Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback

TL;DR

This paper introduces a modular retrieval-augmented generation system for automatic short answer scoring with feedback, achieving better accuracy and adaptability in zero-shot and few-shot scenarios without extensive fine-tuning.

Contribution

It presents a novel RAG-based approach for ASAS with feedback that is adaptable, scalable, and reduces reliance on resource-intensive fine-tuning and prompt engineering.

Findings

9% improvement in scoring accuracy on unseen questions

Effective feedback generation in zero-shot and few-shot settings

Reduced need for extensive prompt engineering

Abstract

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.