Instructor-Aligned Knowledge Graphs for Personalized Learning
Abdulrahman AlRabah, Priyanka Kargupta, Jiawei Han, Abdussalam Alawini

TL;DR
This paper introduces InstructKG, a framework that automatically constructs detailed, instructor-aligned knowledge graphs from lecture materials to model learning dependencies and support personalized education at scale.
Contribution
InstructKG leverages educational signals and large language models to generate detailed knowledge graphs aligned with instructors' intended learning progressions.
Findings
InstructKG effectively captures rich learning dependencies from diverse lecture materials.
The generated knowledge graphs align well with instructor intentions based on human evaluation.
InstructKG demonstrates applicability across multiple courses and educational contexts.
Abstract
Mastering educational concepts requires understanding both their prerequisites (e.g., recursion before merge sort) and sub-concepts (e.g., merge sort as part of sorting algorithms). Capturing these dependencies is critical for identifying students' knowledge gaps and enabling targeted intervention for personalized learning. This is especially challenging in large-scale courses, where instructors cannot feasibly diagnose individual misunderstanding or determine which concepts need reinforcement. While knowledge graphs offer a natural representation for capturing these conceptual relationships at scale, existing approaches are either surface-level (focusing on course-level concepts like "Algorithms" or logistical relationships such as course enrollment), or disregard the rich pedagogical signals embedded in instructional materials. We propose InstructKG, a framework for automatically…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics
