Automatic short answer scoring (ASAS) helps reduce the grading burden on educators but often lacks detailed, explainable feedback. Existing methods in ASAS with feedback (ASAS-F) rely on fine-tuning language models with limited datasets, which is resource-intensive and struggles to generalize across contexts. Recent approaches using large language models (LLMs) have focused on scoring without extensive fine-tuning. However, they often rely heavily on prompt engineering and either fail to generate elaborated feedback or do not adequately evaluate it. In this paper, we propose a modular retrieval augmented generation based ASAS-F system that scores answers and generates feedback in strict zero-shot and few-shot learning scenarios. We design our system to be adaptable to various educational tasks without extensive prompt engineering using an automatic prompt generation framework. Results show an improvement in scoring accuracy by 9\% on unseen questions compared to fine-tuning, offering a scalable and cost-effective solution.

本研究解决了自动短答案评分（ASAS）在提供详细可解释反馈方面的不足。我们提出了一种基于模块化检索增强生成的ASAS-F系统，该系统能够在零-shot和少-shot学习场景下进行评分及生成反馈，不需要大量的提示工程。实验结果表明，该系统在未见问题的评分准确性上提升了9%，为教育任务提供了可扩展且具成本效益的解决方案。

超越评分：一种基于模块化检索增强生成的短答案自动评分系统及反馈