This study evaluates large language models (LLMs) for automated essay scoring (AES), comparing prompt strategies and fairness across student groups. We found that well-designed prompting helps LLMs approach traditional AES performance, but both differ from human scores for ELLs—the traditional model shows larger overrall gaps, while LLMs show subtler disparities.