@inproceedings{an-etal-2025-blcu,
    title = "{BLCU}-{ICALL} at {BEA} 2025 Shared Task: Multi-Strategy Evaluation of {AI} Tutors",
    author = "An, Jiyuan  and
      Fu, Xiang  and
      Liu, Bo  and
      Zong, Xuquan  and
      Kong, Cunliang  and
      Liu, Shuliang  and
      Wang, Shuo  and
      Liu, Zhenghao  and
      Yang, Liner  and
      Fan, Hanghang  and
      Yang, Erhong",
    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-1.84/",
    pages = "1084--1097",
    ISBN = "979-8-89176-270-1",
    abstract = "This paper describes our approaches for the BEA-2025 Shared Task on assessing pedagogical ability and attributing tutor identities in AI-powered tutoring systems. We explored three methodological paradigms: in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). Results indicate clear methodological strengths: SFT is highly effective for structured classification tasks such as mistake identification and feedback actionability, while ICL with advanced prompting excels at open-ended tasks involving mistake localization and instructional guidance. Additionally, fine-tuned models demonstrated strong performance in identifying tutor authorship. Our findings highlight the importance of aligning methodological strategy and task structure, providing insights toward more effective evaluations of educational AI systems."
}