Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM
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Computer Science > Computation and Language
Title:Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM
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 university course (N > 1000), 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.
| Comments: | 8 pages, Accepted to 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.26405 [cs.CL] |
| (or arXiv:2605.26405v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26405
arXiv-issued DOI via DataCite (pending registration)
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