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[2602.17111] Instructor-Aligned Knowledge Graphs for Personalized Learning
Summary
This article presents InstructKG, a framework for creating instructor-aligned knowledge graphs that enhance personalized learning by mapping educational concepts and their dependencies.
Why It Matters
The development of InstructKG addresses the challenge of identifying knowledge gaps in large-scale educational settings. By leveraging knowledge graphs, it enables targeted interventions, improving learning outcomes and making personalized education more feasible.
Key Takeaways
- InstructKG constructs knowledge graphs from lecture materials to represent learning progressions.
- It captures dependencies between educational concepts, aiding in personalized learning.
- The framework utilizes large language models to enhance the extraction of pedagogical signals.
- Experiments show InstructKG's effectiveness across diverse courses.
- This approach can significantly improve how instructors diagnose and address student misunderstandings.
Computer Science > Artificial Intelligence arXiv:2602.17111 (cs) [Submitted on 19 Feb 2026] Title:Instructor-Aligned Knowledge Graphs for Personalized Learning Authors:Abdulrahman AlRabah, Priyanka Kargupta, Jiawei Han, Abdussalam Alawini View a PDF of the paper titled Instructor-Aligned Knowledge Graphs for Personalized Learning, by Abdulrahman AlRabah and 3 other authors View PDF 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 constructing instructor-aligned knowledge graphs that capture a course's intended learning progression. Given a course's lecture materials (slides, notes, etc.), InstructKG extracts significant concepts as no...
