Instructor-Aligned Knowledge Graphs for Personalized Learning

2026-02-19

AI Summary

This work addresses the challenge of accurately identifying student knowledge gaps and enabling personalized interventions in large-scale courses, where fine-grained concept dependency structures aligned with instructors’ pedagogical intent are often absent. To bridge this gap, the paper proposes a novel approach that integrates temporal and semantic signals from instructional materials with the generalization capabilities of large language models to automatically construct fine-grained knowledge graphs that explicitly model prerequisite, part-whole, and other learning dependencies among core concepts. Experiments across multiple real-world courses demonstrate that the resulting knowledge graphs faithfully reconstruct the intended learning progression envisioned by instructors. Human evaluations further confirm high alignment with teaching logic and strong practical utility, offering a reliable foundation for personalized learning path recommendation.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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 nodes and infers learning dependencies as directed edges (e.g., "part-of" or "depends-on" relationships). The framework synergizes the rich temporal and semantic signals unique to educational materials (e.g., "recursion" is taught before "mergesort"; "recursion" is mentioned in the definition of "merge sort") with the generalizability of large language models. Through experiments on real-world, diverse lecture materials across multiple courses and human-based evaluation, we demonstrate that InstructKG captures rich, instructor-aligned learning progressions.

Problem

• knowledge graph
• personalized learning
• learning dependencies
• conceptual relationships
• instructor-aligned

Innovation

• knowledge graph
• personalized learning
• instructor-aligned
• learning dependencies
• large language models