@inproceedings{bruno-becker-2025-explainable,
    title = "Explainable Writing Scores via Fine-grained, {LLM}-Generated Features",
    author = "Bruno, James V  and
      Becker, Lee",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress",
    month = oct,
    year = "2025",
    address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.aimecon-wip.19/",
    pages = "155--165",
    ISBN = "979-8-218-84229-1",
    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."
}