@inproceedings{anagnostopoulou-etal-2025-human,
    title = "Human and {LLM}-based Assessment of Teaching Acts in Expert-led Explanatory Dialogues",
    author = "Anagnostopoulou, Aliki  and
      Feldhus, Nils  and
      Hsu, Yi-Sheng  and
      Alshomary, Milad  and
      Wachsmuth, Henning  and
      Sonntag, Daniel",
    editor = "Strube, Michael  and
      Braud, Chloe  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Loaiciga, Sharid  and
      Zeldes, Amir  and
      Li, Chuyuan",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.codi-1.15/",
    pages = "166--181",
    ISBN = "979-8-89176-343-2",
    abstract = "Understanding the strategies that make expert-led explanations effective is a core challenge in didactics and a key goal for explainable AI. To study this computationally, we introduce ReWIRED, a large corpus of explanatory dialogues annotated by education experts with fine-grained, span-level teaching acts across five levels of explainee knowledge. We use this resource to assess the capabilities of modern language models, finding that while few-shot LLMs struggle to label these acts, fine-tuning is a highly effective methodology. Moving beyond structural annotation, we propose and validate a suite of didactic quality metrics. We demonstrate that a prompt-based evaluation using an LLM as a ``judge'' is required to capture how the functional quality of an explanation aligns with the learner{'}s expertise {--} a nuance missed by simpler static metrics. Together, our dataset, modeling insights, and evaluation framework provide a comprehensive methodology to bridge pedagogical principles with computational discourse analysis."
}