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The feedback conundrum: can Generative AI replace lecturers' feedback?

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Title: The feedback conundrum: can Generative AI replace lecturers' feedback?
Authors: Sheppick, Chloe1 (AUTHOR) chloe.sheppick@kcl.ac.uk, Butler, Michael1 (AUTHOR)
Source: Law Teacher. Jun2026, Vol. 60 Issue 2, p318-339. 22p.
Subject Terms: *Generative artificial intelligence, *Higher education, *Psychological feedback, *Evaluation methodology, *Legal education, *Learning, Lecturers, Artificial intelligence in education
Company/Entity: King's College London
Abstract: It has been argued that for feedback to be effective, it needs to be provided quickly, close to the act of learning production. However, increasing student numbers, and additional demands on lecturers, makes this goal look unachievable. A radical solution may be needed to combat this: could Generative AI provide useful feedback to students at speed? This paper explains how a Generative AI tool was built to produce feedback to students on the MSc in Law and Professional Practice at King's College London and examines the results of a study as to the advantages, and disadvantages, of using Generative AI to produce feedback. The results of the study have shown that while the students valued the speed of the feedback produced by Generative AI, and elements of its quality such as the specificity, structure and organisation of the feedback, ultimately they would view AI-generated feedback as a supplement, rather than a replacement, for lecturer feedback. [ABSTRACT FROM AUTHOR]
Copyright of Law Teacher is the property of Taylor & Francis Ltd 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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  Data: It has been argued that for feedback to be effective, it needs to be provided quickly, close to the act of learning production. However, increasing student numbers, and additional demands on lecturers, makes this goal look unachievable. A radical solution may be needed to combat this: could Generative AI provide useful feedback to students at speed? This paper explains how a Generative AI tool was built to produce feedback to students on the MSc in Law and Professional Practice at King's College London and examines the results of a study as to the advantages, and disadvantages, of using Generative AI to produce feedback. The results of the study have shown that while the students valued the speed of the feedback produced by Generative AI, and elements of its quality such as the specificity, structure and organisation of the feedback, ultimately they would view AI-generated feedback as a supplement, rather than a replacement, for lecturer feedback. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Law Teacher is the property of Taylor & Francis Ltd 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.)
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        Value: 10.1080/03069400.2026.2619364
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      – Code: eng
        Text: English
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      – SubjectFull: Psychological feedback
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      – SubjectFull: Evaluation methodology
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      – SubjectFull: Legal education
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      – SubjectFull: Artificial intelligence in education
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      – SubjectFull: King's College London
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              M: 06
              Text: Jun2026
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              Y: 2026
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