View in EDS HTML Full Text PDF Full Text

Generative AI offers more, but students revise less: comparing the effects of teacher and AI feedback on student essay revisions.

Saved in:
Bibliographic Details
Title: Generative AI offers more, but students revise less: comparing the effects of teacher and AI feedback on student essay revisions.
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]
Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ehh
DbLabel: Education Research Complete
An: 191500574
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Generative AI offers more, but students revise less: comparing the effects of teacher and AI feedback on student essay revisions.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Farrokhnia%2C+Mohammadreza%22">Farrokhnia, Mohammadreza</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> m.farrokhnia@utwente.nl</i><br /><searchLink fieldCode="AR" term="%22Latifi%2C+Saeed%22">Latifi, Saeed</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> saeed.latifi@khu.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Papadopoulos%2C+Pantelis+M%2E%22">Papadopoulos, Pantelis M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> p.m.papadopoulos@utwente.nl</i><br /><searchLink fieldCode="AR" term="%22Hogenkamp%2C+Loes%22">Hogenkamp, Loes</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> l.hogenkamp@utwente.nl</i><br /><searchLink fieldCode="AR" term="%22Gijlers%2C+Hannie%22">Gijlers, Hannie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> a.h.gijlers@utwente.nl</i><br /><searchLink fieldCode="AR" term="%22Khosravi%2C+Hassan%22">Khosravi, Hassan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> h.khosravi@uq.edu.au</i><br /><searchLink fieldCode="AR" term="%22Noroozi%2C+Omid%22">Noroozi, Omid</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> omid.noroozi@wur.nl</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 2/10/2026, Vol. 23 Issue 1, p1-28. 28p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Revision+%28Writing+process%29%22">Revision (Writing process)</searchLink><br />*<searchLink fieldCode="DE" term="%22Psychological+feedback%22">Psychological feedback</searchLink><br /><searchLink fieldCode="DE" term="%22Essays%22">Essays</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=191500574
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1186/s41239-026-00579-9
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 1
    Subjects:
      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: Revision (Writing process)
        Type: general
      – SubjectFull: Psychological feedback
        Type: general
      – SubjectFull: Essays
        Type: general
    Titles:
      – TitleFull: Generative AI offers more, but students revise less: comparing the effects of teacher and AI feedback on student essay revisions.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Farrokhnia, Mohammadreza
      – PersonEntity:
          Name:
            NameFull: Latifi, Saeed
      – PersonEntity:
          Name:
            NameFull: Papadopoulos, Pantelis M.
      – PersonEntity:
          Name:
            NameFull: Hogenkamp, Loes
      – PersonEntity:
          Name:
            NameFull: Gijlers, Hannie
      – PersonEntity:
          Name:
            NameFull: Khosravi, Hassan
      – PersonEntity:
          Name:
            NameFull: Noroozi, Omid
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 10
              M: 02
              Text: 2/10/2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 23659440
          Numbering:
            – Type: volume
              Value: 23
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: International Journal of Educational Technology in Higher Education
              Type: main
ResultId 1