Explainable Writing Scores via Fine-grained, LLM-Generated Features

James V. Bruno, Lee Becker

Abstract: Advancements in deep learning have enhanced Automated Essay Scoring (AES) accuracy but reduced interpretability. This paper investigates using LLM-generated features to train an explainable scoring model. By framing feature engineering as prompt engineering, state-of-the-art language technology can be integrated into simpler, more interpretable AES models.