Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM

• URL: http://arxiv.org/abs/2605.26405v1
• Date: Tue, 26 May 2026 00:30:19 GMT
• Title: Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM
• Abstract summary: We present a framework that provides adaptive feedback by grounding Large Language Models with domain-specific expert knowledge.<n>We deploy this framework in a large-scale university course (N > 1000), where it improved student performance by over 80% compared to previous semesters.
• Score: 16.09740388389993
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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