate students by providing visualization opportunities using near-peer teaching. The Journal of Medical Investigation, 69(3.4), 332-334. https://doi.org/10.2152/jmi.69.332
Ismayanti, D., Said, Y. R., Usman, N., & Nur, M. I. (2024). The Students Ability in Translating Newspaper Headlines into English A Case Study. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 12(1), 108-131.
Jin, S. (2023). Shadowing practice using ted talks on the metaverse platform, gather: affective factors and oral proficiency development. Stem Journal, 24(2), 31-46. https://doi.org/10.16875/stem.2023.24.2.31
Iksan, M., Husnaini, H., & Masruddin, M. (2022). Implementation of weekly English Program with fun learning method for Pesantren students. Ethical Lingua: Journal of Language Teaching and Literature, 9(2), 872-879.
Jin, S. (2023). The effects of a shadowing activity using movies on Korean efl college learners’ speaking skills and their affective attitudes. International Education Forum, 1(2), 1-18. https://doi.org/10.26689/ief.v1i2.5623
Kim, N. and Kim, M. (2022). Teacher’s perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.755914
Masruddin, Hartina, S., Arifin, M. A., & Langaji, A. (2024). Flipped learning: facilitating student engagement through repeated instruction and direct feedback. Cogent Education, 11(1), 2412500.
Madehang, M., Masruddin, M., & Iksan, M. Reflecting on the Implementation of Online English Learning in Islamic Higher Education. International Journal of Asian Education, 5(3).
Lawrence, L. (2023). How teachers conceptualize shared control with an ai co‐orchestration tool: a multiyear teacher‐centered design process. British Journal of Educational Technology, 55(3), 823-844. https://doi.org/10.1111/bjet.13372
Liu, M. (2023). Exploring the application of artificial intelligence in foreign language teaching: challenges and future development. SHS Web of Conferences, 168, 03025. https://doi.org/10.1051/shsconf/202316803025
Liu, Y. (2023). A comparison of automated corrective feedback and traditional corrective feedback: a review study. The Educational Review Usa, 7(9), 1365-1368. https://doi.org/10.26855/er.2023.09.024
Makhlouf, M. (2021). Effect of artificial intelligence-based application on saudi preparatory -year students’ efl speaking skills at albaha university. International Journal of English Language Education, 9(2), 36. https://doi.org/10.5296/ijele.v9i2.18782
Mantell, J. and Pfordresher, P. (2013). Vocal imitation of song and speech. Cognition, 127(2), 177-202. https://doi.org/10.1016/j.cognition.2012.12.008
Moulieswaran, N. and S, P. (2022). Amelioration of google assistant – a review of artificial intelligence stimulated second language learning and teaching. World Journal of English Language, 13(1), 86. https://doi.org/10.5430/wjel.v13n1p86
Moulieswaran, N. and S, P. (2023). Google assistant assisted language learning (gaall): esl learners’ perception and problem towards ai-powered google assistant-assisted English language learning. Studies in Media and Communication, 11(4), 122. https://doi.org/10.11114/smc.v11i4.5977
Othman, N. and Chuah, K. (2021). The relationship between English language fluency and learning engagement: a case study among first-year undergraduates. International Journal of Academic Research in Progressive Education and Development, 10(2). https://doi.org/10.6007/ijarped/v10-i2/9670
Park, J. (2023). Integrating artificial intelligence into science lessons: teachers’ experiences and views. International Journal of Stem Education, 10(1). https://doi.org/10.1186/s40594-023-00454-3
Peng, H., Ma, S., & Spector, J. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1). https://doi.org/10.1186/s40561-019-0089-y
Popenici, S. and Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1). https://doi.org/10.1186/s41039-017-0062-8
Putra, M. (2023). Ai writing correction tools: teachers and students’ perception. Jurnal Tatsqif, 21(1), 35-66. https://doi.org/10.20414/jtq.v21i1.7963
Qiao, H. (2023). Artificial intelligence-based language learning: illuminating the impact on speaking skills and self-regulation in Chinese efl context. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1255594
Reynolds, B. and Yu, M. (2020). Using english as an international language for fluency development in the internationalized asian university context. The Asia-Pacific Education Researcher, 31(1), 11-21. https://doi.org/10.1007/s40299-020-00534-w
Rossiter, M., Derwing, T., Manimtim, L., & Thomson, R. (2010). Oral fluency: the neglected component in the communicative language classroom. Canadian Modern Language Review/ La Revue Canadienne des Langue’s Vivantes, 66(4), 583-606. https://doi.org/10.3138/cmlr.66.4.583
Rusmiyanto, R., Huriati, N., Fitriani, N., Tyas, N., Rofi’i, A., & Sari, M. (2023). The role of artificial intelligence (ai) in developing English language learner's communication skills. Journal on Education, 6(1), 750-757. https://doi.org/10.31004/joe.v6i1.2990
Rusmiyanto, R., Huriati, N., Fitriani, N., Tyas, N., Rofi’i, A., & Sari, M. (2023). The role of artificial intelligence (ai) in developing English language learner's communication skills. Journal on Education, 6(1), 750-757. https://doi.org/10.31004/joe.v6i1.2990
Tapalova, O. and Zhiyenbayeva, N. (2022). Artificial intelligence in education: aied for personalized learning pathways. The Electronic Journal of E-Learning, 20(5), 639-653. https://doi.org/10.34190/ejel.20.5.2597
Venezia, J., Fillmore, P., Matchin, W., Isenberg, A., Hickok, G., & Fridriksson, J. (2016). Perception drives production across sensory modalities: a network for sensorimotor integration of visual speech. Neuroimage, 126, 196-207. https://doi.org/10.1016/j.neuroimage.2015.11.038
Wang, B. (2023). The application and challenges of artificial intelligence in speech recognition. Applied and Computational Engineering, 17(1), 36-40. https://doi.org/10.54254/2755-2721/17/20230907
Wang, L., Pfordresher, P., Jiang, C., & Liu, F. (2021). Individuals with autism spectrum disorder are impaired in absolute but not relative pitch and duration matching in speech and song imitation. Autism Research, 14(11), 2355-2372. https://doi.org/10.1002/aur.2569
Wei, L. (2023). Artificial intelligence in language instruction: impact on English learning achievement, l2 motivation, and self-regulated learning. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1261955
Williams, R., Ali, S., Devasia, N., DiPaola, D., Hong, J., Kaputsos, S., … & Breazeal, C. (2022). Ai + ethics curricula for middle school youth: lessons learned from three project-based curricula. International Journal of Artificial Intelligence in Education, 33(2), 325-383. https://doi.org/10.1007/s40593-022-00298-y
Wilt, H., Wu, Y., Trotter, A., & Adank, P. (2022). Automatic imitation of human and computer-generated vocal stimuli. Psychonomic Bulletin & Review, 30(3), 1093-1102. https://doi.org/10.3758/s13423-022-02218-6
Wu, Y., Evans, B., & Adank, P. (2019). Sensorimotor training modulates automatic imitation of visual speech. Psychonomic Bulletin & Review, 26(5), 1711-1718. https://doi.org/10.3758/s13423-019-01623-8
Yang, H. and Kyun, S. (2022). The current research trend of artificial intelligence in language learning: a systematic empirical literature review from an activity theory perspective. Australasian Journal of Educational Technology, 180-210. https://doi.org/10.14742/ajet.7492
Zia, N. and Karnawati, R. (2022). Learning model course Choukri through shadowing technique. Journal of Japanese Language Education and Linguistics, 6(1), PRESS. https://doi.org/10.18196/jjlel.v6i1.12083
Zou, B., Guan, X., Shao, Y., & Chen, P. (2023). Supporting speaking practice by social network-based interaction in artificial intelligence (ai)-assisted language learning. Sustainability, 15(4), 2872. https://doi.org/10.3390/su15042872
Zulkarnain, N. and Yunus, M. (2023). Teachers' perceptions and continuance usage intention of artificial intelligence technology in tesl. International Journal of Multidisciplinary Research and Analysis, 06(05). https://doi.org/10.47191/ijmra/v6-i5-34
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