Instructor-Aligned Knowledge Graphs for Personalized Learning

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 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.