AI辅助助教反馈，学生修改质量显著提升
AI-Mediated Feedback Improves Student Revisions: A Randomized Trial with FeedbackWriter in a Large Undergraduate Course
- AI生成建议供助教参考，可自由采纳或修改
- 采用更多AI建议的助教，学生修改质量更高
- 适合教育技术、智能辅导系统研究者
尽管对使用大模型生成写作反馈的兴趣日益增长，但关于学生对AI辅助反馈与人工反馈的反应仍知之甚少。我们在一门大型入门级经济学课程（N=354）中开展随机对照试验，引入并部署FeedbackWriter系统——该系统为助教提供AI建议，协助其批改知识密集型论文。助教可自主选择采纳、编辑或忽略建议。学生被随机分配至两组：一组接收助教手写反馈（基线），另一组接收助教结合AI建议的反馈。学生根据反馈修订稿件，修订稿再次评分。共完成1,366篇作文的评分。结果显示，接受AI辅助反馈的学生修订质量显著更高，且助教采纳的AI建议越多，提升越明显。助教认为AI建议有助于发现知识盲点并更清晰理解评分标准。
原文摘要 · Abstract (English)
Despite growing interest in using LLMs to generate feedback on students' writing, little is known about how students respond to AI-mediated versus human-provided feedback. We address this gap through a randomized controlled trial in a large introductory economics course (N=354), where we introduce and deploy FeedbackWriter - a system that generates AI suggestions to teaching assistants (TAs) while they provide feedback on students' knowledge-intensive essays. TAs have the full capacity to adopt, edit, or dismiss the suggestions. Students were randomly assigned to receive either handwritten feedback from TAs (baseline) or AI-mediated feedback where TAs received suggestions from FeedbackWriter. Students revise their drafts based on the feedback, which is further graded. In total, 1,366 essays were graded using the system. We found that students receiving AI-mediated feedback produced significantly higher-quality revisions, with gains increasing as TAs adopted more AI suggestions. TAs found the AI suggestions useful for spotting gaps and clarifying rubrics.
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