@inproceedings{lyu-2025-cu,
    title = "{CU} at {BEA} 2025 Shared Task: A {BERT}-Based Cross-Attention Approach for Evaluating Pedagogical Responses in Dialogue",
    author = "Lyu, Zhihao",
    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-1.81/",
    pages = "1060--1072",
    ISBN = "979-8-89176-270-1",
    abstract = "Automatic evaluation of AI tutor responses in educational dialogues is a challenging task, requiring accurate identification of mistakes and the provision of pedagogically effective guidance. In this paper, we propose a classification model based on BERT, enhanced with a cross-attention mechanism that explicitly models the interaction between the tutor{'}s response and preceding dialogue turns. This design enables better alignment between context and response, supporting more accurate assessment along the educational dimensions defined in the BEA 2025 Shared Task. To address the substantial class imbalance in the dataset, we employ data augmentation techniques for minority classes. Our system consistently outperforms baseline models across all tracks. However, performance on underrepresented labels remains limited, particularly when distinguishing between semantically similar cases. This suggests room for improvement in both model expressiveness and data coverage, motivating future work with stronger decoder-only models and auxiliary information from systems like GPT-4.1. Overall, our findings offer insights into the potential and limitations of LLM-based approaches for pedagogical feedback evaluation."
}