Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers
- 1Michigan State University
Abstract
Background: Instructors and education researchers are increasingly developing feedback with the assistance of generative AI, which we refer to as AI-enabled feedback, yet we do not know whether established understandings of feedback uptake and effectiveness apply similarly in AI-mediated contexts. This makes the recipience of AI-enabled feedback a central concern to understanding how to best support students in developing their scientific writing skills. This study examined how two teams of undergraduate biology students used AI-enabled feedback to revise their scientific writing, and the extent to which their revisions addressed that feedback. The teams revised their research proposal drafts after receiving AI-enabled feedback developed by their instructor with the aid of a custom AI tool. Results: Our findings show that AI-enabled feedback prompted actionable revision across both teams. Each team addressed approximately half of the feedback comments, indicating comparable levels of uptake despite their differing approaches to revision. We observed that both teams were inconsistent in providing the additional information and reasoning requested in the feedback, and they struggled to address comments requiring self-evaluation. Taken together, we see that students often addressed the more easily actionable parts of AI-enabled feedback while omitting more demanding revisions. Conclusions: These findings provide behavioral evidence of how students used AI-enabled feedback to revise their scientific writing. The observed patterns suggest that the content of AI-enabled feedback supported meaningful revision, while limitations in students agency, strategies, and volition likely shaped their implementation of comments. Although the degree of uptake observed in this study is encouraging, it is unlikely that a single application of generative AI within the feedback process will resolve all the challenges associated with feedback uptake.
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BibTeX
@misc{weston2026biology,
title = {{Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers}},
author = {M. Weston and T. Long},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.19.760216},
url = {https://www.biorxiv.org/content/10.64898/2026.10.19.760216v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers
AU - M. Weston
AU - T. Long
PY - 2026
DA - 2026/10/19
PB - bioRxiv
DO - 10.64898/2026.10.19.760216
UR - https://www.biorxiv.org/content/10.64898/2026.10.19.760216v1
AB - Background: Instructors and education researchers are increasingly developing feedback with the assistance of generative AI, which we refer to as AI-enabled feedback, yet we do not know whether established understandings of feedback uptake and effectiveness apply similarly in AI-mediated contexts. This makes the recipience of AI-enabled feedback a central concern to understanding how to best support students in developing their scientific writing skills. This study examined how two teams of undergraduate biology students used AI-enabled feedback to revise their scientific writing, and the extent to which their revisions addressed that feedback. The teams revised their research proposal drafts after receiving AI-enabled feedback developed by their instructor with the aid of a custom AI tool. Results: Our findings show that AI-enabled feedback prompted actionable revision across both teams. Each team addressed approximately half of the feedback comments, indicating comparable levels of uptake despite their differing approaches to revision. We observed that both teams were inconsistent in providing the additional information and reasoning requested in the feedback, and they struggled to address comments requiring self-evaluation. Taken together, we see that students often addressed the more easily actionable parts of AI-enabled feedback while omitting more demanding revisions. Conclusions: These findings provide behavioral evidence of how students used AI-enabled feedback to revise their scientific writing. The observed patterns suggest that the content of AI-enabled feedback supported meaningful revision, while limitations in students agency, strategies, and volition likely shaped their implementation of comments. Although the degree of uptake observed in this study is encouraging, it is unlikely that a single application of generative AI within the feedback process will resolve all the challenges associated with feedback uptake.
ER -