Unpacking Online Feedback Uptake: How Students Use Instructor, Peer, and AI-Generated Feedback for Writing Revisions

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Abstract

Feedback is essential to academic writing development, yet how university students engage with multiple feedback sources, particularly in online environments that blend human and AI-generated input, remains underexplored. We analyzed how six undergraduate students revised their research papers in response to instructor annotations, instructor end comments, peer reviews, and generative AI tools. Feedback uptake was coded by source, type, operation, and quality. Findings revealed clear differences across feedback sources: instructor feedback prompted the most substantive and successful revisions; AI feedback supported sentence-level and stylistic refinements during iterative drafting; peer feedback had minimal impact due to its low directive value. Implications call for multi-source feedback strategies that scaffold both conceptual development and language refinement in academic writing instruction.

Authors

• Albert W. Li, University of California - Irvine

• Penelope Collins, University of California - Irvine