To resist it or to embrace it? Examining ChatGPT’s potential to support teacher feedback in EFL writing
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Literature suggests complementary configurations where AI complements rather than replaces teacher feedback.
“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].”
With GenAI, students may be able to complete tasks for which they lack necessary skills, such as creating photographs and getting ideas from other disciplines.
“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
AI has the potential to lessen individual biases in grading procedures, including essays, project proposals, and theses.
“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
Various assignment types, including essays and computer coding, are impacted differently by GenAI.
“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
AI-generated feedback is specific and actionable, often focused on surface-level issues such as grammar, spelling, and vocabulary.
“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
Teacher-generated feedback is typically concise, formative, and context-sensitive, focusing on critical areas for improvement in a more targeted manner.
“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)
AI-generated feedback tends to be lengthier and more overwhelming, often containing redundant or overly generic suggestions.
“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)
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.
“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)
AI-generated feedback was greater in volume and primarily direct, and evenly distributed across content, language, and organizational issues.
“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)
AI-generated feedback demonstrates a more consistent distribution across local and global issues as well as a high agreement with instructors.
“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
Teacher-generated feedback offers more contextualized, selective insights tailored to both content-specific and language-specific aspects of students' work.
“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
AI-generated feedback provides a greater volume of direct, concise, and formative suggestions, often focused on surface-level issues like grammar and vocabulary.
“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
The slight advantage of the AIGF-TGF 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.
“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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- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › 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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