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Cited by 26Student Assessment and FeedbackArtificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive Learning

Who Gives Feedback Matters: Student Biases Towards Human and AI ‐Generated Formative Feedback

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Nazaretsky, Tanya, Mejia‐Domenzain, Paola, Swamy, Vinitra, Frej, Jibril, Käser, Tanja·2026·Journal of Computer Assisted Learning
DOI 10.1111/jcal.70153
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Abstract

Background: Feedback is essential for learning, helping individuals understand and improve their performance. However, providing timely, personalised feedback in higher education is challenging. Generative AI offers a scalable solution, yet little is known about students' biases towards AI‐generated feedback. Objectives: This study aims to investigate how the identity of the feedback provider (human vs. AI) affects students' perceptions of feedback quality and credibility. Methods: The study involved 472 students across diverse academic programmes and levels in authentic educational environments and employed a within‐subject experimental design with a priming effect. A mixed‐methods approach combined quantitative analysis of feedback evaluations with qualitative insights into students' perceptions to deepen understanding of the observed biases. Results and Conclusions: Students perceived AI as a significantly less credible feedback provider and tended to associate lower feedback quality with AI. Disclosing the feedback provider's identity led to decreased evaluations of AI‐generated feedback and an increased preference for human‐crafted feedback. These patterns were consistent across academic levels, genders, and fields of study. These insights highlight the need for targeted interventions, such as improving AI literacy and building human‐in‐the‐loop systems, to mitigate biases and enhance the effectiveness of AI in educational feedback systems.

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  • Students' trust in AI, perceptions of feedback source, and AI literacy can influence whether they accept or act on AI-generated feedback comments.

    “This consideration is particularly relevant because students' trust in AI, perceptions of feedback source, and AI literacy can influence whether they accept or act on AI-generated feedback comments [15, 46].”

    From Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment (2026) · cites this paper as [15]

  • The literature shows a clear preference for educator-generated feedback comments over AI-generated feedback comments in terms of trust.

    “Whilst AI-generated feedback comments may be similar in form and quality to educator-generated comments, the literature shows a clear preference for educatorgenerated feedback comments in terms of trust [15].”

    From Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment (2026) · cites this paper as [15]

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Cited by

Sorted by citations

Authors
Nazaretsky, T., Mejia‐Domenzain, P., Swamy, V., Frej, J., & Käser, T.
Affiliations
École Polytechnique Fédérale de Lausanne (CH)
Corresponding author
Nazaretsky, Tanya
ORCID
Venue
Journal of Computer Assisted Learning
Publisher
Wiley
Published
2025-12-02
Volume
42
Issue
1
ISSN
  • 0266-4909 (ISSN-L)
  • 1365-2729
DOI
10.1111/jcal.70153
OpenAlex
W4416902917
Cited by
26
References
26
Citations by year
2026: 25 · 2025: 1
FWCI
81.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Computer Assisted Learning (publishedVersion, cc-by)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
Funders
Singapore Eye Research Institute
Topics
  • Social Sciences › Education › Student Assessment and Feedback
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
AI-generated feedback, student perceptions, feedback quality, feedback credibility, generative AI in education, formative feedback, higher education, AI literacy, human-in-the-loop systems, priming effect

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