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.

本研究探讨了使用大型语言模型（LLMs）对 K-12 科学问答进行评分和解释的方法。通过结合少样本学习、主动学习和推理链条，采用 GPT-4 自动评分中学地球科学问答，我们成功地对作业答案进行评分并提供了有意义的解释，进一步系统分析了该方法的优缺点，揭示了人与机联合评分技术提升开放式科学评估的潜力。