AI-generated feedback on writing: insights into efficacy and ENL student preference
Open accessAbstract
The question of how generative AI tools, such as large language models and chatbots, can be leveraged ethically and effectively in education is ongoing. Given the critical role that writing plays in learning and assessment within educational institutions, it is of growing importance for educators to make thoughtful and informed decisions as to how and in what capacity generative AI tools should be leveraged to assist in the development of students’ writing skills. This paper reports on two longitudinal studies. Study 1 examined learning outcomes of 48 university English as a new language (ENL) learners in a six-week long repeated measures quasi experimental design where the experimental group received writing feedback generated from ChatGPT (GPT-4) and the control group received feedback from their human tutor. Study 2 analyzed the perceptions of a different group of 43 ENLs who received feedback from both ChatGPT and their tutor. Results of study 1 showed no difference in learning outcomes between the two groups. Study 2 results revealed a near even split in preference for AI-generated or human-generated feedback, with clear advantages to both forms of feedback apparent from the data. The main implication of these studies is that the use of AI-generated feedback can likely be incorporated into ENL essay evaluation without affecting learning outcomes, although we recommend a blended approach that utilizes the strengths of both forms of feedback. The main contribution of this paper is in addressing generative AI as an automatic essay evaluator while incorporating learner perspectives.
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Students considered AI-generated feedback more readable and detailed compared to human-generated feedback.
“This explanation is supported by a study from Escalante, Pack, and Barrett (2023) in which students considered AI-generated feedback more 'readable and detailed' compared to human-generated feedback.”
From Summarise, elaborate, try again: exploring the effect of feedback literacy on AI-enhanced essay writing (2026) · cites this paper as Escalante, Pack, and Barrett (2023)
In Study 2 (Relating to RQ2), students who preferred AI feedback cited its clarity, ease of understanding, and detailed comments; one contrasted its extensive feedback with tutors’ limited time. But the Discussion and conclusion attributes the exact finding that AI feedback was more “readable and detailed” than instructor feedback to Dai et al. (2023), while this study found preferences were roughly evenly split. The citing sentence overstates that comparison as a finding about Escalante et al.’s students generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredCombining AI-driven feedback with human input may provide comprehensive guidance tailored to individual needs.
“Combining AI-driven feedback with human input may provide comprehensive guidance tailored to individual needs [142].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [142]
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A systematic review of AI-based feedback in educational settings
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Pedagogical framework for hybrid intelligent feedback
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How understanding the limitations and risks of using ChatGPT can contribute to willingness to use
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Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence in Teaching
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Generative AI and L2 Written Feedback Studies: A Scoping Review
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The Role of ChatGPT in Education: Applications, Challenges: Insights From a Systematic Review
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The Impact of <scp>ChatGPT</scp> on Students' Academic Achievement: A Meta‐Analysis
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Evaluating large language models for criterion-based grading from agreement to consistency
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Revisiting Trends in GenAI-Assisted Second Language Writing: Retrospect and Prospect
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Navigating the Ethical Terrain of AI-Generated Text Tools: A Review
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AI-assisted marking: Functionality and limitations of ChatGPT in written assessment evaluation
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Teachers' Perspectives on Integrating ChatGPT into EFL Writing Instruction
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The role of AI assisted writing feedback in developing secondary students writing skills
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ChatGPT’s impact on student learning outcomes: a meta-analysis of 35 experimental studies
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The perceived impact of artificial intelligence on academic learning
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Generative AI and Literacy Development in the Language Classroom: A Systematic Review of Literature
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- Authors
- Escalante, J., Pack, A., & Barrett, A.
- Affiliations
- Brigham Young University–Hawaii (US)
- Florida State University (US)
- Corresponding author
- Escalante, Juan
- Venue
- International Journal of Educational Technology in Higher Education
- Published
- 2023-10-27
- Volume
- 20
- Issue
- 1
- ISSN
- 2365-9440 (ISSN-L)
- OpenAlex
- W4387954002
- Cited by
- 589
- References
- 38
- Citations by year
- 2026: 267 · 2025: 231 · 2024: 90 · 2023: 1
- FWCI
- 138.70
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- AI-generated feedback, generative AI in education, ChatGPT, large language models, writing instruction, learner preferences, writing learning outcomes
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.
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