TreeQuestion: Assessing Conceptual Learning Outcomes with LLM-Generated Multiple-Choice Questions

Abstract

The advances of generative AI have posed a challenge for using open-ended questions to assess conceptual learning outcomes, as it is increasingly common for students to use tools like ChatGPT to generate long textual answers. However, teachers still have to spend substantial time reading the answers and inferring students' learning outcomes. We present TreeQuestion, a human-in-the-loop system designed to help teachers create a set of multiple-choice questions to assess students' conceptual learning outcomes. When a teacher seeks to assess students' comprehension of specific concepts, TreeQuestion taps into the wealth of knowledge embedded within large language models and generates a set of multiple-choice questions organized in a tree-like structure. We evaluated TreeQuestion with 96 students and 10 teachers. Results indicated that students achieved similar performance in multiple-choice questions generated by TreeQuestion and open-ended questions graded by teachers. Meanwhile, TreeQuestion could reduce teachers' efforts in creating and grading the multiple-choice questions in contrast to manually generated open-ended questions. We estimate that in a hypothetical class with 20 students, using multiple-choice questions from TreeQuestion may require only 4.6% of the time compared to open-ended questions for assessing learning outcomes.