大学数学自动反馈生成：一种AI教学助教的开发与评估
Automated Feedback Generation for Undergraduate Mathematics: Development and Evaluation of an AI Teaching Assistant
AI总结：
本文提出了一种基于AI的教学助手系统，用于生成大学数学作业的自动反馈，通过模块化工作流和大型语言模型实现了对技术正确性和风格的评估。
摘要：
智能辅导系统长期以来能够为学生在严格结构化格式下提交的工作提供即时自动反馈，当问题非常受限时。但可靠地评估自由形式的数学推理仍具有挑战性。我们提出了一种系统，能够处理自由形式的自然语言输入，处理广泛的边缘情况，并能对提交的证明的技术正确性以及风格和呈现问题进行评论。我们讨论了各种评估此类系统的不同方法的优缺点，并表明根据我们评估的指标，生成的反馈质量与评估早期本科生作业的人类专家相当。我们通过一组更高级和不寻常的问题对系统进行压力测试，并在更具有挑战性的设置中报告了显著的差距和鼓励的成功。我们的系统使用大型语言模型在模块化的工作流中进行处理。工作流配置是人类可读且可编辑的，无需编程知识，允许某些中间步骤由教师预计算或注入。我们的工具的一个版本已部署在帝国学院数学作业平台Lambdafeedback上。我们还报告了该工具与该平台的集成情况。
英文摘要：
Intelligent tutoring systems have long enabled automated immediate feedback on student work when it is presented in a tightly structured format and when problems are very constrained, but reliably assessing free-form mathematical reasoning remains challenging. We present a system that processes free-form natural language input, handles a wide range of edge cases, and comments competently not only on the technical correctness of submitted proofs, but also on style and presentation issues. We discuss the advantages and disadvantages of various approaches to the evaluation of such a system, and show that by the metrics we evaluate, the quality of the feedback generated is comparable to that produced by human experts when assessing early undergraduate homework. We stress-test our system with a small set of more advanced and unusual questions, and report both significant gaps and encouraging successes in that more challenging setting. Our system uses large language models in a modular workflow. The workflow configuration is human-readable and editable without programming knowledge, and allows some intermediate steps to be precomputed or injected by the instructor. A version of our tool is deployed on the Imperial mathematics homework platform Lambdafeedback. We report also on the integration of our tool into this platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
