Hybrid real-time synchronisation algorithm for generative English learning

Artificial intelligence is driving a paradigm shift toward active generation in English learning.However, generative English learning faces challenges such as high latency in hybrid data synchronisation and low accuracy in grammatical proofreading.To address this, this paper proposes a hybrid real-time synchronisation algorithm-driven framework for generative English learning.Adopting a hierarchical modular design, the framework integrates a log-stream real-time synchronisation engine with a dual-encoder grammatical proofreading engine.The synchronisation layer achieves millisecond-level data synchronisation through log encapsulation and final state extraction.The correction layer constructs a dual-encoder model that dynamically fuses cross-sentence contextual and intrasentential semantic features using a gated attention mechanism.Experimental results demonstrate that the proposed method achieves a minimum synchronisation delay of 0.91 ms and a syntax correction accuracy of 93.8%, significantly outperforming existing approaches.This research provides effective technical support for the intelligent advancement of generative English learning.