设计有效反馈流程，让生成式AI的建议真正被学生采纳并提升学习效果。
Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment
- 通过三类反馈流程对比，发现主动参与式设计能显著提升学生对AI反馈的采纳率。
- 主动参与组学生采纳率达26.2%，自评信心和作业质量均显著优于其他组。
- 适合教育科技研发者与教师参考，优化AI辅助教学中的互动设计。
反馈过程深刻影响学习成效，但其价值取决于两大挑战：大规模提供高质量、及时且个性化的反馈，以及支持学生理解、评估并有效执行反馈。生成式AI可解决前者，但学生对AI反馈的采纳仍有限。本研究开展大规模准实验序列队列研究，比较三种AI辅助反馈流程在13,037名学生、51,296份作业中的表现。在定向反馈组（n=3,723）中，学生接收无结构支持的AI反馈；在自主反馈组（n=3,951）中，学生可自主发起可选的AI对话；在践行反馈组（n=5,363）中，学生需选择反馈建议、评估相关性，并围绕选定内容进行目标导向的AI对话。践行反馈组的AI反馈采纳率高达26.2%，显著高于定向反馈组的14.1%和自主反馈组的0.1%。该组还表现出更高的自评信心与作业质量。结果表明，AI反馈的教育价值不仅取决于反馈质量，更依赖于能引导学生主动实践反馈素养的流程设计。这提示应将学习者定位为判断、对话与改进的积极参与者，而非被动接收者。仅提供AI访问不足；精心设计的工作流才是有效利用反馈的关键。
原文摘要 · Abstract (English)
Feedback processes strongly influence student learning, yet their educational value depends on addressing two distinct challenges: providing high-quality, timely, and individualised feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively. Generative AI offers a credible means of addressing the provision challenge, but students' uptake of AI-generated feedback remains limited. We conducted a large-scale quasi-experimental sequential cohort study comparing three AI-mediated feedback workflows across 13,037 students and 51,296 student-authored resources. In Directed Feedback (n = 3,723), students received AI-generated feedback comments without structured support. In Self-Directed Feedback (n = 3,951), students could initiate optional AI-supported dialogue. In Enacted Feedback (n = 5,363), students were prompted to select feedback suggestions, evaluate their relevance, and engage in targeted AI-supported dialogue anchored to those selections. Enacted Feedback was associated with significantly higher uptake of AI-generated feedback, with an estimated probability of 26.2%, compared with 14.1% for Directed Feedback and 0.1% for Self-Directed Feedback. It was also associated with significantly higher self-assessment confidence and submitted-work quality than both comparison conditions. These findings suggest that the educational value of AI-generated feedback depends not only on the quality of feedback comments, but also on workflows that actively structure students' enactment of feedback literacy processes. The results have implications for the design of AI feedback systems that position learners as active participants in judgement, dialogue, and improvement rather than passive recipients of comments. Overall findings show that AI access alone is insufficient; purposeful workflow design is central to productive feedback use.
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