Evaluating Students' Open-ended Written Responses With Llms: Using The RAG Framework For GPT-3.5, GPT-4, Claude-3, And Mistral-large

Abstract

Evaluating open-ended written examination responses from students is an essential yet time-intensive task for educators, requiring a high degree of effort, consistency, and precision. Recent developments in Large Language Models (LLMs) present a promising opportunity to balance the need for thorough evaluation with efficient use of educators' time. In our study, we explore the effectiveness of LLM