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

Researchers have developed a framework that uses large language models to deliver just-in-time adaptive feedback to students by grounding the AI with domain-specific expert knowledge. Deployed in a university course with over 1,000 students, the system improved student performance by more than 80% compared to previous semesters. The framework analyzes students' written reasoning to identify errors and provides non-intrusive feedback, with iterative LLM conversations helping shift misconceptions toward correct understanding.

arXiv:2605.26405v1 Announce Type: new 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.