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.
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GenAI applications in language education can offer numerous benefits that can particularly support learners from low-income families.
“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
Section 4.2.1 (Benefits) reports several benefits for language learners, including help with writing, vocabulary, reading, and feedback. It does not identify learners from low-income families as particular beneficiaries. Section 4.2.2 (Limitations and challenges) instead notes that payment requirements can disadvantage students without easy access. (LLM, conf=0.86)
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- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Physical Sciences › Computer Science › Information Systems › 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 ›
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