  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.
