Instructor-Aligned Knowledge Graphs for Personalized Learning

arXiv:2602.17111v2 Announce Type: replace 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, especially in large-scale courses where instructors cannot feasibly diagnose individual misunderstanding. While knowledge graphs offer a natural representation for capturing these conceptual relationships at scale, existing approaches are either surface-level 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, 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") with the generalizability of large language models. Through experiments on diverse lecture materials, human evaluation, and a prerequisite-learning benchmark, we demonstrate that InstructKG captures rich, instructor-aligned learning progressions.