Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers
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…
In a study examining the impact of AI-enabled feedback on scientific writing revisions, two teams of undergraduate biology students were tasked with revising their research proposal drafts following AI-assisted feedback. The results indicate that AI-enabled feedback led to actionable revisions in both teams, with each addressing roughly half of the feedback comments.
Despite their distinct approaches, the teams exhibited similar levels of uptake in utilizing the AI-generated input. However, inconsistencies emerged in the students' revisions, as they frequently failed to provide the additional context and reasoning requested by the AI feedback. Moreover, the students encountered difficulties when tasked with self-evaluation, a critical aspect of scientific writing.
These findings suggest that while AI-enabled feedback can facilitate meaningful revisions, it is not without its limitations. The content of the feedback proved instrumental in driving revision, yet factors such as students' agency, strategies, and volition significantly influenced how they implemented the feedback. Although the scope of this study provides encouraging evidence of AI's role in enhancing scientific writing skills, it also highlights the need for continued exploration into strategies that can overcome the identified barriers to full implementation.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
