A Transformer-Based Semantic Control Framework for an Intelligent English Writing Assistance System with Real-Time Feedback
Abstract
English writing plays a key role in education and cross-cultural communication, yet current assistive tools struggle with coherence and adaptive feedback. This study develops an intelligent English-writing assistance system based on a Transformer-driven generative architecture enhanced by a semantic control unit and dynamic scenario attention mechanism. A large-scale corpus of about 2.1 million tokens from student essays and public datasets (ASAP, TOEFL11) was used for training and evaluation. Performance was assessed using BLEU, ROUGE-L, BERTScore, METEOR, and TaskFit indices under consistent hardware settings. In a comparative experiment with 30 participants, average writing time was reduced by 28 %, revision count by 22 %, and recommendation adoption rate rose to 78.3 %. Usability improved significantly (SUS 68.5→84.2) while workload decreased (NASA-TLX –15 points). These results demonstrate that the proposed system enhances logical coherence, semantic accuracy, and user experience through an integrated feedback loop architecture linking generation, evaluation, and error-driven optimization.References
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