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

This paper develops a human-in-the-loop chain-of-thought prompting approach using GPT-4 to automatically score and generate explanations for middle school Earth Science formative assessment responses. The method combines few-shot learning, active learning, and chain-of-thought reasoning to evaluate open-ended short-answer student responses.

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 provid

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