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

Autor:	Cohn, Clayton, Hutchins, Nicole, Le, Tuan, Biswas, Gautam
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
DOI:	10.1609/aaai.v38i21.30364
Popis:	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. Comment: In press at EAAI-24: The 14th Symposium on Educational Advances in Artificial Intelligence
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.14565 Zobrazit plný text záznamu View this record from Arxiv