@inproceedings{correa-busquets-etal-2025-ialab,
    title = "{IAL}ab {UC} at {BEA} 2025 Shared Task: {LLM}-Powered Expert Pedagogical Feature Extraction",
    author = "Correa Busquets, Sof{\'i}a  and
      C{\'o}rdova V{\'e}liz, Valentina  and
      Baier, Jorge",
    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/landing_page/2025.bea-1.94/",
    pages = "1187--1193",
    ISBN = "979-8-89176-270-1",
    abstract = "As AI{'}s presence in educational environments grows, it becomes critical to evaluate how its feedback may impact students' learning processes. Pedagogical theory, with decades of effort into understanding how human instructors give good-quality feedback to students, may provide a rich source of insight into feedback automation. In this paper, we propose a novel architecture based on pedagogical-theory feature extraction from the conversation history and tutor response to predict pedagogical guidance on MRBench. Such features are based on Brookhart{'}s canonical work in pedagogical theory, and extracted by prompting the language model LearnLM. The features are then used to train a random-forest classifier to predict the `providing guidance' dimension of the MRBench dataset. Our approach ranked 8th in the dimension{'}s leaderboard with a test Macro F1-score of {\textasciitilde}0.54. Our work provides some evidence in support of using pedagogical theory qualitative factors treated separately to provide clearer guidelines on how to improve low-scoring intelligent tutoring systems. Finally, we observed several inconsistencies between pedagogical theory and MRBench{'}s inherent relaxation of the tutoring problem implied in evaluating on a single-conversation basis, calling for the development of more elaborate measures which consider student profiles to serve as true heuristics of AI tutors' usefulness."
}