The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Using generative artificial intelligence/ChatGPT for academic communication: Students' perspectives
Open accessAbstract
Generative artificial intelligence (GenAI) tools such as ChatGPT with their human‐like intelligence and language processing capabilities are significantly impacting the way we live, work, and communicate with each other. While scholars have increasingly focused on the use of GenAI in higher education since its inception, little is known about how key higher education stakeholders, particularly students, perceive its impact on teaching and learning within the context of academic communication, an area central to students' development of transferable skills and literacy competencies yet heavily influenced by the technology. This empirical study addresses the gap by investigating students' experiences and attitudes toward GenAI tools for English academic communication, focusing on their overall perceptions, perceived benefits, limitations, and challenges. Drawing on data from a questionnaire survey with 475 students and interviews with 12 at two universities in China, our findings indicate that students generally view GenAI positively, considering them useful for learning academic communication skills, particularly in writing, grammar, vocabulary, and reading. However, limitations are recognized in terms of giving feedback on critical thinking, creativity, and speaking skills. In addition, information reliability, ethical issues, and impact on assessment and academic integrity also emerged as important concerns. Our study argues that universities should embrace and capitalize on the affordances of GenAI and address its challenges to better support students' learning of critical academic literacy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Four studies (Foung et al., 2024;Li, Li, & Cho, 2023;Liu et al., 2024;Mabuan, 2024) illustrate how GenAI applications in language education can offer numerous benefits that can particularly support learners from lowincome families.”
From Generative AI in Language Education: Bridging Divide and Fostering Inclusivity (2025) · cites this paper as Liu et al., 2024
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
The Content Analysis Guidebook
Neuendorf, K. A. (2017) · doi:10.4135/9781071802878 · Cited by 9,286
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301
ChatGPT for Language Teaching and Learning
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023) · RELC Journal · doi:10.1177/00336882231162868 · Cited by 1,371
Chatbots applications in education: A systematic review
Okonkwo, C. W., & Ade-Ibijola, A. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100033 · Cited by 879
Summary of ChatGPT-Related research and perspective towards the future of large language models
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100017 · Cited by 855
Self-citationHuang, W., Hew, K. F., & Fryer, L. K. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12610 · Cited by 830
Flagged first, then by citations
Research works printed but not linked: 5
- Biggs J., 2011, EBOOK: Teaching for quality learning at university
- Chakraborty U., 2023, Rise of generative AI and ChatGPT: Understand how generative AI and ChatGPT are transforming and reshaping the business world (English Edition)
- Chan C. K. Y. &Zhou W.(2023).Deconstructing student perceptions of generative AI (GenAI) through an expectancy value theory (EVT)‐based instrument.arXiv preprint arXiv:2305.01186. · arXiv
- Clarizia F., 2018, Lecture notes in computer science: Volume 11161. Cyberspace safety and security, 291
- Raman R. Mandal S. Das P. Kaur T. Sanjanasri J. &Nedungadi P.(2023).University students as early adopters of ChatGPT: Innovation diffusion study.https://doi.org/10.21203/rs.3.rs‐2734142/v1 · DOI
Cited by
Sorted by citations
Items 101–106 of 106
Xu, H., Wu, C., & Zhang, Y. (2026) · Learned Publishing · doi:10.1002/leap.2098
Gang, Y., Jiang, R., & Shen, Q. (2026) · System · doi:10.1016/j.system.2026.104143
Yalçın, Y., Bakır-Yalçın, E., İslamoğlu, H., & Ursavaş, Ö. F. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70341
Academic writing needs and writing-support accessibility in a Saudi university
Alkubaidi, M. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103768
Liu, W., & Blin, F. (2026) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2026-0001
Sieciński, K., & Oliński, M. (2026) · Industry and Higher Education · doi:10.1177/09504222261498278
Metadata
- Authors
- Liu, Y., Park, J., & McMinn, S.
- Affiliations
- The Hong Kong University of Science and Technology (Guangzhou) (CN)
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Park, Jaeuk
- Venue
- International Journal of Applied Linguistics
- Publisher
- Wiley
- Published
- 2024-06-27
- Volume
- 34
- Issue
- 4
- Pages
- 1437-1461
- ISSN
- 0802-6106 (ISSN-L)
- 1473-4192
- OpenAlex
- W4400137179
- Cited by
- 106
- References
- 47
- Citations by year
- 2026: 56 · 2025: 46 · 2024: 4
- FWCI
- 39.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Applied Linguistics (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › English Language Learning and Teaching
- Keywords
- generative AI, ChatGPT, scholarly communication, student perceptions, English academic writing, academic literacy, higher education, AI in teaching and learning, academic integrity, information reliability, AI ethics, assessment, transferable skills
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