@inproceedings{tian-etal-2025-implementation,
    title = "Implementation Considerations for Automated {AI} Grading of Student Work",
    author = "Tian, Zewei  and
      Liu, Alex  and
      Esbenshade, Lief  and
      Sarkar, Shawon  and
      Zhang, Zachary  and
      He, Kevin  and
      Sun, Min",
    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.2/",
    pages = "9--20",
    ISBN = "979-8-218-84228-4",
    abstract = "19 K-12 teachers participated in a co-design pilot study of an AI education platform, testing assessment grading. Teachers valued AI{'}s rapid narrative feedback for formative assessment but distrusted automated scoring, preferring human oversight. Students appreciated immediate feedback but remained skeptical of AI-only grading, highlighting needs for trustworthy, teacher-centered AI tools."
}