Feedback from Generative AI: Correlates of Student Engagement in Text Revision from 655 Classes from Primary and Secondary School
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
The feedback literacy and self-regulated learning objective aims to strengthen students' capacity to understand, act on, and self-regulate in response to AI-generated feedback.
“This objective aims to (1) strengthen students' capacity to understand, act on, and selfregulate in response to AI-generated feedback [124-127];”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [126]
Cites
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · see citing works
Kluger, A. N., & DeNisi, A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.2.254 · Cited by 5,882 · see citing works
The Psychology of Written Composition
Cumming, A. H., Bereiter, C., & Scardamalia, M. (1989) · Modern Language Journal · doi:10.2307/327270 · Cited by 2,806 · see citing works
MacArthur, C. A., Graham, S. W., & Fitzgerald, J. (2005) · Cited by 2,327 · 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 · cited 1× · see citing works
“Without active engagement, the potential benefits of feedback may not be realized [4, 30].”
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
Winstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017) · Educational Psychologist · doi:10.1080/00461520.2016.1207538 · Cited by 884 · cited 3× · see citing works
“…focus of feedback as information to incorporate a more student-centered perspective, placing greater emphasis on student engagement with feedback [22, 29, 30] and defining it as a process in which the learner actively makes sense of performance-related information to facilitate learning [13, 29].”
Student engagement with teacher and automated feedback on L2 writing
Zhang, Z. (., & Hyland, K. (2018) · Assessing Writing · doi:10.1016/j.asw.2018.02.004 · Cited by 546 · see citing works
Koltovskaia, S. (2020) · Assessing Writing · doi:10.1016/j.asw.2020.100450 · Cited by 447 · see citing works
Feedback: Focusing attention on engagement
Price, M., Handley, K., & Millar, J. (2011) · Studies in Higher Education · doi:10.1080/03075079.2010.483513 · Cited by 442 · see citing works
Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100199 · Cited by 311 · see citing works
Self-citationThe Effects of Writing on Learning in Science, Social Studies, and Mathematics: A Meta-Analysis
Graham, S., Kiuhara, S. A., & MacKay, M. (2020) · Review of Educational Research · doi:10.3102/0034654320914744 · Cited by 295 · see citing works
Automated Writing Assessment in the Classroom
Warschauer, M., & Grimes, D. (2008) · Pedagogies: An International Journal · doi:10.1080/15544800701771580 · Cited by 243 · see citing works
The Effectiveness of Automated Writing Evaluation on Writing Quality: A Meta-Analysis
Zhai, N., & Ma, X. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331221127300 · Cited by 169 · see citing works
Validity arguments for diagnostic assessment using automated writing evaluation
Chapelle, C. A., Cotos, E., & Lee, J. (2015) · Language Testing · doi:10.1177/0265532214565386 · Cited by 158 · see citing works
Automated feedback and writing: a multi-level meta-analysis of effects on students' performance
Fleckenstein, J., Liebenow, L. W., & Meyer, J. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1162454 · Cited by 145 · see citing works
Self-citationTeaching Children About Revision in Writing
Fitzgerald, J., & Markham, L. R. (1987) · Cognition and Instruction · doi:10.1207/s1532690xci0401_1 · Cited by 145 · see citing works
Evidence-Based Writing Practices: A Meta-Analysis of Existing Meta-Analyses
Graham, S., & Harris, K. R. (2017) · BRILL eBooks · doi:10.1163/9789004270480_003 · Cited by 117 · see citing works
The effectiveness of automated writing evaluation in EFL/ESL writing: a three-level meta-analysis
Ngo, T. T.-N., Chen, H. H.-J., & Lai, K. K.-W. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2096642 · Cited by 108 · see citing works
The impact of computer-based feedback on students' written work?
El Ebyary, K., & Windeatt, S. (2010) · International Journal of English Studies · doi:10.6018/ijes/2010/2/119231 · Cited by 94 · 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
Identifying Feedback That Has Impact
Henderson, M., Ajjawi, R., Boud, D. J., & Molloy, E. (2019) · Springer eBooks · doi:10.1007/978-3-030-25112-3_2 · Cited by 92 · see citing works
Wu, Y., & Schunn, C. D. (2020) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2020.101897 · Cited by 83 · see citing works
Panadero, E. (2023) · Educational Psychologist · doi:10.1080/00461520.2023.2223642 · Cited by 76 · see citing works
The Effects of Online Feedback on ESL/EFL Writing: A Meta-Analysis
Lv, X., Ren, W., & Xie, Y. H. (2021) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-021-00594-6 · Cited by 76 · see citing works
eRevis(ing): Students’ revision of text evidence use in an automated writing evaluation system
Wang, E. L., Matsumura, L. C., Correnti, R. J., Litman, D. J., Zhang, H., Howe, E., Magooda, A., & Quintana, R. (2020) · Assessing Writing · doi:10.1016/j.asw.2020.100449 · Cited by 73 · see citing works
Toward a cohesive psychological science of effective feedback
Winstone, N., & Nash, R. A. (2023) · Educational Psychologist · doi:10.1080/00461520.2023.2224444 · Cited by 67 · see citing works
Computer-Mediated Corrective Feedback to Improve L2 Writing Skills: A Meta-Analysis
Mohsen, M. A. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331211064066 · Cited by 56 · see citing works
Jansen, T., Meyer, J., Fleckenstein, J., Horbach, A., Keller, S., & Møller, J. (2023) · Learning and Instruction · doi:10.1016/j.learninstruc.2023.101847 · Cited by 28 · see citing works
Self-citationEvidence-based recommendations for teaching writing
Graham, S., Collins, A. A., & Ciullo, S. P. (2024) · Education 3-13 · doi:10.1080/03004279.2024.2357893 · Cited by 23 · see citing works
The Impact of Criterion Writing Evaluation Technology on Criminal Justice Student Writing Skills
Schroeder, J. A., Grohe, B., & Pogue, R. (2008) · Journal of Criminal Justice Education · doi:10.1080/10511250802476269 · Cited by 20 · see citing works
Zitzmann, S., Machts, N., Hübner, N., Schauber, S. K., Møller, J., & Lindner, C. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09776-8 · Cited by 11 · see citing works
Receptivity to Instructional Computer-Based Feedback (RIF)
Bahr, J. L., Höft, L., Meyer, J., & Jansen, T. (2024) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000841 · Cited by 9 · see citing works
Self-citationZitzmann, S., Lindner, C., Leucht, C., & Leucht, S. (2024) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.03.008 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
Ge, Z.-G. (2026) · Innovations in Education and Teaching International · doi:10.1080/14703297.2026.2620543 · Cited by 3 · see citing works
Πέλλας, Ν. (2026) · Multimodal Technologies and Interaction · doi:10.3390/mti10050057 · Cited by 2 · see citing works
From Wandering to Collaboration: Discourse Patterns in Middle School Generative AI Use
Ritchie, D., Nixon, N., Tate, T. P., & Warschauer, M. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785094 · Cited by 1 · see citing works
Luo, J., Tang, Z., & Sun, M. (2026) · AI Brain and Child · doi:10.1007/s44436-026-00031-9 · Cited by 2 · see citing works
Maier, M. L. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14070112 · Cited by 1 · see citing works
Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning
Cao, J., Zhao, C. Q., Schunn, C., McLaughlin, E. A., Lin, J., & Koedinger, K. R. (2026) · arXiv · doi:10.48550/arxiv.2604.07469
Zur Qualität KI-generierten Feedbacks
Fürstenberg, M. (2025) · Literatur - Medien - Didaktik · doi:10.57088/978-3-7329-8796-2_7
Mapping the Evolution of Online Feedback Tools in Writing Education
Abuhassna, H., Toh, C., Yunos, F. N. A., Qatawneh, M. A. H., & Almabhouh, A. A. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5601-3.ch015
Meyer, J., Steinbach, M., Schiller, R., Mertens, U., Schaller, N.-J., Horbach, A., Fleckenstein, J., Köller, O., Kizilcec, R. F., & Jansen, T. (2026) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3774398.3811602 · Cited by 1 · see citing works
Self-citationEvaluating the Effect of Generative AI-Assisted Debugging on Students’ Critical Thinking
Albadarin, Y., Saqr, M., Pope, N., & Tukiainen, M. (2026) · Technology Knowledge and Learning · doi:10.1007/s10758-026-09998-z
Cavalcanti, A. P., Rodrigues, L., Xavier, C., da Costa, N. T., Ribeiro, F. G. C., Silva, L. F. B., de Melo, W. C., Stacciarini, L. S., Weber, G., Vieira, E., et al. (2026) · Revista Brasileira de Informática na Educação · doi:10.5753/rbie.2026.7395
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
- Authors
- Jansen, T., Horbach, A., & Meyer, J.
- Affiliations
- Leibniz Institute for Science and Mathematics Education (DE)
- Venue
- Proceedings of the 15th International Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2025-03-03
- Pages
- 831-836
- OpenAlex
- W4407842253
- Cited by
- 12
- References
- 34
- Citations by year
- 2026: 10 · 2025: 2
- FWCI
- 137.42
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion, cc-by)
- Funders
- Deutsche Telekom Stiftung (GENIUS)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Keywords
- generative AI, student engagement, textual revision, writing instruction, elementary school, secondary schools, AI-generated feedback
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
