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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
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…”
The effect of different types of corrective feedback on ESL student writing
Bitchener, J., Young, S., & Cameron, D. (2005) · Journal of Second Language Writing · doi:10.1016/j.jslw.2005.08.001 · Cited by 900
A comparative study of peer and teacher feedback in a Chinese EFL writing class
Yang, M., Badger, R., & Yu, Z. (2006) · Journal of Second Language Writing · doi:10.1016/j.jslw.2006.09.004 · Cited by 703
ChatGPT - Reshaping medical education and clinical management
Khan, R. A., Jawaid, M., Khan, A. R., & Sajjad, M. (2023) · Pakistan Journal of Medical Sciences · doi:10.12669/pjms.39.2.7653 · Cited by 628
The influence of teacher commentary on student revision
Ferris, D. R. (1997) · TESOL Quarterly · doi:10.2307/3588049 · Cited by 618 · cited 2×
“Including more details in feedback has been found to be more helpful than offering general comments only (Ferris, 1997).”
The nature of feedback: How different types of peer feedback affect writing performance
Nelson, M. M., & Schunn, C. D. (2009) · Instructional Science · doi:10.1007/s11251-008-9053-x · Cited by 602
Handbook of Automated Essay Evaluation
(2013) · Routledge eBooks · doi:10.4324/9780203122761 · Cited by 518
“What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter
Taecharungroj, V. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010035 · Cited by 489
Koltovskaia, S. (2020) · Assessing Writing · doi:10.1016/j.asw.2020.100450 · Cited by 447
Rethinking the role of automated writing evaluation (AWE) feedback in ESL writing instruction
Li, J., Link, S., & Hegelheimer, V. H. (2014) · Journal of Second Language Writing · doi:10.1016/j.jslw.2014.10.004 · Cited by 362
García-Peñalvo, F. J. (2023) · Education in the Knowledge Society (EKS) · doi:10.14201/eks.31279 · Cited by 331
Wilson, J., & Czik, A. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.05.004 · Cited by 297
The effects of computer-generated feedback on the quality of writing
Stevenson, M., & Phakiti, A. (2014) · Assessing Writing · doi:10.1016/j.asw.2013.11.007 · Cited by 275
Link, S., Mehrzad, M., & Rahimi, M. (2022) · Computer Assisted Language Learning · doi:10.1080/09588221.2020.1743323 · Cited by 275
Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education
Qadir, J. (2022) · doi:10.36227/techrxiv.21789434.v1 · Cited by 264
Automated Writing Assessment in the Classroom
Warschauer, M., & Grimes, D. (2008) · Pedagogies: An International Journal · doi:10.1080/15544800701771580 · Cited by 243
Utility in a Fallible Tool: A Multi-Site Case Study of Automated Writing Evaluation.
Grimes, D., & Warschauer, M. (2010) · The Journal of Technology, Learning and Assessment (Boston College) · Cited by 241
Automated essay evaluation: the criterion online writing service
Burstein, J. C., Chodorow, M. S., & Leacock, C. (2004) · doi:10.1609/aimag.v25i3.1774 · Cited by 240
Cho, K., Schunn, C. D., & Charney, D. (2006) · Written Communication · doi:10.1177/0741088306289261 · Cited by 235
Zhu, M., Liu, O. L., & Lee, H.-S. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103668 · Cited by 229
Automated written corrective feedback: how well can students make use of it?
Ranalli, J. (2018) · Computer Assisted Language Learning · doi:10.1080/09588221.2018.1428994 · Cited by 228 · cited 1×
“…et al., 2020), and Pigai (Geng & Razali, 2020), have been developed, which have helped increase teachers’ feedback, expedite the process of providing feedback (Wilson & Czik, 2016), accelerate the practice–feed-back loop (Kellogg et al., 2010), and lessen teachers’ feedback burden (Ranalli, 2018).”
A review of AWE feedback: types, learning outcomes, and implications
Fu, Q.-K., Zou, D., Xie, H., & Cheng, G. (2022) · Computer Assisted Language Learning · doi:10.1080/09588221.2022.2033787 · Cited by 210
In the face of fallible AWE feedback: how do students respond?
Bai, L., & Hu, G. (2016) · Educational Psychology · doi:10.1080/01443410.2016.1223275 · Cited by 209
The debate on automated essay grading
Hearst, M. A. (2000) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.889104 · Cited by 176
The Effectiveness of feedback for L1-English and L2-writing development: a meta-analysis.
Biber, D., Nekrasova, T., & Horn, B. (2011) · ETS Research Report Series · doi:10.1002/j.2333-8504.2011.tb02241.x · Cited by 175
Gilson, A., Safranek, C., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2022) · doi:10.1101/2022.12.23.22283901 · Cited by 172
Hyland, K. (1990) · ELT Journal · doi:10.1093/elt/44.4.279 · Cited by 162
Wilson, J., Ahrendt, C., Fudge, E. A., Raiche, A., Beard, G., & MacArthur, C. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104208 · Cited by 118 · cited 1×
“…words, teachers’ perceptions will influence whether an AWE technology is appropriately utilised Education and Information Technologies (2024) 29:8435–8463 for classroom instruction and provide an important source of evidence when examining the efficacy of that technology (Wilson et al., 2021).”
Enhancing argumentative writing with automated feedback and social comparison nudging
Wambsganß, T., Janson, A., & Leimeister, J. M. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104644 · Cited by 93
McMartin-Miller, C. (2013) · Assessing Writing · doi:10.1016/j.asw.2013.11.003 · Cited by 89
Does Automated Feedback Help Students Learn to Write?
Kellogg, R. T., Whiteford, A. P., & Quinlan, T. (2010) · Journal of Educational Computing Research · doi:10.2190/ec.42.2.c · Cited by 88
Stevenson, M. (2016) · Computers & composition/Computers and composition · doi:10.1016/j.compcom.2016.05.001 · Cited by 85
Progress feedback effects on students' writing mastery goal, self-efficacy beliefs, and performance
Duijnhouwer, H., Prins, F. J., & Stokking, K. M. (2010) · Educational Research and Evaluation · doi:10.1080/13803611003711393 · Cited by 82
eRevis(ing): Students’ revision of text evidence use in an automated writing evaluation system
Wang, E. L., Matsumura, L. C., Correnti, R. J., Litman, D. J., Zhang, H., Howe, E., Magooda, A., & Quintana, R. (2020) · Assessing Writing · doi:10.1016/j.asw.2020.100449 · Cited by 73
Implementation and Applications of the Intelligent Essay Assessor
Foltz, P. W., Streeter, L. A., Lochbaum, K. E., & Landauer, T. K. (2015) · Routledge eBooks · doi:10.4324/9780203122761.ch5 · Cited by 54
Feedback in writing: Issues and challenges
Lee, I. (2013) · Assessing Writing · doi:10.1016/j.asw.2013.11.009 · Cited by 47
Jingxin, G., & Razali, A. B. (2020) · Universal Journal of Educational Research · doi:10.13189/ujer.2020.082638 · Cited by 37
The Effect of Praise upon the Quality and Quantity Of Creative Writing
Taylor, W. F., & Hoedt, K. C. (1966) · The Journal of Educational Research · doi:10.1080/00220671.1966.10883440 · Cited by 36
Investigating a collaborative approach to peer feedback
Alshuraidah, A., & Storch, N. (2019) · ELT Journal · doi:10.1093/elt/ccy057 · Cited by 29
Written feedback and scoring of sixth-grade girls’ and boys’ narrative and persuasive writing
Peterson, S. S., Childs, R. A., & Kennedy, K. (2004) · Assessing Writing · doi:10.1016/j.asw.2004.07.002 · Cited by 15
Exploring a Collaborative Approach to Peer Feedback in EFL Writing: How Do Students Participate?
Guo, K., Chen, X., & Qiao, S. (2022) · RELC Journal · doi:10.1177/00336882221143192 · Cited by 14
Self-citationDistributed collaborative writing
Neuwirth, C. M., Chandh, R., Charney, D., Wojahn, P. G., & Kim, L. (1994) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/259963.260285 · Cited by 11
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
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
