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

Yong-Jik Lee 1, Robert O. Davis 2 * , Sun Ok Lee 2
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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   1223 Views   1208 Downloads   Published online: 10 Feb 2024
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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

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