The DOI matches its Crossref record.
Perceptions of High School Students on AI Chatbots Use in English Learning: Benefits, Concerns, and Ethical Consideration
Abstract
This study explored high school students’ perceptions of using AI chatbots in English learning. Specifically, it aimed to gauge the breadth of chatbot utilization and discern perceptions surrounding potential challenges linked to their use. Thirty students from a high school took part in the survey. Data analysis involved frequency, mean and an independent sample t-test. The findings are as follows. First, students recognized the importance and value of chatbots highly and perceived their usability positively. However, their previous experience with chatbots did not sway this perception. Second, students perceived using chatbots in English learning as very beneficial. Especially, those with chatbot usage experience have a more positive perception compared to those without experience. Third, students were relatively aware of potential ethical issues with using chatbots. They were particularly concerned about plagiarism and copyright issues and potential personal information breaches regardless of their experience with chatbots. They also perceived potential educational issues, fearing over-reliance on chatbots might hinder their exploratory learning or lead to copying assignments directly, missing out on learning opportunities. However, those without experience were more skeptical than those with experience. The implications and suggestions derived from these findings are also discussed.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Lund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025) · AI in Education · doi:10.3390/aieduc1010002 · Cited by 64
“J. E. Lee and Maeng (2023), recommend that “teachers should provide educational guidance for students to take a critical approach to information provided by the chatbots” (J. E. Lee & Maeng, 2023, p. 69).”
Li, M., & Wilson, J. (2025) · Information · doi:10.3390/info16070519 · Cited by 49
Acuña, B. P., & Durão, R. C. F. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1490067 · Cited by 69
Integrating Artificial Intelligence Into English Language Teaching: A Systematic Review
Daud, A., Aulia, A. F., Muryanti, M., Harfal, Z., Nabilla, O., & Ali, H. S. (2025) · European Journal of Educational Research · doi:10.12973/eu-jer.14.2.677 · Cited by 55
Artificial intelligence in K-12 instruction: the role of teacher professional development
Mah, D.-K., Groß, N., Egloffstein, M., & Prilop, C. N. (2026) · Smart Learning Environments · doi:10.1186/s40561-026-00442-4 · Cited by 22
Metadata
- Authors
- Lee, J. E., & Maeng, U.
- ORCID
- Lee, Ji Eun
- Venue
- Journal of Pan-Pacific Association of Applied Linguistics
- Published
- 2023-12-31
- Volume
- 27
- Issue
- 2
- Pages
- 53-72
- ISSN
- 1345-8353 (ISSN-L)
- 2671-938X
- OpenAlex
- W4390587276
- Cited by
- 17
- References
- 0
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 1
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Pan-Pacific Association of Applied Linguistics (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › English Language Learning and Teaching
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Keywords
- AI chatbots, English language learning, high school students, student perceptions, chatbot usability, ethical considerations, plagiarism, copyright, data privacy, exploratory learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
