@inproceedings{jung-etal-2025-input,
    title = "Input Optimization for Automated Scoring in Reading Assessment",
    author = "Jung, Ji Yoon  and
      Bezirhan, Ummugul  and
      von Davier, Matthias",
    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.1/",
    pages = "1--8",
    ISBN = "979-8-218-84228-4",
    abstract = "This study examines input optimization for enhanced efficiency in automated scoring (AS) of reading assessments, which typically involve lengthy passages and complex scoring guides. We propose optimizing input size using question-specific summaries and simplified scoring guides. Findings indicate that input optimization via compression is achievable while maintaining AS performance."
}