An Explanation-Based Classroom Response System for Real-Time Analysis of Undergraduate Students’ Natural Language Explanations
Jordan Esiason, Priyanka Khare, Claire Aguiar, Dan Carpenter, Wookhee Min, Seung Lee, Gamze Ozogul, Xiaoying Zheng
Abstract
Effective classroom teaching requires instructors to be responsive to their students, such as by pivoting their lectures in real-time to address common misconceptions that their students may have developed. Classroom response systems such as multiple-choice "clicker" systems are one method by which instructors can gauge their students’ understanding during classroom lectures, but open-ended questions that prompt students to engage in self-explanation are better suited to promoting critical thinking. Additionally, analyzing students’ natural language responses typically requires time-consuming manual analysis, which makes it challenging to implement in a classroom setting. To address this challenge, we present an LLM-driven method for automatically assessing students
BibTeX
@inproceedings{aaai2026_anexplanationbas,
title = {An Explanation-Based Classroom Response System for Real-Time Analysis of Undergraduate Students’ Natural Language Explanations},
author = {Jordan Esiason and Priyanka Khare and Claire Aguiar and Dan Carpenter and Wookhee Min and Seung Lee and Gamze Ozogul and Xiaoying Zheng and James Lester},
booktitle = {AAAI 2026},
year = {2026}
}