Automated grammar error diagnosis remains challenging due to the complexity of syntactic structures and semantic dependencies in natural language. This study proposes a novel framework that integrates Graph Convolutional Networks (GCNs) with domain-specific knowledge graphs for enhanced English grammar error detection and correction. The approach constructs sentence-level dependency graphs to explicitly model syntactic relationships, while a multi-layered grammar knowledge graph systematically organizes grammatical concepts, error taxonomies, and correction strategies. Multi-layer graph convolutions propagate contextual information across syntactic dependencies, and attention mechanisms dynamically weight node representations for diagnostic relevance. Knowledge graph integration enriches neural representations with structured linguistic knowledge, enabling both accurate error detection and interpretable feedback generation. Experimental evaluation on CoNLL-2014, JFLEG, and BEA-2019 benchmark datasets demonstrates marked improvements, achieving F1-scores of 0.6484, 0.6719, and 0.6367 respectively, outperforming the strongest baseline BERT+BiLSTM by approximately 8.8% on CoNLL-2014 and the competitive GECToR sequence-tagging system by 4.4%, with all gains confirmed as statistically significant through bootstrap resampling. The framework proves especially effective at identifying syntactic errors such as verb tense inconsistencies and subject-verb agreement violations. We believe this research pushes graph-based natural language processing forward by connecting data-driven learning with explicit grammatical knowledge, offering diagnostic tools with promising pedagogical potential for language education—though further user studies are needed to fully validate their educational effectiveness.
Zhang et al. (2026) studied this question.