The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
To resist it or to embrace it? Examining ChatGPT’s potential to support teacher feedback in EFL writing
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Citations
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
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
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
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
ChatGPT: five priorities for research
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023) · Nature · doi:10.1038/d41586-023-00288-7 · Cited by 1,842 · cited 1×
“…human-like responses to users’ prompts in a conversational manner, acting as a chatbot, and has become a versatile tool with a range of applications, such as composing poetry, commenting on news, debugging code, writing essays, and summarising literature (Taecharungroj, 2023; van Dis et al., 2023).”
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 2×
“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…”
Pavlik, J. V. (2023) · Journalism & Mass Communication Educator · doi:10.1177/10776958221149577 · Cited by 972 · cited 1×
“…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).”
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952 · cited 1×
“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
Zhai, X. (2023) · doi:10.2139/ssrn.4312418 · Cited by 935 · cited 1×
“…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…”
Research works printed but not linked: 2
- Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. Retrieved 15 January 2023 from https://life-extension.github.io/2020/05/27/GPT%E6%8A%80%E6%9C%AF%E5%88%9D%E6%8E%A2/language-models.pdf
- Stiennon, N., Ouyang, L., Wu, J., Ziegler, D. M., Lowe, R., Voss, C., Radford, A., Amodei, D., & Christiano, P. (2020). Learning to summarize from human feedback. arXiv preprint arXiv:2009.01325. · arXiv
Cited by
Sorted by citations
A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00468-z · Cited by 493
Karataş, F., Abedi, F. Y., Ozek Gunyel, F., Karadeniz, D., & Kuzgun, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12574-6 · Cited by 252
The use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach
Mai, D. T. T., da, C. A. N., & Hanh, N. V. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1328769 · Cited by 363
Koltovskaia, S., Rahmati, P., & Saeli, H. (2024) · Journal of Second Language Writing · doi:10.1016/j.jslw.2024.101130 · Cited by 241
Teng, M. F. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100270 · Cited by 182
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
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