  Large Language Models (LLMs) have shown promise in Automated Essay Scoring
(AES), but their zero-shot and few-shot performance often falls short compared
to state-of-the-art models and human raters. However, fine-tuning LLMs for each
specific task is impractical due to the variety of essay prompts and rubrics
used in real-world educational contexts. This study proposes a novel approach
combining LLMs and Comparative Judgment (CJ) for AES, using zero-shot prompting
to choose between two essays. We demonstrate that a CJ method surpasses
traditional rubric-based scoring in essay scoring using LLMs.
