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To resist it or to embrace it? Examining ChatGPT’s potential to support teacher feedback in EFL writing
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In the citing paper
“The second is the displacement of teacher expertise, and thus the suggestion in literature is for complementary configurations where AI is used to complement rather than replace teacher feedback [17].”
In the citing paper
“First, with the aid of this technology, students may be able to complete tasks for which they lack the necessary skills, such as creating photographs and getting some ideas from other disciplines (Guo & Wang, 2023).”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Guo & Wang, 2023
In the citing paper
“AI has the potential to lessen individual biases in grading procedures, including essays, project proposals, and theses (Guo & Wang, 2023).”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Guo & Wang, 2023
In the citing paper
“Various assignment types, including essays and computer coding, are impacted differently by GenAI (Fergus et al., 2023; Greiner et al., 2023; Guo & Wang, 2023; Naidu & Sevnarayan, 2023; Ouh et al., 2023; Smolansky et al., 2023).”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Guo & Wang, 2023
In the citing paper
“This finding can be attributed to the specific and actionable nature of AI-generated feedback, which often focused on surface-level issues such as grammar, spelling, and vocabulary.”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo & Wang, 2024
In the citing paper
“In contrast, teacher-generated feedback is typically concise, formative, and context-sensitive, focusing on critical areas for improvement in a more targeted manner.”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo and Wang (2024)
In the citing paper
“Research by Guo and Wang (2024) and Yoon et al. (2023) highlights that AI-generated feedback tends to be lengthier and more overwhelming, often containing redundant or overly generic suggestions.”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo and Wang (2024)
In the citing paper
“In contrast, teacher-generated feedback tended to focus selectively on content-specific and language-specific aspects, with a preference for indirect feedback types, such as 'informative' or 'querying' remarks.”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo and Wang (2024)
In the citing paper
“Their findings revealed that while AI-generated feedback was greater in volume and primarily direct, it was evenly distributed across content, language, and organizational issues.”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo and Wang (2024)
In the citing paper
“In contrast, AI-generated feedback demonstrates a more consistent distribution across local and global issues (Guo & Wang, 2024) as well as a high agreement with instructors (Dai et al., 2023)”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo & Wang, 2024
In the citing paper
“In contrast, teacher-generated feedback offers more contextualized, selective insights tailored to both content-specific and language-specific aspects of students' work (Guo & Wang, 2024; Yoon et al., 2023).”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo & Wang, 2024
In the citing paper
“According to the literature, AI-generated feedback provides a greater volume of direct, concise, and formative suggestions, often focused on surface-level issues like grammar and vocabulary.”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo & Wang, 2024
In the citing paper
“The slight advantage of the AIGFTGF order can be attributed to the nature of AI-generated feedback, which is particularly effective at addressing surface-level issues, such as grammar, vocabulary, and basic organization (Guo & Wang, 2024; Lin & Crosthwaite, 2024; Yoon et al., 2023).”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Guo & Wang, 2024
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“In the realm of education, researchers have been discussing ChatGPT’s implications for teaching and learning (Garcia-Peñalvo, 2023; Rudolph et al., 2023; Zhai, 2022) and exploring its potential use in various subjects, such as journalism and media (Pavlik, 2023), medicine (Gilson et al., 2022; Khan…”
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“…ChatGPT’s implications for teaching and learning (Garcia-Peñalvo, 2023; Rudolph et al., 2023; Zhai, 2022) and exploring its potential use in various subjects, such as journalism and media (Pavlik, 2023), medicine (Gilson et al., 2022; Khan et al., 2023), and engineering education (Qadir, 2022).”
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“In the future, researchers may pay attention to the importance of prompt programming (Reynolds & McDonell, 2021) and attempt diverse inputs and compare their output results to identify effective ways to use ChatGPT for performing the desired task of feedback generation.”
ChatGPT User Experience: Implications for Education
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“…realm of education, researchers have been discussing ChatGPT’s implications for teaching and learning (Garcia-Peñalvo, 2023; Rudolph et al., 2023; Zhai, 2022) and exploring its potential use in various subjects, such as journalism and media (Pavlik, 2023), medicine (Gilson et al., 2022; Khan et…”
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Singh, R. G., Chow, K. M., Dong, H., Wong, H., Fung, M., Myers, P. S., Wong, L. L. C., Lai, P., Ngai, C. S. B., Koon, A. C., et al. (2026) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2026.101729 · Cited by 2
Beyond grammar: How ChatGPT-4.0 shapes writing self-efficacy and performance in CSL writing
Huang, G., & Chen, H. (2026) · Language learning & technology · doi:10.64152/10125/73706 · Cited by 1
Metadata
- Authors
- Guo, K., & Wang, D.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Wang, Deliang
- Venue
- Education and Information Technologies
- Published
- 2023-08-29
- Volume
- 29
- Issue
- 7
- Pages
- 8435-8463
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4386253646
- Cited by
- 449
- References
- 51
- Citations by year
- 2026: 191 · 2025: 166 · 2024: 89 · 2023: 3
- FWCI
- 503.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Education and Information Technologies (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › English Language Learning and Teaching
- Keywords
- ChatGPT, large language models, teacher feedback, EFL writing, argumentative writing, automated feedback, teacher perceptions, Chinese undergraduates, writing development, teacher-AI collaboration
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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