Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM
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 reas
The rapid advancement and accessibility of LLMs enable their application in diverse fields, including education, making such research timely.
This development indicates a tangible path towards integrating advanced AI into education beyond basic tools, potentially personalizing learning experiences at scale and improving outcomes.
The ability to provide knowledge-grounded, just-in-time adaptive feedback via LLMs shifts educational intervention from generic to highly personalized and immediate.
- · Education technology companies
- · Students
- · Educators
- · AI developers
- · Traditional educational feedback methods
- · Generic online learning platforms
Widespread adoption of AI-powered personalized learning systems in educational institutions.
A re-evaluation of teaching methodologies and the role of human educators in a highly individualized, AI-supported learning environment.
Potential for significantly narrowed achievement gaps and a more efficient, globally accessible education system.
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