@inproceedings{ribeiro-flucht-etal-2025-framework,
    title = "A Framework for Proficiency-Aligned Grammar Practice in {LLM}-Based Dialogue Systems",
    author = "Ribeiro-Flucht, Luisa  and
      Chen, Xiaobin  and
      Meurers, Detmar",
    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-1.74/",
    pages = "978--987",
    ISBN = "979-8-89176-270-1",
    abstract = "Communicative practice is critical for second language development, yet learners often lack targeted, engaging opportunities to use new grammar structures. While large language models (LLMs) can offer coherent interactions, they are not inherently aligned with pedagogical goals or proficiency levels. In this paper, we explore how LLMs can be integrated into a structured framework for contextually-constrained, grammar-focused interaction, building on an existing goal-oriented dialogue system. Through controlled simulations, we evaluate five LLMs across 75 A2-level tasks under two conditions: (i) grammar-targeted, task-anchored prompting and (ii) the addition of a lightweight post-generation validation pipeline using a grammar annotator.Our findings show that template-based prompting alone substantially increases target-form coverage up to 91.4{\%} for LLaMA 3.1-70B-Instruct, while reducing overly advanced grammar usage. The validation pipeline provides an additional boost in form-focused tasks, raising coverage to 96.3{\%} without significantly degrading appropriateness."
}