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    <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.</abstract>
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%0 Conference Proceedings
%T Explainable Writing Scores via Fine-grained, LLM-Generated Features
%A Bruno, James V.
%A Becker, Lee
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84229-1
%F bruno-becker-2025-explainable
%X 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.
%U https://aclanthology.org/2025.aimecon-wip.19/
%P 155-165

Markdown (Informal)

[Explainable Writing Scores via Fine-grained, LLM-Generated Features](https://aclanthology.org/2025.aimecon-wip.19/) (Bruno & Becker, AIME-Con 2025)

• Explainable Writing Scores via Fine-grained, LLM-Generated Features (Bruno & Becker, AIME-Con 2025)

ACL

• James V Bruno and Lee Becker. 2025. Explainable Writing Scores via Fine-grained, LLM-Generated Features. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 155–165, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).