Generative AI offers more, but students revise less: comparing the effects of teacher and AI feedback on student essay revisions.
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| Title: | Generative AI offers more, but students revise less: comparing the effects of teacher and AI feedback on student essay revisions. |
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| Authors: | Farrokhnia, Mohammadreza1 (AUTHOR) m.farrokhnia@utwente.nl, Latifi, Saeed2 (AUTHOR) saeed.latifi@khu.ac.ir, Papadopoulos, Pantelis M.1 (AUTHOR) p.m.papadopoulos@utwente.nl, Hogenkamp, Loes1 (AUTHOR) l.hogenkamp@utwente.nl, Gijlers, Hannie1 (AUTHOR) a.h.gijlers@utwente.nl, Khosravi, Hassan3 (AUTHOR) h.khosravi@uq.edu.au, Noroozi, Omid4 (AUTHOR) omid.noroozi@wur.nl |
| Source: | International Journal of Educational Technology in Higher Education. 2/10/2026, Vol. 23 Issue 1, p1-28. 28p. |
| Subject Terms: | *Generative artificial intelligence, *Revision (Writing process), *Psychological feedback, Essays |
| Abstract: | Providing high-quality feedback on student writing is essential yet increasingly difficult due to rising class sizes and limited instructional capacity. Generative AI (GenAI) offers a promising and scalable alternative, but its effectiveness compared to traditional teacher feedback, particularly across different prompting techniques, remains uncertain. This study employed a quantitative, randomized three-group experimental design with 70 graduate students to compare the effects of teacher feedback and GenAI feedback generated using two prompting techniques: Zero-shot and chain-of-thought (CoT). The study explored how these feedback sources affect feedback quality and students' uptake during essay revision. It involved a two-stage process in which students first wrote an argumentative essay and then revised it based on the feedback received. Feedback and essay quality were evaluated using a rubric based on Toulmin's model of argumentation and analysed using inferential statistical methods. Results showed that CoT prompting produced higher quality feedback than both Zero-shot prompting and teacher feedback, suggesting that stepwise reasoning in CoT aligns GenAI outputs more closely with the cognitive demands of argumentative writing. However, this higher feedback quality did not lead to significantly greater improvements in revisions. Teacher feedback, although rated lower in quality, resulted in comparable gains in essay quality. In addition, GenAI feedback quality was significantly associated with students' initial essay quality, whereas teacher feedback quality showed no such association. These findings indicate that feedback quality alone is insufficient to enhance writing outcomes; rather, students' engagement with and uptake of feedback play a critical role. Overall, the results highlight the potential of hybrid intelligent feedback systems in which teachers support students in interpreting and applying GenAI feedback to meaningfully improve their writing. [ABSTRACT FROM AUTHOR] |
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| Database: | Education Research Complete |
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| Abstract: | Providing high-quality feedback on student writing is essential yet increasingly difficult due to rising class sizes and limited instructional capacity. Generative AI (GenAI) offers a promising and scalable alternative, but its effectiveness compared to traditional teacher feedback, particularly across different prompting techniques, remains uncertain. This study employed a quantitative, randomized three-group experimental design with 70 graduate students to compare the effects of teacher feedback and GenAI feedback generated using two prompting techniques: Zero-shot and chain-of-thought (CoT). The study explored how these feedback sources affect feedback quality and students' uptake during essay revision. It involved a two-stage process in which students first wrote an argumentative essay and then revised it based on the feedback received. Feedback and essay quality were evaluated using a rubric based on Toulmin's model of argumentation and analysed using inferential statistical methods. Results showed that CoT prompting produced higher quality feedback than both Zero-shot prompting and teacher feedback, suggesting that stepwise reasoning in CoT aligns GenAI outputs more closely with the cognitive demands of argumentative writing. However, this higher feedback quality did not lead to significantly greater improvements in revisions. Teacher feedback, although rated lower in quality, resulted in comparable gains in essay quality. In addition, GenAI feedback quality was significantly associated with students' initial essay quality, whereas teacher feedback quality showed no such association. These findings indicate that feedback quality alone is insufficient to enhance writing outcomes; rather, students' engagement with and uptake of feedback play a critical role. Overall, the results highlight the potential of hybrid intelligent feedback systems in which teachers support students in interpreting and applying GenAI feedback to meaningfully improve their writing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 23659440 |
| DOI: | 10.1186/s41239-026-00579-9 |
