@inproceedings{lee-etal-2026-towards,
    title = "Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded {LLM}",
    author = "Lee, Younghun  and
      Bralin, Amir  and
      Rebello, Nobel Sanjay  and
      Goldwasser, Dan",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.8/",
    pages = "93--107",
    ISBN = "979-8-89176-409-5",
    abstract = "Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback at scale, current studies lack clear methodologies for providing Just-in-Time (JiT) feedback in authentic instructional settings. In this paper, we present a framework that provides adaptive feedback by grounding LLMs with domain-specific expert knowledge. Our approach collects written reasoning logic (strategy essays) from students, analyzes potential error types based on the content of that reasoning, and delivers non-intrusive feedback designed to clarify missing or incorrect concepts. We deploy this framework in a large-scale college course (N {\ensuremath{>}} 1,000), where it improved student performance by over 80{\%} compared to previous semesters. Lastly, we validate the framework{'}s pedagogical utility by analyzing the learning trajectories; we demonstrate how iterative conversations with LLM facilitate shifting one{'}s misconception to correct understanding."
}