University students’ perceptions of artificial intelligence-based tools for English writing courses

Yong-Jik Lee 1, Robert O. Davis 2 * , Sun Ok Lee 2
More Detail
1 The Institute of Educational Research, Chonnam National University, Gwangju City, SOUTH KOREA
2 Department of Education, Chonnam National University, Gwangju City, SOUTH KOREA
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 1, Article No: e202412. https://doi.org/10.30935/ojcmt/14195
OPEN ACCESS   4515 Views   4288 Downloads   Published online: 10 Feb 2024
Download Full Text (PDF)

ABSTRACT

This research explores the perceptions of Korean university students regarding artificial intelligence (AI)-based writing tools that include tools guided by machine learning, such as Google Translate and Naver Papago, and generative AI tools, such as Grammarly. A mixed methodology was used, including both quantitative and qualitative data. Among students who have taken English writing courses, 80 Korean university students volunteered for the online survey. After the survey, the research team recruited interview participants, and five volunteered participants joined the focus group interview. The study results indicate that these AI-based writing tools could improve English language learners (ELLs) writing skills. ELLs also noted the strengths and weaknesses of each AI-based tool, including the accessibility of translation machine learning and the error-checking capabilities of generative AI. However, interview data analysis indicates that the excessive use of AI-based writing tools could interfere with ELLs’ English writing process. This study highlights the need to effectively integrate AI-based tools in English language teaching for adult ELLs worldwide.
Keywords: AI, AI-based writing tools, machine translation, generative AI, Naver Papago, Google Translate, Grammarly

CITATION

Lee, Y.-J., Davis, R. O., & Lee, S. O. (2024). University students’ perceptions of artificial intelligence-based tools for English writing courses. Online Journal of Communication and Media Technologies, 14(1), e202412. https://doi.org/10.30935/ojcmt/14195

REFERENCES

  • Ahn, S., & Chung, E. S. (2020). Students’ perceptions of the use of online machine translation in L2 writing. Multimedia-Assisted Language Learning, 23(2), 10-35. https://doi.org/10.1080/09588221.2020.1871029
  • Bahri, H., & Mahadi, T. S. T. (2016). Google Translate as a supplementary tool for learning Malay: A case study at Universiti Sains Malaysia. Advances in Language and Literary Studies, 7(3), 161-167. https://doi.org/10.7575/aac.alls.v.7n.3p.161
  • Beiler, I. R., & Dewilde, J. (2020). Translation as translingual writing practice in English as an additional language. The Modern Language Journal, 104(3), 533-549. https://doi.org/10.1111/modl.12660
  • Briggs, N. (2018). Neural machine translation tools in the language learning classroom: Students’ use, perceptions, and analyses. Jalt Call Journal, 14(1), 2-24. https://doi.org/10.29140/jaltcall.v14n1.221
  • Castleberry, A., & Nolen, A. (2018). Thematic analysis of qualitative research data: Is it as easy as it sounds? Currents in Pharmacy Teaching and Learning, 10(6), 807-815. https://doi.org/10.1016/j.cptl.2018.03.019
  • Chen, H. H. J., Yang, C. T. Y., & Lai, K. K. W. (2023). Investigating college EFL learners’ perceptions toward the use of Google Assistant for foreign language learning. Interactive Learning Environments, 31(3), 1335-1350. https://doi.org/10.1080/10494820.2020.1833043
  • Chon, Y., Shin, D., & Kim, G. E. (2021). Comparing L2 learners’ writing against parallel machine-translated texts: Raters’ assessment, linguistic complexity, and errors. System, 96, 102408. https://doi.org/10.1016/j.system.2020.102408
  • Fithriani, R. (2023). Utilizing artificial intelligence-based paraphrasing tool in EFL writing class: A focus on Indonesian university students’ perceptions. Scope: Journal of English Language Teaching, 7(2), 210-218. https://doi.org/10.30998/scope.v7i2.14882
  • Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based writing assistant’s impact on English language learners. Computers and Education: Artificial Intelligence, 3, 100055. https://doi.org/10.1016/j.caeai.2022.100055
  • Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology & Society, 26(1), 112-131. https://www.jstor.org/stable/48707971
  • Im, H. J. (2017). The university students’ perceptions or attitudes on the use of the English automatic translation in a general English class: Based on English writing lessons. Korean Journal of General Education, 11(6), 727-751. https://j-kagedu.or.kr/upload/pdf/kagedu-11-6-727.pdf
  • Jeong, N. S. (2021). A study on the effects of machine translators on college students’ writing proficiency and affective attitude. Multimedia-Assisted Language Learning, 24(1), 134-157. https://doi.org/10.15702/mall.2021.24.1.134
  • Kim, H, K, & Han, S, M. (2021). College students’ perceptions of AI-based writing learning tools: With a focus on Google Translate, Naver Papago, and Grammarly. Modern English Education, 22(4), 90-100. https://doi.org/10.18095/meeso.2021.22.4.90
  • Kim, J, K, & Song, K, S. (2012). A comparison of web-based and mobile-assisted English writing using smart media. Korean Institute of Information Technology, 10(12), 197-204. https://www.kci.go.kr/kciportal/ci/ sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001721045
  • Klekovkina, V., & Denié-Higney, L. (2022). Machine translation: Friend or foe in the language classroom? L2 Journal, 14(1), 105-135. https://doi.org/10.5070/L214151723
  • Lee, S. M. (2019). Korean college students’ perceptions toward the effectiveness of machine translation on L2 revision. Multimedia-Assisted Language Learning, 22(4), 206-225. https://doi.org/10.15702/mall.2019.22.4.206
  • Lee, S. M. (2020). The impact of using machine translation on EFL students’ writing. Computer Assisted Language Learning, 33(3), 157-175. https://doi.org/10.1080/09588221.2018.1553186
  • Lee, S. M., & Briggs, N. (2021). Effects of using machine translation to mediate the revision process of Korean university students’ academic writing. RECALL, 33(1), 18-33. https://doi.org/10.1017/S0958344020000191
  • Murtisari, E. T., Widiningrum, R., Branata, J., & Susanto, R. D. (2019). Google Translate in language learning: Indonesian EFL students’ attitudes. The Journal of Asia TEFL, 16(3), 978. https://doi.org/10.18823/asiatefl.2019.16.3.14.978
  • O’Neill, R., & Russell, A. (2019). Stop! Grammar time: University students’ perceptions of the automated feedback program Grammarly. Australasian Journal of Educational Technology, 35(1), 42-56. https://doi.org/10.14742/ajet.3795
  • Qassemzadeh, A., & Soleimani, H. (2016). The impact of feedback provision by Grammarly software and teachers on learning passive structures by Iranian EFL learners. Theory and Practice in Language Studies, 6(9), 1884-1894. https://doi.org/10.17507/tpls.0609.23
  • Stapleton, P., & Kin, B. L. K. (2019). Assessing the accuracy and teachers’ impressions of Google Translate: A study of primary L2 writers in Hong Kong. English for Specific Purposes, 56, 18-34. https://doi.org/10.1016/j.esp.2019.07.001
  • Tao, Y., & Zou, B. (2023). Students’ perceptions of the use of Kahoot! in English as a foreign language classroom learning context. Computer Assisted Language Learning, 36(8), 1668-1687. https://doi.org/10.1080/09588221.2021.2011323
  • Tsai, S. C. (2019). Using Google Translate in EFL drafts: A preliminary investigation. Computer Assisted Language Learning, 32(5-6), 510-526. https://doi.org/10.1080/09588221.2018.1527361
  • Yoon, C. W., & Chon, Y. V. (2022). Machine translation errors and L2 learners’ correction strategies by error type and English proficiency. English Teaching, 77(3), 153-175. https://doi.org/10.15858/engtea.77.3.202209.153