A large language model-assisted education tool to provide feedback on open-ended responses

作者：Jordan Matelsky, Felipe Parodi, Tony Liu, Richard D. Lange, Konrad P. Körding · 发表于：arXiv (Cornell University) · 年份：2023 · DOI：10.48550/arxiv.2308.02439 · 被引用次数：11 · 研究领域：Topic Modeling、Online Learning and Analytics、Natural Language Processing Techniques

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.