Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback.
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| Title: | Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback. |
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| Authors: | Yilmaz, Mutluhan1,2 (AUTHOR) dtnvyil@ucl.ac.uk, Temur, Hilmi Bahadir3,4 (AUTHOR) hilmibahadir.temur@giresun.edu.tr, Emmungil, Levent2 (AUTHOR) levent.emmungil@ostimteknik.edu.tr, Çelik, Embiya5 (AUTHOR) embiya@atauni.edu.tr, Gauthier, Andrea1 (AUTHOR) qtnvgau@ucl.ac.uk, Cukurova, Mutlu1 (AUTHOR) utnvmcu@ucl.ac.uk |
| Source: | International Journal of Educational Technology in Higher Education. 4/30/2026, Vol. 23 Issue 1, p1-25. 25p. |
| Subject Terms: | *Self-regulated learning, *Generative artificial intelligence, *Study skills, *Learning analytics, *Online education, *Psychological feedback, *Psychology of students, Psychological techniques |
| Abstract: | Given the increased autonomy and limited direct instructor presence in online learning settings necessitating advanced self-regulated learning skills (SRLs), many online students face significant learning difficulties, such as low course completion rates. External support, particularly in the form of feedback, is crucial in addressing these difficulties. However, providing timely and personalised feedback at scale remains a key challenge. This study examines the effects of generative artificial intelligence (GenAI) feedback on students' SRLs in an online higher education context. The study compares GenAI feedback, informed by student trace data and learning analytics, with tutor-generated feedback, assessing both student perceptions of feedback and their SRLs development. Employing a mixed-methods research design, we collected survey data, trace-based SRL indicators, and open-ended responses from 46 university students. Quantitative findings revealed that students rated GenAI feedback more positively than tutor-generated feedback, with a statistically significant difference in Genuineness. Moreover, the treatment group showed significant improvement in the Task Strategies dimension of SRLs, suggesting that personalised GenAI feedback may enhance specific regulatory behaviours. Qualitative insights revealed varying student awareness of the feedback source, with some prioritising content over the source of feedback. In contrast, others indicated their attitudes might have differed had they known the provider, highlighting the socio-emotional aspects of feedback adoption and impact. This study concludes that GenAI has the potential to scale SRLs feedback in online learning environments, but the student perceptions of feedback should be carefully managed to observe the expected impact. [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 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 193494061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yilmaz%2C+Mutluhan%22">Yilmaz, Mutluhan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> dtnvyil@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Temur%2C+Hilmi+Bahadir%22">Temur, Hilmi Bahadir</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> hilmibahadir.temur@giresun.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Emmungil%2C+Levent%22">Emmungil, Levent</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> levent.emmungil@ostimteknik.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Çelik%2C+Embiya%22">Çelik, Embiya</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> embiya@atauni.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Gauthier%2C+Andrea%22">Gauthier, Andrea</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qtnvgau@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Cukurova%2C+Mutlu%22">Cukurova, Mutlu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> utnvmcu@ucl.ac.uk</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>. 4/30/2026, Vol. 23 Issue 1, p1-25. 25p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Self-regulated+learning%22">Self-regulated learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Study+skills%22">Study skills</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br />*<searchLink fieldCode="DE" term="%22Psychological+feedback%22">Psychological feedback</searchLink><br />*<searchLink fieldCode="DE" term="%22Psychology+of+students%22">Psychology of students</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+techniques%22">Psychological techniques</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Given the increased autonomy and limited direct instructor presence in online learning settings necessitating advanced self-regulated learning skills (SRLs), many online students face significant learning difficulties, such as low course completion rates. External support, particularly in the form of feedback, is crucial in addressing these difficulties. However, providing timely and personalised feedback at scale remains a key challenge. This study examines the effects of generative artificial intelligence (GenAI) feedback on students' SRLs in an online higher education context. The study compares GenAI feedback, informed by student trace data and learning analytics, with tutor-generated feedback, assessing both student perceptions of feedback and their SRLs development. Employing a mixed-methods research design, we collected survey data, trace-based SRL indicators, and open-ended responses from 46 university students. Quantitative findings revealed that students rated GenAI feedback more positively than tutor-generated feedback, with a statistically significant difference in Genuineness. Moreover, the treatment group showed significant improvement in the Task Strategies dimension of SRLs, suggesting that personalised GenAI feedback may enhance specific regulatory behaviours. Qualitative insights revealed varying student awareness of the feedback source, with some prioritising content over the source of feedback. In contrast, others indicated their attitudes might have differed had they known the provider, highlighting the socio-emotional aspects of feedback adoption and impact. This study concludes that GenAI has the potential to scale SRLs feedback in online learning environments, but the student perceptions of feedback should be carefully managed to observe the expected impact. [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=193494061 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s41239-026-00592-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1 Subjects: – SubjectFull: Self-regulated learning Type: general – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Study skills Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Online education Type: general – SubjectFull: Psychological feedback Type: general – SubjectFull: Psychology of students Type: general – SubjectFull: Psychological techniques Type: general Titles: – TitleFull: Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yilmaz, Mutluhan – PersonEntity: Name: NameFull: Temur, Hilmi Bahadir – PersonEntity: Name: NameFull: Emmungil, Levent – PersonEntity: Name: NameFull: Çelik, Embiya – PersonEntity: Name: NameFull: Gauthier, Andrea – PersonEntity: Name: NameFull: Cukurova, Mutlu IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 04 Text: 4/30/2026 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 |
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