Teacher, peer, or AI? Comparing effects of feedback sources in higher education
Open accessAbstract
With the emergence of Large Language Models (LLMs), AI-generated feedback is gaining traction as a scalable feedback source for higher education. To qualify as viable alternatives for higher education classrooms, its relative effectiveness compared to teacher feedback and peer feedback needs to be better understood. To this end, a randomized field experiment ( N = 90) compared the effects of three feedback sources—teacher, peer, and LLM—on students’ feedback perceptions and their achievement. To eliminate potential confounds, we (a) controlled for learning gains that may result from students giving feedback to their peers and (b) blinded feedback sources from feedback recipients. Results showed that students rated teacher feedback as less fair and harder to accept than peer and LLM feedback. Students receiving teacher feedback also indicated less willingness to revise their work based on the feedback. Conversely, teacher feedback produced the strongest improvements in scientific argumentation and formal quality of students’ work. Here, LLM feedback yielded the smallest improvement overall. Lastly, feedback literacy and intrinsic motivation partly moderated feedback effects on perceptions and achievement outcomes. For example, students with more productive attitudes toward feedback achieved higher argumentation quality after receiving teacher feedback than their peers. Findings indicate that the impact of feedback on both student perceptions and performance depends on the interplay between the feedback source and learner dispositions; deliberately aligning these factors could therefore amplify the benefits of feedback interventions. Future research should explore hybrid and adaptive feedback models that integrate human and AI input.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior
Deci, E. L., & Ryan, R. M. (2000) · Psychological Inquiry · doi:10.1207/s15327965pli1104_01 · Cited by 34,210 · see citing works
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · see citing works
Nicol, D. J., & Macfarlane‐Dick, D. (2006) · Studies in Higher Education · doi:10.1080/03075070600572090 · Cited by 5,846 · see citing works
Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631 · see citing works
Developing the theory of formative assessment
Black, P., & Wiliam, D. (2009) · Educational Assessment, Evaluation and Accountability · doi:10.1007/s11092-008-9068-5 · Cited by 3,301 · see citing works
The development of student feedback literacy: Enabling uptake of feedback
Carless, D., & Boud, D. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1463354 · Cited by 2,428 · see citing works
Rethinking models of feedback for learning: the challenge of design
Boud, D., & Molloy, E. (2013) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2012.691462 · Cited by 1,636 · see citing works
Self-Regulated Learning: Beliefs, Techniques, and Illusions
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013) · Annual Review of Psychology · doi:10.1146/annurev-psych-113011-143823 · Cited by 1,468 · see citing works
The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research
Wisniewski, B., Zierer, K., & Hattie, J. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03087 · Cited by 1,199 · see citing works
Student Motivation and Associated Outcomes: A Meta-Analysis From Self-Determination Theory
Howard, J. L., Bureau, J., Guay, F., Chong, J. X. Y., & Ryan, R. M. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620966789 · Cited by 1,001 · see citing works
Winstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017) · Educational Psychologist · doi:10.1080/00461520.2016.1207538 · Cited by 884 · see citing works
To give is better than to receive: The benefits of peer review to the reviewer's own writing
Lundstrom, K., & Baker, W. (2009) · Journal of Second Language Writing · doi:10.1016/j.jslw.2008.06.002 · Cited by 843 · see citing works
Stratified Randomization for Clinical Trials
Kernan, W. N., Viscoli, C. M., Makuch, R. W., Brass, L. M., & Horwitz, R. I. (1999) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(98)00138-3 · Cited by 627 · see citing works
The nature of feedback: How different types of peer feedback affect writing performance
Nelson, M. M., & Schunn, C. D. (2009) · Instructional Science · doi:10.1007/s11251-008-9053-x · Cited by 602 · see citing works
Developing a learning-centred framework for feedback literacy
Molloy, E., Boud, D., & Henderson, M. (2020) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1667955 · Cited by 555 · see citing works
Comparing the quality of human and ChatGPT feedback of students’ writing
Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., Moon, Y., Tseng, W., Warschauer, M., & Olson, C. B. (2024) · Learning and Instruction · doi:10.1016/j.learninstruc.2024.101894 · Cited by 509 · see citing works
The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies
Double, K. S., McGrane, J. A., & Hopfenbeck, T. N. (2020) · Educational Psychology Review · doi:10.1007/s10648-019-09510-3 · Cited by 446 · see citing works
A review of feedback models and typologies: Towards an integrative model of feedback elements
Panadero, E., & Lipnevich, A. A. (2022) · Educational Research Review · doi:10.1016/j.edurev.2021.100416 · Cited by 389 · see citing works
The challenges of feedback in higher education
Henderson, M., Ryan, T., & Phillips, M. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1599815 · Cited by 335 · see citing works
Huisman, B., Saab, N., van den Broek, P., & van Driel, J. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1545896 · Cited by 306 · see citing works
A Meta-Analysis of Negative Feedback on Intrinsic Motivation
Fong, C. J., Patall, E. A., Vasquez, A. C., & Stautberg, S. (2019) · Educational Psychology Review · doi:10.1007/s10648-018-9446-6 · Cited by 299 · see citing works
van Gennip, N. A. E., Segers, M. S. R., & Tillema, H. H. (2010) · Learning and Instruction · doi:10.1016/j.learninstruc.2009.08.010 · Cited by 259 · see citing works
Panadero, E., & Alqassab, M. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1600186 · Cited by 232 · see citing works
AcaWriter: A learning analytics tool for formative feedback on academic writing
Knight, S., Shibani, A., Abel, S., Gibson, A., & Ryan, P. (2020) · Journal of Writing Research · doi:10.17239/jowr-2020.12.01.06 · Cited by 151 · see citing works
Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness
Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2502582 · Cited by 145 · see citing works
Rotsaert, T., Panadero, E., & Schellens, T. (2018) · European Journal of Psychology of Education · doi:10.1007/s10212-017-0339-8 · Cited by 126 · see citing works
Assessing student perceptions and use of instructor versus <scp>AI</scp> ‐generated feedback
Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13558 · Cited by 114 · see citing works
Dai, W., Tsai, Y.-S., Lin, J., Aldino, A., Jin, H., Li, T., Gašević, D., & Chen, G. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100299 · Cited by 93 · see citing works
Gao, Y., Schunn, C. D. D., & Yu, Q. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1499075 · Cited by 71 · see citing works
SycEval: Evaluating LLM Sycophancy
Fanous, A., Goldberg, J., Agarwal, A., Lin, J., Zhou, A., Xu, S., Bikia, V., Daneshjou, R., & Koyejo, S. (2025) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v8i1.36598 · Cited by 62 · see citing works
What aspects of online peer feedback robustly predict growth in students' task performance?
Zong, Z., Schunn, C. D., & Wang, Y. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106924 · Cited by 61 · see citing works
Chein, J., Martinez, S., & Barone, A. (2024) · doi:10.21203/rs.3.rs-4277893/v1 · Cited by 38 · see citing works
Flagged first, then by citations
- Authors
- Weidlich, J., Gotsch, F., Schudel, K., Marusic-Würscher, C., Mazzarella, J., Bolten, H., Bütler, D., Luger, S., Wohlfender, B., & Merki, K. M.
- Affiliations
- University of Zurich (CH)
- Corresponding author
- Weidlich, Joshua
- Venue
- Computers and Education Open
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 9
- Pages
- 100300
- ISSN
- 2666-5573 (ISSN-L)
- OpenAlex
- W4415317129
- Cited by
- 36
- References
- 32
- Citations by year
- 2026: 36
- FWCI
- 111.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Open (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- 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
- feedback source, teacher feedback, peer feedback, AI-generated feedback, large language models, feedback perception, academic performance, feedback literacy, intrinsic motivation, scientific argumentation, higher education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
