How Do Biology Undergraduates Use AI-enabled Feedback to Revise Scientific Writing?

Instructors and education researchers are increasingly leveraging genAI to support feedback practices, yet we still know relatively little about how students use AI-enabled feedback. This gap is consequential because students must actively interpret and use feedback to benefit from it. This study examined how two teams of undergraduate biology students used AI-enabled feedback to revise their research proposals. The teams revised their proposal drafts after receiving AI-enabled feedback developed by a GPT and subsequently reviewed and edited by the instructor. 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 revision approaches. This pattern indicates that both teams were inconsistent in taking up the additional information and reasoning highlighted in the feedback, reflecting challenges similar to those commonly observed with traditional feedback. These findings provide behavioral evidence of how students used AI-enabled feedback to revise their scientific writing and demonstrates ways that prior research on feedback use and barriers can extend to AI-enabled feedback.