  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.
