Impact of Using Generative AI as a “Feedback Partner” on Students’ Writing Quality

Authors

  • Laura Ochs Pottmeyer Carnegie Mellon University Author
  • Chad Hershock Carnegie Mellon University Author
  • Danielle Zawodny Wetzel Carnegie Mellon University Author
  • Zach Mineroff Carnegie Mellon University Author

Keywords:

generative AI; SOTL; writing instruction

Abstract

students’ writing quality and self-efficacy for producing claims with supporting evidence. They assigned approximately students across first-year writing courses to four conditions (one peer review and three GenAI feedback conditions). In all conditions, performance and self-efficacy increased on second drafts. Writing skills improved most when students received multiple rounds of GenAI feedback. Subsequent instructor feedback further increased performance. Students receiving peer feedback reported a more positive experience than those receiving GenAI feedback. The authors discuss practical implications for writing instruction and teaching with GenAI.

Downloads

Download data is not yet available.

Author Biographies

  • Laura Ochs Pottmeyer, Carnegie Mellon University

    Laura Ochs Pottmeyer, Ph.D., is a Senior Data Science Research Associate at the Eberly Center for Teaching Excellence and Educational Innovation at Carnegie Mellon University.

  • Chad Hershock, Carnegie Mellon University

    Chad Hershock, Ph.D., is the Executive Director of the Eberly Center for Teaching Excellence and Educational Innovation and Generative AI Teaching as Research Initiative at Carnegie Mellon University.

  • Danielle Zawodny Wetzel, Carnegie Mellon University

    Danielle Zawodny Wetzel is a Teaching Professor in Rhetoric, Writing & Communication in the English Department at Carnegie Mellon University, where she directs the undergraduate foundational writing program. 

  • Zach Mineroff, Carnegie Mellon University

    Zach Mineroff is an Assistant Director of Learning Engineering at Carnegie Mellon University and supports the design, development, and implementation of innovative educational technologies and learning experiences.

References

Annapureddy, R., Fornaroli, A., & Daniel Gatica-Perez, D. (2025). Generative AI Literacy: Twelve Defining Competencies. Digital Government: Research and Practice, Volume 6, (1), 1 - 21. https://doi.org/10.1145/3685680

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman and Company

Bastani, H.., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2024). Generative AI Can Harm Learning. The Wharton School Research Paper, http://dx.doi.org/10.2139/ssrn.4895486

Bean & Melzer (2021). Engaging ideas: The professor’s guide to integrating writing, critical thinking, and active learning in the classroom, 3rd edition. Jossey Bass.

Bowen, J.A., & Watson, C.E. (2024). Teaching with AI: A Practical Guide to a New Era of Human Learning. Johns Hopkins University Press and American Association of Colleges and Universities.

Cizek, G.J., Andrade, H.L., & Bennett, R.E. (2019). Formative assessment: History, definition and progress. In H.L. Andrade et al (eds.), Handbook of formative assessment in the disciplines (pp. 3-19). Routledge.

Cho, K., & MacArthur, C. (2011). Learning by reviewing. Journal of Educational Psychology, 103(1), 73–84. https://doi.org/10.1037/a0021950

Cho, K., & MacArthur, C. (2010). Student revision with peer and expert reviewing. Learning and Instruction, 20(4), 328–338. https://doi.org/10.1016/j.learninstruc.2009.08.006

Cho, K. & Schunn, C.D. (2007). Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system. Computers & Education, 48(3), 409-426. https://doi.org/10.1016/j.compedu.2005.02.004

Dijks, M. A., Brummer, L., & Kostons, D. (2018). The anonymous reviewer: the relationship between perceived expertise and the perceptions of peer feedback in higher education. Assessment & Evaluation in Higher Education, 43(8), 1258–1271. https://doi.org/10.1080/02602938.2018.1447645

Doğan, M., Celik, A., & Arslan, H. (2024). AI In Higher Education: Risks and Opportunities From the Academician Perspective. European Journal of Education Volume 60, (1), 1-11. https://doi.org/10.1111/ejed.12863

Downs, D. (2015). Revision is central to writing. In L. Adler Kassner & E. Wardle, Naming What We Know: Threshold Concepts in Writing Studies (pp. 66-67). Utah State University Press.

Evans K. & Ferris, D. (2019). Revision from Multiple Feedback Sources: The Attitudes and Behaviors of Three Multilingual Student Writers. Research in the Teaching of English 54(2), 131-160.

Flower (1979). Writer-based prose. College English, 41(1), 19-37. https://doi.org/10.58680/ce197916016

Gaynor, J. W. (2019). Peer review in the classroom: student perceptions, peer feedback quality and the role of assessment. Assessment & Evaluation in Higher Education, 45(5), 758–775. https://doi.org/10.1080/02602938.2019.1697424

Hayes, J. R. (2012). Modeling and Remodeling Writing. Written Communication, 29(3), 369-388. https://doi.org/10.1177/0741088312451260

Hillocks, G. (1986). Research on Written Composition: New Directions for Teaching. National Conference on Research in English.

Huisman, B., Saab, N., van Driel, J., & van den Broek, P. (2018). Peer feedback on academic writing: undergraduate students’ peer feedback role, peer feedback perceptions and essay performance. Assessment & Evaluation in Higher Education, 43(6), 955–968. https://doi.org/10.1080/02602938.2018.1424318

Lovett, M. C., Bridges, M. W., DiPietro, M., Ambrose, S. A., & Norman, M. K. (2023). How learning works: Eight research-based principles for smart teaching. John Wiley & Sons, Incorporated

Lundstrom K. & Baker, W. (2009). To give is better than to receive: The benefits of peer review to the reviewer's own writing. Journal of Second Language Writing, 18(1), 30-43. https://doi.org/10.1016/j.jslw.2008.06.002

Kellogg, R.T. (2008). Training writing skills: A cognitive developmental perspective. Journal of Writing Research, 1(1), 1-26. https://doi.org/10.17239/jowr-2008.01.01.1

Kestin, G., Miller, K., Klales, A., Milbourne, T., Ponti, G. (2025). AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports 15, 17458. https://doi.org/10.1038/s41598-025-97652-6

Markey, B., Brown, D. W., Laudenbach, M., & Kohler, A. (2024). Dense and disconnected: Analyzing the sedimented style of ChatGPT-generated text at scale. Written Communication, 41(4), 571-600.

Mollick, E. (2024). Co-Intelligence: Living and Working with AI. Penguin.

Noy, S., & Whitney, Z. (2023). Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. Science 381: 187–192. DOI: 10.1126/science.adh258

Pardos, Z.A., & Bhandari, S. (2024). ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills. PLoS ONE 19(5): e0304013. https://doi.org/10.1371/journal.pone.0304013

Patchan, M. M., & Schunn, C. D. (2016). Understanding the effects of receiving peer feedback for text revision: Relations between author and reviewer ability. Journal of Writing Research, 8(2), 227-265. doi: 10.17239/jowr-2016.08.02.03

Pincus, J., & Hershock, C. (2024). A narrative review of interdisciplinary teaching and learning scholarship: closing the gap between theory and practice. International Journal for Academic Development, 1-18.

Sanders-Reio, J. (2010). Investigation of the relations between domain-specific beliefs about writing, writing self-efficacy, writing apprehension, and writing performance in undergraduates. Unpublished doctoral dissertation. College Park: University of Maryland.

Reinhart, A., Markey, B., Laudenbach, M., Pantusen, K., Yurko, R., Weinberg, G., & Brown, D. W. (2025). Do LLMs write like humans? Variation in grammatical and rhetorical styles. Proceedings of the National Academy of Sciences, 122(8), e2422455122.https://doi.org/10.1073/pnas.2422455122

Watson, C. E., & Rainie, L. (2025). Higher Education Executives Assess AI’s Impacts on Teaching and Learning. AAC&U. https://dgmg81phhvh63.cloudfront.net/content/user-photos/AACU_AI_Report_2025.pdf

Wu, Y., & Schunn, C. D. (2020). When peers agree, do students listen? The central role of feedback quality and feedback frequency in determining uptake of feedback. Contemporary Educational Psychology, 62, Article 101897. https://doi.org/10.1016/j.cedpsych.2020.101897

Wu, Y., & Schunn, C. D. (2023). Assessor writing performance on peer feedback: Exploring the relation between assessor writing performance, problem identification accuracy, and helpfulness of peer feedback. Journal of Educational Psychology, 115(1), 118–142. https://doi.org/10.1037/edu0000768

Yu, S., & Lee, I. (2016). Peer feedback in second language writing (2005–2014). Language Teaching, 49(4), 461–493. doi:10.1017/S0261444816000161

Published

2026-03-17

Data Availability Statement

Per our IRB protocol, our data is not currently available.

Issue

Section

Articles

Categories

How to Cite

Impact of Using Generative AI as a “Feedback Partner” on Students’ Writing Quality. (2026). Journal on Excellence in College Teaching. https://celt.miamioh.edu/index.php/JECT/article/view/1305