@inproceedings{rezayi-etal-2025-automated,
    title = "Automated Scoring of Communication Skills in Physician-Patient Interaction: Balancing Performance and Scalability",
    author = "Rezayi, Saed  and
      Ha, Le An  and
      Zhou, Yiyun  and
      Houriet, Andrew  and
      D{'}Addario, Angelo  and
      Baldwin, Peter  and
      Harik, Polina  and
      King, Ann  and
      Yaneva, Victoria",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.66/",
    pages = "891--897",
    ISBN = "979-8-89176-270-1",
    abstract = "This paper presents an automated scoring approach for a formative assessment tool aimed at helping learner physicians enhance their communication skills through simulated patient interactions. The system evaluates transcribed learner responses by detecting key communicative behaviors, such as acknowledgment, empathy, and clarity. Built on an adapted version of the ACTA scoring framework, the model achieves a mean binary F1 score of 0.94 across 8 clinical scenarios. A central contribution of this work is the investigation of how to balance scoring accuracy with scalability. We demonstrate that synthetic training data offers a promising path toward reducing reliance on large, annotated datasets{---}making automated scoring more accurate and scalable."
}