发表机构
The University of Melbourne; Monash University（墨尔本大学; 莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究设计并试点了一个生成式人工智能练习平台，在大型数学课堂中提供即时、有支架的反馈，通过将人工监督转移到事先验证来解决即时性与可信度之间的张力，并探索学生参与度及跨学科经验迁移。
AI 中文摘要
及时且具体的反馈是对学生学习影响最强的因素之一，然而在大型电气工程课堂中，学生与演示者的比例很高，学习者卡住时可能需要等待数天才能发现方法错误的原因，因此难以维持这种反馈。生成式人工智能（GenAI）提供了一种扩展对话式反馈的途径，但使用它来评分评估作业会引发信任和责任问题，而让人参与其中以确保其判断的准确性，则会重新引入削弱反馈价值的延迟。这导致了即时性（使反馈具有影响力）与人工监督（使其可信）之间的张力。在这项工作中，我们旨在通过设计和试点一个GenAI练习平台来实际解决这一张力，该平台在自主练习期间提供即时、有支架的反馈。这将人工监督从实时评分转移到对解决方案的事先验证。我们的目标是了解学生如何使用该工具，他们如何感知其反馈的价值和可靠性，以及哪些经验可迁移到其他工程学科。
英文摘要
Timely and specific feedback is one of the strongest influences on student learning, yet it is difficult to sustain in large electrical engineering classes where the ratio of students to demonstrators is high and a learner who is stuck may wait days to find out why an approach was wrong. Generative Artificial Intelligence (GenAI) offers a way to scale conversational feedback, but using it to grade assessed work raises trust and accountability concerns, and keeping a human in the loop to assure its judgements reintroduces the very delay that erodes the value of feedback. The result is a tension between the immediacy that makes feedback so impactful and the human oversight that makes it trustworthy. In this work, we set out to resolve that tension in practice by designing and piloting a GenAI practice platform that delivers immediate, scaffolded feedback during self-directed practice. This relocates human oversight from real-time grading to the upfront verification of solutions. Our goal was to understand how students engaged with the tool, how they perceived the value and reliability of its feedback, and what lessons transfer to other engineering subjects.
