This paper explores the use of large language models (LLMs) to score and explain short-answer assessments in K-12 science. While existing methods can score more structured math and computer science assessments, they often do not provide explanations for the scores. Our study focuses on employing GPT-4 for automated assessment in middle school Earth Science, combining few-shot and active learning with chain-of-thought reasoning. Using a human-in-the-loop approach, we successfully score and provide meaningful explanations for formative assessment responses. A systematic analysis of our method's pros and cons sheds light on the potential for human-in-the-loop techniques to enhance automated grading for open-ended science assessments.
@article{arxiv.2403.14565,
title = {A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science},
author = {Clayton Cohn and Nicole Hutchins and Tuan Le and Gautam Biswas},
journal= {arXiv preprint arXiv:2403.14565},
year = {2024}
}
Comments
In press at EAAI-24: The 14th Symposium on Educational Advances in Artificial Intelligence