@inproceedings{gui-2025-develop,
    title = "Develop a Generic Essay Scorer for Practice Writing Tests of Statewide Assessments",
    author = "Gui, Yi",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
    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/ingest-emnlp/2025.aimecon-main.8/",
    pages = "58--81",
    ISBN = "979-8-218-84228-4",
    abstract = "This study examines whether NLP transfer learning techniques, specifically BERT, can be used to develop prompt-generic AES models for practice writing tests. Findings reveal that fine-tuned DistilBERT, without further pre-training, achieves high agreement (QWK {\ensuremath{\approx}} 0.89), enabling scalable, robust AES models in statewide K-12 assessments without costly supplementary pre-training."
}