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arXiv · 2403.14565

A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science

Clayton Cohn·Nicole Hutchins·Tuan Le·Gautam Biswas
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Abstract

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.

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Clayton Cohn, Nicole Hutchins, Tuan Le, Gautam Biswas. 2024-03-21. A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science. https://doi.org/10.1609/aaai.v38i21.30364

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