AI assistance in peer feedback provision: Pedagogically sound, but minimally adopted
Open accessAbstract
Engaging students in peer feedback offers significant learning benefits by promoting collaboration, critical thinking, and skill development. However, challenges persist because many students struggle to provide constructive and actionable feedback due to gaps in disciplinary knowledge and pedagogical skills. This study investigates whether Generative AI (GenAI) can help address these challenges by supporting students in delivering high-quality peer feedback. To examine this potential, we implemented human-led, GenAI-powered assistance for feedback provision in an educational platform using structured prompt instructions and a tailored user interface. We then examined the characteristics of this AI assistance (AI-A) and how students engaged with it during peer feedback. Our analysis draws on data from 433 students who engaged in 7670 instances of peer feedback supported by AI assistance as part of a large undergraduate semester-long course. Our results indicate that AI-A was typically positive, well structured, and focused on pedagogical strengths (79% of all instances). However, uptake remained relatively low, with just 9% (690) of AI assistance suggestions leading to revisions. AI-A that was less accurate, less complete in describing the original peer feedback, or less positively worded was more likely to prompt revision, with small-to-medium effect sizes. One possible explanation is that these instances drew students’ attention because they appeared to require correction.…
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Students' uptake and productive use of AI-generated feedback comments remain consistently limited.
“Yet despite these technological advances, students' uptake and productive use of AI-generated feedback comments remain consistently limited [16].”
From Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment (2026) · cites this paper as [16]
The Abstract (p. 1) reports low uptake: only 9% of AI-assistance suggestions led to revisions. The Conclusion (p. 16) likewise says uptake was low. But the paper does not establish that students’ productive use was consistently limited across settings; its Limitations (p. 15) caution that findings may be context-specific. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · see citing works
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087 · see citing works
Flesch, R. (1948) · Journal of Applied Psychology · doi:10.1037/h0057532 · Cited by 5,391 · see citing works
Campbell, J. L., Quincy, C., Osserman, J., & Pedersen, O. K. (2013) · Sociological Methods & Research · doi:10.1177/0049124113500475 · Cited by 2,974 · 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
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works
McDonald, N., Schoenebeck, S., & Forte, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359174 · Cited by 1,206 · see citing works
Peer feedback: the learning element of peer assessment
Liu, N.-F., & Carless, D. (2006) · Teaching in Higher Education · doi:10.1080/13562510600680582 · Cited by 1,034 · 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
Developing evaluative judgement: enabling students to make decisions about the quality of work
Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018) · Higher Education · doi:10.1007/s10734-017-0220-3 · Cited by 810 · see citing works
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13544 · Cited by 805 · see citing works
Self-citationImproving the effectiveness of peer feedback for learning
Gielen, S., Peeters, E., Dochy, F., Onghena, P., & Struyven, K. (2010) · Learning and Instruction · doi:10.1016/j.learninstruc.2009.08.007 · Cited by 615 · 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
AI-generated feedback on writing: insights into efficacy and ENL student preference
Escalante, J., Pack, A., & Barrett, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00425-2 · Cited by 589 · see citing works
A task–technology fit view of learning management system impact
McGill, T. J., & Klobas, J. E. (2008) · Computers & Education · doi:10.1016/j.compedu.2008.10.002 · Cited by 555 · see citing works
Impact of AI assistance on student agency
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024) · Computers & Education · doi:10.1016/j.compedu.2023.104967 · Cited by 548 · see citing works
Self-citationCo-creation in learning and teaching: the case for a whole-class approach in higher education
Bovill, C. (2020) · Higher Education · doi:10.1007/s10734-019-00453-w · Cited by 535 · see citing works
Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review
Qian, Y., & Lehman, J. (2018) · ACM Transactions on Computing Education · doi:10.1145/3077618 · Cited by 529 · 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
Automatic feedback in online learning environments: A systematic literature review
Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100027 · Cited by 440 · see citing works
Self-citationMatcha, W., Uzir, N. A., Gašević, D., & Pardo, A. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2916802 · Cited by 434 · see citing works
Self-citationAssessor or assessee: How student learning improves by giving and receiving peer feedback
Li, L., Liu, X., & Steckelberg, A. L. (2009) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2009.00968.x · Cited by 388 · see citing works
A systematic literature review of automated feedback generation for programming exercises
Keuning, H., Jeuring, J., & Heeren, B. (2019) · ACM Transactions on Computing Education · doi:10.1145/3231711 · Cited by 370 · see citing works
Strijbos, J.-W., Narciss, S., & Dünnebier, K. (2009) · Learning and Instruction · doi:10.1016/j.learninstruc.2009.08.008 · Cited by 369 · 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
Cukurova, M. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13514 · Cited by 302 · see citing works
Feedback sources in essay writing: peer-generated or AI-generated feedback?
Banihashem, S. K., Kerman, N. T., Noroozi, O., Moon, J., & Drachsler, H. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00455-4 · Cited by 282 · see citing works
Self-citationConditions that enable effective feedback
Henderson, M., Phillips, M., Ryan, T., Boud, D., Dawson, P., Molloy, E., & Mahoney, P. (2019) · Higher Education Research & Development · doi:10.1080/07294360.2019.1657807 · Cited by 248 · see citing works
The nature, reception, and use of online peer feedback in higher education
van der Pol, J., van den Berg, B. A. M., Admiraal, W. F., & Simons, P. R. J. (2008) · Computers & Education · doi:10.1016/j.compedu.2008.06.001 · Cited by 230 · see citing works
Molenaar, I. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100070 · Cited by 217 · see citing works
Hovardas, T., Tsivitanidou, O. E., & Zacharia, Z. C. (2014) · Computers & Education · doi:10.1016/j.compedu.2013.09.019 · Cited by 176 · see citing works
Guo, K., Pan, M., Li, Y., & Lai, C. (2024) · The Internet and Higher Education · doi:10.1016/j.iheduc.2024.100962 · Cited by 173 · see citing works
I do not need feedback! Or do I? Self-efficacy, perspective taking, and feedback seeking.
Sherf, E. N., & Morrison, E. W. (2019) · Journal of Applied Psychology · doi:10.1037/apl0000432 · Cited by 172 · 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
Peer Feedback Improves Students’ Academic Self-Concept in Higher Education
Simonsmeier, B. A., Peiffer, H., Flaig, M., & Schneider, M. (2020) · Research in Higher Education · doi:10.1007/s11162-020-09591-y · Cited by 135 · see citing works
Bauer, E., Greisel, M., Kuznetsov, I., Berndt, M., Kollar, I., Dresel, M., Fischer, M. R., & Fischer, F. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13336 · Cited by 130 · see citing works
Online peer feedback patterns of success and failure in argumentative essay writing
Kerman, N. T., Noroozi, O., Banihashem, S. K., Karami, M., & Biemans, H. J. A. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2093914 · Cited by 123 · see citing works
Self-citationGielen, M., & De Wever, B. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.06.019 · Cited by 122 · see citing works
Moving beyond classic readability formulas: new methods and new models
Crossley, S. A., Skalicky, S., & Dascălu, M. (2019) · Journal of Research in Reading · doi:10.1111/1467-9817.12283 · Cited by 120 · see citing works
GLAT: The generative AI literacy assessment test
Jin, Y., Martínez‐Maldonado, R., Gašević, D., & Yan, L. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100436 · Cited by 119 · see citing works
Self-citationAssessing 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
Self-citationUsher, M., & Barak, M. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1405238 · Cited by 110 · see citing works
Feedback scope in written corrective feedback: Analysis of empirical research in L2 contexts
Mao, Z., & Lee, I. (2020) · Assessing Writing · doi:10.1016/j.asw.2020.100469 · Cited by 110 · see citing works
Online peer feedback in higher education: A synthesis of the literature
Kerman, N. T., Banihashem, S. K., Karami, M., Er, E., van Ginkel, S., & Noroozi, O. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12273-8 · Cited by 109 · see citing works
Self-citationLatifi, S., Noroozi, O., & Talaee, E. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2020.1799032 · Cited by 107 · see citing works
Self-citationUsher, M. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2487495 · Cited by 104 · see citing works
Using learning analytics to understand student perceptions of peer feedback
Misiejuk, K., Wasson, B., & Egelandsdal, K. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106658 · Cited by 98 · see citing works
The effects of online peer feedback and epistemic beliefs on students’ argumentation-based learning
Noroozi, O., & Hatami, J. (2019) · Innovations in Education and Teaching International · doi:10.1080/14703297.2018.1431143 · Cited by 96 · see citing works
Self-citationDai, 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
Self-citationThe Reliability and Validity of Peer Review of Writing in High School AP English Classes.
Schunn, C. D., Godley, A., & Martino, S. D. (2016) · Journal of Adolescent & Adult Literacy · doi:10.1002/jaal.525 · Cited by 87 · see citing works
Self-regulation and student engagement with feedback: The case of Chinese EFL student writers
Yang, L., & Zhang, L. J. (2023) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2023.101226 · Cited by 86 · see citing works
Ba, S., Yang, L., Yan, Z., Looi, C. K., & Gašević, D. (2025) · Computers and Education Open · doi:10.1016/j.caeo.2025.100284 · Cited by 84 · see citing works
Self-citationRiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities
Khosravi, H., Kitto, K., & Williams, J. J. (2019) · Journal of Learning Analytics · doi:10.18608/jla.2019.63.12 · Cited by 81 · see citing works
Self-citationEffects of student engagement in peer feedback on writing performance in higher education
Jin, X., Jiang, Q., Xiong, W., Feng, Y., & Zhao, W. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2081209 · Cited by 80 · see citing works
Effects of adaptive feedback generated by a large language model: A case study in teacher education
Kinder, A., Briese, F. J., Jacobs, M., Dern, N., Glodny, N., Jacobs, S., & Leßmann, S. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100349 · Cited by 79 · see citing works
AI or Human? Evaluating Student Feedback Perceptions in Higher Education
Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72315-5_20 · Cited by 76 · see citing works
Alsaiari, O. A. S., Baghaei, N., Lahza, H. F., Lodge, J. M., Bodén, M., & Khosravi, H. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105363 · Cited by 75 · see citing works
Self-citationMachine learning based feedback on textual student answers in large courses
Bernius, J. P., Krusche, S., & Bruegge, B. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100081 · Cited by 75 · see citing works
Yeung, S. (2025) · Journal of Second Language Writing · doi:10.1016/j.jslw.2025.101203 · Cited by 70 · see citing works
Generative AI and multimodal data for educational feedback: Insights from embodied math learning
Cosentino, G., Anton, J., Sharma, K., Gelsomini, M., Giannakos, M. N., & Abrahamson, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13587 · Cited by 69 · see citing works
Guo, K., Zhang, E. D., Li, D., & Yu, S. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13540 · Cited by 69 · see citing works
Pedagogical framework for hybrid intelligent feedback
Banihashem, S. K., Noroozi, O., Khosravi, H., Schunn, C. D., & Drachsler, H. (2025) · Innovations in Education and Teaching International · doi:10.1080/14703297.2025.2499174 · Cited by 67 · see citing works
Self-citationDijks, M. A., Brummer, L., & Kostons, D. D. N. M. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1447645 · Cited by 66 · see citing works
Learnersourcing in the age of AI: Student, educator and machine partnerships for content creation
Khosravi, H., Denny, P. C., Moore, S., & Stamper, J. C. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100151 · Cited by 65 · see citing works
Self-citationpMineR: An Innovative R Library for Performing Process Mining in Medicine
Gatta, R., Lenkowicz, J., Vallati, M., Rojas, E., Damiani, A., Sacchi, L., Bari, B. D., Dagliati, A., Fernández-Llatas, C., Montesi, M., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59758-4_42 · Cited by 64 · see citing works
Students’ feedback seeking behaviour in undergraduate education: A scoping review
Leenknecht, M. J. M., & Carless, D. (2023) · Educational Research Review · doi:10.1016/j.edurev.2023.100549 · Cited by 62 · see citing works
Factors influencing students’ peer feedback uptake: instructional design matters
Juan, C. M., Ion, G., & Vicario, A. D. (2020) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2020.1726283 · Cited by 62 · see citing works
From the Automated Assessment of Student Essay Content to Highly Informative Feedback: a Case Study
Gombert, S., Fink, A., Giorgashvili, T., Jivet, I., Mitri, D. D., Yau, J. Y.-K., Frey, A., & Drachsler, H. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00387-6 · Cited by 61 · see citing works
Effects of instructional conditions and experience on the adoption of a learning tool
Gašević, D., Mirriahi, N., Dawson, S., & Joksimovíc, S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.10.026 · Cited by 55 · see citing works
Self-citationFrom nCoder to ChatGPT: From Automated Coding to Refining Human Coding
Zambrano, A. F., Liu, X., Barany, A., Baker, R. S., Kim, J., & Nasiar, N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-47014-1_32 · Cited by 54 · see citing works
Ruwe, T., & Mayweg-Paus, E. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100189 · Cited by 53 · see citing works
Doyle, E., Buckley, P., & Whelan, J. (2019) · Teaching in Higher Education · doi:10.1080/13562517.2018.1498077 · Cited by 50 · see citing works
Kaliisa, R., Misiejuk, K., López‐Pernas, S., & Saqr, M. (2025) · Educational Psychology · doi:10.1080/01443410.2025.2553639 · Cited by 50 · see citing works
Alemdağ, E., & Yıldırım, Z. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104574 · Cited by 47 · see citing works
Students’ perceptions of the peer-feedback experience in MOOCs
Kasch, J., van Rosmalen, P., Löhr, A., Klemke, R., Antonaci, A., & Kalz, M. (2021) · Distance Education · doi:10.1080/01587919.2020.1869522 · Cited by 47 · see citing works
Wu, W., Huang, J., Han, C., & Zhang, J. (2022) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2022.101140 · Cited by 45 · see citing works
Why Language Models Hallucinate
Kalai, A. T., Nachum, O., Vempala, S. S., & Zhang, E. (2025) · arXiv · doi:10.48550/arxiv.2509.04664 · Cited by 45 · see citing works
Large language models meet user interfaces: The case of provisioning feedback
Pozdniakov, S., Brazil, J., Abdi, S., Bakharia, A., Sadiq, S., Gašević, D., Denny, P., & Khosravi, H. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100289 · Cited by 43 · see citing works
Self-citationMartin, A. J., Collie, R. J., Kennett, R., Liu, D., Ginns, P., Sudimantara, L. B., Dewi, E. W., & Rüschenpöhler, L. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102723 · Cited by 42 · see citing works
Peer feedback in a large undergraduate blended course: Perceptions of value and learning
Ertmer, P. A., Richardson, J. C., Lehman, J. D., Newby, T. J., Cheng, X. (., Mong, C., & Sadaf, A. (2010) · Journal of Educational Computing Research · doi:10.2190/ec.43.1.e · Cited by 40 · see citing works
Iraj, H., Fudge, A. L., Khan, H., Faulkner, M., Pardo, A., & Kovanović, V. (2021) · Journal of Learning Analytics · doi:10.18608/jla.2021.7184 · Cited by 40 · see citing works
Jin, Y., Yang, K., Yan, L., Echeverría, V., Zhao, L., Alfredo, R., Milesi, M. E., Fan, J. X., Li, X., Gašević, D., et al. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706545 · Cited by 40 · see citing works
Self-citationQian, K., Liu, S., Li, T., Raković, M., Li, X., Guan, R., Molenaar, I., Nawaz, S., Swiecki, Z., Yan, L., et al. (2026) · Computers & Education · doi:10.1016/j.compedu.2025.105448 · Cited by 39 · see citing works
Self-citationRevealing the regulation of learning strategies of MOOC retakers: A learning analytic study
Fan, Y., Jovanović, J., John, S., Jiang, Y., Wang, Q., & Gašević, D. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104404 · Cited by 38 · see citing works
Self-citationLe, H., Shen, Y., Li, Z., Xia, M., Tang, L., Li, X., Jia, J., Wang, Q., Gašević, D., & Fan, Y. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13614 · Cited by 38 · see citing works
Self-citationUntangling Critical Interaction with AI in Students’ Written Assessment
Shibani, A., Knight, S., Kitto, K., Karunanayake, A., & Buckingham Shum, S. (2024) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613905.3651083 · Cited by 35 · see citing works
Bauer, E., Richters, C., Pickal, A. J., Klippert, M., Sailer, M., & Stadler, M. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13609 · Cited by 33 · see citing works
Augmenting assessment with AI coding of online student discourse: A question of reliability
Misiejuk, K., Kaliisa, R., & Scianna, J. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100216 · Cited by 33 · see citing works
Hutt, S. J., DePiro, A., Wang, J., Rhodes, S., Baker, R. S., Hieb, G., Sethuraman, S., Ocumpaugh, J. L., & Mills, C. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636850 · Cited by 32 · see citing works
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
Scarlatos, A., Smith, D., Woodhead, S., & Lan, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64302-6_20 · Cited by 32 · see citing works
Incorporating Training, Self-monitoring and AI-Assistance to Improve Peer Feedback Quality
Darvishi, A., Khosravi, H., Abdi, S., Sadiq, S., & Gašević, D. (2022) · Proceedings of the Ninth ACM Conference on Learning @ Scale · doi:10.1145/3491140.3528265 · Cited by 31 · see citing works
Self-citationLi, T., Nath, D., Cheng, Y., Fan, Y., Li, X., Raković, M., Khosravi, H., Swiecki, Z., Tsai, Y., & Gašević, D. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706559 · Cited by 30 · see citing works
Self-citationXu, S., Huang, X., Lo, C. K., Chen, G., & Jong, M. S. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100325 · Cited by 30 · see citing works
Peer and AI Review + Reflection (PAIRR): A human-centered approach to formative assessment
Sperber, L., MacArthur, M., Minnillo, S., Stillman, N., & Whithaus, C. (2025) · Computers and Composition · doi:10.1016/j.compcom.2025.102921 · Cited by 29 · see citing works
What does it take to provide effective peer feedback?
Little, T., Dawson, P., Boud, D. J., & Tai, J. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2475059 · Cited by 28 · see citing works
Chen, W., Hu, H., Lyu, Q., & Zheng, L. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13078 · Cited by 26 · see citing works
Supporting peer learning with artificial intelligence: A systematic literature review
Sichterman, B., Noroozi, O., Boetje, J., van Ginkel, S. O., Khosravi, H., & Versendaal, J. (2025) · Innovations in Education and Teaching International · doi:10.1080/14703297.2025.2530118 · Cited by 26 · see citing works
Self-citationXie, X., Zhang, L. J., & Wilson, A. (2025) · Behavioral Sciences · doi:10.3390/bs15070884 · Cited by 24 · see citing works
Moore, S., Nguyen, H. A., & Stamper, J. C. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460140 · Cited by 24 · see citing works
The effects of gender and training on peer feedback characteristics
Ocampo, J. C. G., Panadero, E., Zamorano, D., Sánchez‐Iglesias, I., & Díez, F. (2023) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2023.2286432 · Cited by 23 · see citing works
Learnersourcing in Theory and Practice: Synthesizing the Literature and Charting the Future
Singh, A., Brooks, C. A., & Doroudi, S. (2022) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3491140.3528277 · Cited by 20 · see citing works
Denny, P. C., Khosravi, H., Hellas, A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.10509 · Cited by 19 · see citing works
Self-citationReviewriter: AI-Generated Instructions For Peer Review Writing
Su, X., Wambsganß, T., Rietsche, R., Neshaei, S. P., & Kser, T. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.5 · Cited by 19 · see citing works
Mello, R. F., Fiorentino, G., Oliveira, H., de Miranda, P. B. C., Raković, M., & Gašević, D. (2022) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3506860.3506977 · Cited by 18 · see citing works
Self-citationSupporting peer evaluation of student-generated content: a study of three approaches
Gyamfi, G., Hanna, B. E., & Khosravi, H. (2021) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2021.2006140 · Cited by 17 · see citing works
Self-citationDarvishi, A., Khosravi, H., & Sadiq, S. W. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460129 · Cited by 17 · see citing works
Self-citationYin, X., Ye, J., Yu, S., Li, H., Liu, Q., & Zhao, G. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105434 · Cited by 15 · see citing works
Dialogic feedback at scale: Recommendations for learning analytics design
Maheshi, B., Dai, W., Martínez‐Maldonado, R., & Tsai, Y. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13034 · Cited by 13 · see citing works
Darvishi, A., Khosravi, H., Rahimi, A., Sadiq, S. W., & Gašević, D. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3229022 · Cited by 12 · see citing works
Self-citationImprovement in the quality of feedback as an indication of the development of evaluative judgement
Cano, E., Jardí, A., Molins, L. L., & Martins, L. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2329170 · Cited by 12 · see citing works
Li, Y., Shan, Z., Raković, M., Guan, Q., Gašević, D., & Chen, G. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13741-z · Cited by 12 · see citing works
Self-citationTowards the Automated Generation of Readily Applicable Personalised Feedback in Education
Liang, Z., Sha, L., Tsai, Y., Gašević, D., & Chen, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64299-9_6 · Cited by 12 · see citing works
Self-citationWIP: Using Generative AI to Assist in Individual Performance Feedback for Engineering Student Teams
Sajadi, S., Ryan, O., Schibelius, L., & Huerta, M. V. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343517 · Cited by 12 · see citing works
Fleur, D. S., Marshall, M. S., Pieters, M., Brouwer, N., Oomens, G., Konstantinidis, A., Winnips, K., Moes, S., van den Bos, W., Bert Bredeweg, A., et al. (2023) · Journal of Learning Analytics · doi:10.18608/jla.2023.7853 · Cited by 11 · see citing works
When do students provide more peer feedback? The roles of performance and prior feedback experiences
Zong, Z., Schunn, C. D., & Wang, Y. (2023) · Instructional Science · doi:10.1007/s11251-023-09640-w · Cited by 11 · see citing works
Effects of Technological Interventions for Self-regulation: A Control Experiment in Learnersourcing
Lahza, H. F., Khosravi, H., Demartini, G., & Gašević, D. (2022) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3506860.3506911 · Cited by 11 · see citing works
Self-citationZhang, Y., Chen, H., Wang, J., Pi, Z., & Yang, J. (2025) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2024.101442 · Cited by 10 · see citing works
Seßler, K., Bewersdorff, A., Nerdel, C., & Kasneci, E. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2502.12842 · Cited by 10 · see citing works
Understanding Peer Feedback Contributions Using Natural Language Processing
Castro, M., Mello, R. F., Fiorentino, G., Viberg, O., Spikol, D., Baars, M., & Gašević, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42682-7_27 · Cited by 9 · see citing works
Self-citationConsistency in providing peer feedback: the role of individual and situational factors
Gao, X., Schunn, C. D., Noroozi, O., Gulikers, J. T. M., Banihashem, S. K., & Biemans, H. J. A. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2517617 · Cited by 9 · see citing works
Self-citationGregg, K., Ryan, O., Katz, A., Huerta, M. V., & Sajadi, S. (2025) · Journal of Engineering Education · doi:10.1002/jee.70024 · Cited by 8 · see citing works
Scalable Science Education via Online Cooperative Questioning
Hilton, C. B., Goldwater, M. B., Hancock, D., Clemson, M., Huang, A., & Denyer, G. S. (2021) · CBE—Life Sciences Education · doi:10.1187/cbe.19-11-0249 · Cited by 7 · see citing works
Towards Generating Counterfactual Examples as Automatic Short Answer Feedback
Filighera, A., Tschesche, J., Steuer, T., Tregel, T., & Wernet, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11644-5_17 · Cited by 7 · see citing works
Heimerl, A., Mertes, S., Schneeberger, T., Baur, T., Liu, A., Becker, L., Rohleder, N., Gebhard, P., & André, E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11644-5_67 · Cited by 7 · see citing works
That's What RoBERTa Said: Explainable Classification of Peer Feedback
Huang, K., Mello, R. F., Junior, C. P., Rodrigues, L., Baars, M., & Viberg, O. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706526 · Cited by 7 · see citing works
Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students
Zhang, A., Gao, Y., Suraworachet, W., Nazaretsky, T., & Cukurova, M. (2025) · arXiv · doi:10.48550/arxiv.2504.10961 · Cited by 7 · see citing works
Enhancing coherence and student engagement through portfolio assignments and peer-feedback
Oestergaard, L. G., Hansen, J. S., Ravn, M. B., & Maribo, T. (2024) · Discover Education · doi:10.1007/s44217-024-00260-1 · Cited by 3 · see citing works
Patel, P., Rios, S., Valentine, A., & de Oliveira, E. A. (2024) · ASCILITE Publications · doi:10.14742/apubs.2024.1446 · Cited by 2 · see citing works
Leveraging Epistemic Network Analysis to Understand Peer Feedback in Online Courses
Siggard, R. R., & Lundgren, L. M. (2024) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10165-1 · Cited by 2 · see citing works
Barbero (2014) · doi:10.1016/j · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 14
- Dai, W., Cheng, Y., Aldino, A. A., Tsai, Y. S., Gašević, D., & Chen, G. (2025). Evaluating the capability of large language models in characterising relational feedback: A comparative analysis of prompting strategies. Computers and Education: Artificial Intelligence , 9 , Article 100427. http://dx.doi.org/10.1016/j.caeai. 2025.100427. · DOI
- Field, A., Miles, J., & Field, Z. (2017). Discovering statistics using R . W. Ross MacDonald School Resource Services Library. http://refhub.elsevier.com/S0360-1315(2600029-1/sb35)
- Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. https://arxiv.org/abs/2203.05794. · arXiv
- Khosravi, H., Demartini, G., Sadiq, S., & Gasevic, D. (2021). Charting the design and analytics agenda of learnersourcing systems. In LAK21: 11th international learning analytics and knowledge conference (pp. 32-42). http://dx.doi.org/10.1145/3448139.344814. · DOI
- Koutcheme, C. (2023). Training language models for programming feedback using automated repair tools. In N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, & V. Dimitrova (Eds.), Artificial intelligence in education (pp. 830-835). Cham: Springer Nature Switzerland, http://dx.doi.org/10.1007/978-3-03136272-9\_79. · DOI
- Lahza, H. F., Demartini, G., Noroozi, O., Gašević, D., Sadiq, S., & Khosravi, H. (2025). Enhancing peer feedback provision through user interface scaffolding: a comparative examination of scripting and self-monitoring techniques. Computers & Education , Article 105260. http://dx.doi.org/10.1016/j.compedu.2025. 105260. · DOI
- Lim, L. A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2020). Students' perceptions of, and emotional responses to, personalised learning analytics-based feedback: An exploratory study of four courses. Assessment & Evaluation in Higher Education , 1-21. http://dx.doi.org/10.1080/ 02602938.2020.1782831.
- Pozdniakov, S., Brazil, J., Mohammadi, M., Dollinger, M., Sadiq, S., & Khosravi, H. (2025). AI-assisted co-creation: Bridging skill gaps in student-generated content. Journal of Learning Analytics , 12 (1), 129-151. http://dx.doi.org/10.18608/jla.2025.8601. · DOI
- Rashkin, H., Clark, E., Huot, F., & Lapata, M. (2025). Help me write a story: Evaluating llms' ability to generate writing feedback. In Proceedings of the 63rd annual meeting of the association for computational linguistics (volume 1: long papers) (pp. 25827-25847). Association for Computational Linguistics, http://dx.doi.org/10.18653/v1/2025.acl-long.1254. · DOI
- Shibani, A., & Shum, S. B. (2024). AI-assisted writing in education: Ecosystem risks and mitigations. http://dx.doi.org/10.48550/arXiv.2404.10281, arXiv: 2404.10281. · DOI
- Vanacore, K., Pankiewicz, M., & Baker, R. (2025). Unpacking the impact of generative AI feedback: Divergent effects on student performance and self-regulated learning. http://dx.doi.org/10.35542/osf.io/tbpn3\_v1, Preprint. · DOI
- Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors influencing university students' behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human-Computer Interaction , 1-23. http://dx.doi.org/10.1080/10447318. 2024.2383033. · DOI
- Wang, R., Wirawarn, P., Goodman, N., & Demszky, D. (2023). SIGHT: A large annotated dataset on student insights gathered from higher education transcripts. http://dx.doi.org/10.48550/arXiv.2306.09343, arXiv arXiv:2306.09343. · DOI
- Wynn, A., & Wang, J. (2023). BETTER: An automatic feedback system for supporting emotional speech training. In N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, & V. Dimitrova (Eds.), Artificial intelligence in education (pp. 746-752). Cham: Springer Nature Switzerland, http://dx.doi.org/10.1007/978-3031-36272-9\_66. · DOI
Cited by
Sorted by citations
LLM-generated formative feedback in education: A qualitative systematic literature review
Maier, U., Seibold, M., & Klotz, C. (2026) · Computers and Education Open · doi:10.1016/j.caeo.2026.100374 · Cited by 10 · see citing works
Jansen, T., Bahr, J. L., Schaller, N.-J., Höft, L., & Meyer, J. (2026) · Learning and Individual Differences · doi:10.1016/j.lindif.2026.102969 · Cited by 2 · see citing works
Agentivism: a learning theory for the age of artificial intelligence
Yan, L., & Gašević, D. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100684 · Cited by 1 · see citing works
Self-citationYılmaz, R., & Yılmaz, F. G. K. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105753 · Cited by 1 · see citing works
Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment
Alsaiari, O., Baghaei, N., Lodge, J. M., Gaševi'c, D., Winstone, N., & Khosravi, H. (2026) · arXiv · doi:10.48550/arxiv.2608.11625
“GenAI can relax constraints on the production of timely and individualised comments, but this capability does not ensure their uptake [16, 21, 41].”
InfluentialSelf-citationA survey on the use of generative artificial intelligence by postgraduate students in ChinaPreprint
Qi, R., Fan, C., Wang, J., & Hu, W. (2026) · Research Square · doi:10.21203/rs.3.rs-9862747/v1
A cognitive-affective dual-path model of learner engagement in AI-supported writing feedback
Li, T., Aziz, A. B. A., & Sulaiman, N. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1868103
Peer feedback leads, GenAI complements: tracing revision patterns in undergraduate projects
Usher, M., & Faraon, M. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2713629
Artificial Intelligence Driven Grading and Personalised Feedback in Higher Education AssessmentPreprint
Madwe, M. C. (2026) · Research Square · doi:10.21203/rs.3.rs-10810076/v1
Dabiri, A., & Behzadan, A. H. (2026) · AI · doi:10.3390/ai7090363
Chen, S. C., Schunn, C. D., Cao, J., Banihashem, S. K., Gašević, D., & Fan, Y. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105767
Self-citationChen, H., Ma, L., Hao, Y., Wu, D., Zhao, J., & Zhang, D. (2026) · International Journal of Mechanical Engineering Education · doi:10.1177/03064190261498183
- Authors
- Pozdniakov, S., Brazil, J., Banihashem, S. K., Noroozi, O., Gašević, D., Sadiq, S., & Khosravi, H.
- Affiliations
- The University of Queensland (AU)
- Open University of the Netherlands (NL)
- Wageningen University & Research (NL)
- Monash University (AU)
- Corresponding author
- Pozdniakov, Stanislav
- ORCID
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2026-07
- Volume
- 248
- Pages
- 105591
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W7128727433
- Cited by
- 11
- References
- 81
- Citations by year
- 2026: 11
- FWCI
- 93.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Education (publishedVersion, cc-by)
- Socio-Environmental Systems Modeling (submittedVersion, cc-by)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by)
- HAL (submittedVersion)
- DSpace (Open University in the Netherlands) (submittedVersion, cc-by)
- Funders
- Cooperative Institute for Research in Environmental Sciences
- Australian Research Council (IC200100022)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- generative AI, peer feedback, AI-generated feedback, student independence, peer assessment, undergraduate education, AI interface design
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
