[2603.19276] From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG

Computer Science > Computation and Language arXiv:2603.19276 (cs) [Submitted on 28 Feb 2026] Title:From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG Authors:Yucheng Chu, Haoyu Han, Shen Dong, Hang Li, Kaiqi Yang, Yasemin Copur-Gencturk, Joseph Krajcik, Namsoo Shin, Hui Liu View a PDF of the paper titled From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG, by Yucheng Chu and 8 other authors View PDF HTML (experimental) Abstract:Automated short answer grading (ASAG) is critical for scaling educational assessment, yet large language models (LLMs) often struggle with hallucinations and strict rubric adherence due to their reliance on generalized pre-training. While Rretrieval-Augmented Generation (RAG) mitigates these issues, standard "flat" vector retrieval mechanisms treat knowledge as isolated fragments, failing to capture the structural relationships and multi-hop reasoning essential for complex educational content. To address this limitation, we introduce a Graph Retrieval-Augmented Generation (GraphRAG) framework that organizes reference materials into a structured knowledge graph to explicitly model dependencies between concepts. Our methodology employs a dual-phase pipeline: utilizing Microsoft GraphRAG for high-fidelity graph construction and the HippoRAG neurosymbolic algorithm to execute associative graph traversals, thereby retrieving comprehensive, connected subgraphs of evidence. Experimental evaluations o...