@inproceedings{schultz-etal-2025-examining,
    title = "Examining decoding items using engine transcriptions and scoring in early literacy assessment",
    author = "Schultz, Zachary  and
      Young, Mackenzie  and
      Dugdale, Debbie  and
      Lottridge, Susan",
    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/ingest-emnlp/2025.aimecon-wip.23/",
    pages = "191--196",
    ISBN = "979-8-218-84229-1",
    abstract = "We investigate the reliability of two scoring approaches to early literacy decoding items, whereby students are shown a word and asked to say it aloud. Approaches were rubric scoring of speech, human or AI transcription with varying explicit scoring rules. Initial results suggest rubric-based approaches perform better than transcription-based methods."
}