发表机构
The University of Queensland; University of Surrey; The University of Hong Kong（昆士兰大学; 萨里大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过13037名学生的大规模准实验，发现主动构建的AI反馈执行工作流可显著提升学生对AI反馈的接受度、自我评估信心及作品质量，强调需设计针对性AI反馈工作流。
AI 中文摘要
反馈过程对学生学习有重要影响，但其教育价值取决于解决两个不同的挑战：大规模提供高质量、及时且个性化的反馈，以及支持学生有效解读、评估和运用该反馈。生成式AI为解决反馈提供挑战提供了可行手段，但学生对AI生成反馈的接受度仍然有限。我们开展了一项大规模准实验性序贯队列研究，在13037名学生和51296份学生自主创作的资源中对比三种AI介导的反馈工作流：定向反馈组（n=3723），学生接收AI生成的反馈意见但无结构化支持；自主定向反馈组（n=3951），学生可发起可选的AI支持对话；执行反馈组（n=5363），学生需选择反馈建议、评估其相关性，并围绕所选内容开展针对性的AI支持对话。执行反馈组的AI生成反馈接受概率显著更高，估计概率为26.2%，而定向反馈组为14.1%，自主定向反馈组仅为0.1%。与另外两组相比，执行反馈组还具有显著更高的自我评估信心和提交作品质量。这些发现表明，AI生成反馈的教育价值不仅取决于反馈意见的质量，还取决于主动构建学生反馈素养过程执行的工作流。研究结果对AI反馈系统的设计具有启示意义，这类系统应将学习者定位为判断、对话和改进的主动参与者，而非反馈意见的被动接受者。总体结论显示，仅提供AI访问权限是不够的，有针对性的工作流设计是有效运用反馈的核心。
英文摘要
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.
