一个利用大语言模型辅助的教育工具，用于对开放式回答提供反馈
A large language model-assisted education tool to provide feedback on open-ended responses
AI总结：
本文提出一款基于大语言模型的教育工具，在教师定义标准引导下自动为开放式问题回答提供快速个性化反馈，并开源了Web应用和Jupyter Notebook小部件两种实现。
摘要：
开放式问题是教师评估学生理解程度并鼓励对课程材料进行批判性探索的常用工具。为这类回答提供反馈是一项耗时任务，可能导致教师不堪重负并降低反馈质量。许多教师转而采用更简单的题型，如选择题，虽能提供即时反馈，却牺牲了个性化和有深度的评论。在此，我们提出一款工具，利用大语言模型（LLMs），在教师定义的标准引导下，自动对开放式问题进行回复。我们的工具提供快速个性化反馈，使学生能迅速检验知识并找出需改进之处。我们提供开源参考实现，既可作为Web应用程序，也可作为Jupyter Notebook小部件，配合教学编程或数学笔记本使用。在教师指导下，LLMs有望提升学生学习成果并改进教学方法。
英文摘要：
Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such responses is a time-consuming task that can lead to overwhelmed instructors and decreased feedback quality. Many instructors resort to simpler question formats, like multiple-choice questions, which provide immediate feedback but at the expense of personalized and insightful comments. Here, we present a tool that uses large language models (LLMs), guided by instructor-defined criteria, to automate responses to open-ended questions. Our tool delivers rapid personalized feedback, enabling students to quickly test their knowledge and identify areas for improvement. We provide open-source reference implementations both as a web application and as a Jupyter Notebook widget that can be used with instructional coding or math notebooks. With instructor guidance, LLMs hold promise to enhance student learning outcomes and elevate instructional methodologies.
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。
