Dynamic Path Recommendation Algorithm for English Grammar Difficulties Based on Knowledge Graph and Reinforcement Learning

In line with the increasing emphasis on intelligent learning systems outlined by Frontiers in Computer Science, this study addresses the pivotal challenge of personalized English grammar instruction through advanced computational models. While traditional grammatical correction approaches, such as rule-based and shallow machine learning techniques, offer preliminary support, they often lack contextual awareness and struggle with scalability and adaptability, which are vital for nuanced learner difficulties. In order to bridge the gap, we mention an integrated framework combining knowledge graph structuring with reinforcement learning dynamics to generate dynamic path recommendations tailored to individual grammar difficulties. A compact dual-stream architecture designed to capture syntactic structures and surface forms simultaneously central to our methodology is the grammatical discrepancy encoder (GDE), enabling robust anomaly detection and correction in learner-generated sentences. We introduce adaptive syntax augmentation (ASA), an innovative strategy that enriches training data by simulating realistic syntactic perturbations and optimizes inference through uncertainty-aware refinement, significantly boosting generalization across unseen data. Experimental evaluations demonstrate that the method significantly surpasses traditional baselines in grammar error detection and correction tasks, achieving superior adaptability and linguistic alignment. This research not only advances grammar learning technology but also closely aligns with the journal’s focus on intelligent learning environments, personalized education systems, and adaptive knowledge modeling.