@inproceedings{yang-gulbahar-2025-automatic,
    title = "Automatic Grading of Student Work Using Simulated Rubric-Based Data and {G}en{AI} Models",
    author = "Yang, Yiyao  and
      Gulbahar, Yasemin",
    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.5/",
    pages = "34--39",
    ISBN = "979-8-218-84229-1",
    abstract = "Grading assessment in data science faces challenges related to scalability, consistency, and fairness. Synthetic dataset and GenAI enable us to simulate realistic code samples and automatically evaluate using rubric-driven systems. The research proposes an automatic grading system for generated Python code samples and explores GenAI grading reliability through human-AI comparison."
}