Abstract
The need for personalized and real-time feedback in English writing instruction is increasing rapidly. Traditional systems, which depend on rule-based engines and shallow machine learning models, struggle to meet this demand. They often fall short in addressing key aspects such as grammar correction, sentence variety, and logical coherence. This study introduces a multidimensional feedback system based on the Transformer architecture. The system combines self-attention mechanisms with a dynamic parameter adjustment module to deliver feedback at multiple levels-from individual words to entire paragraphs. A BERT model is fine-tuned on a large, diverse corpus that includes academic papers, blog posts, and student essays. As a result, the system can provide real-time suggestions that address grammar, vocabulary, sentence structure, and logic. Experimental results show that the system improves the writing quality of non-native learners while maintaining a feedback delay of just 1.8 s. Its modular design allows for the customization of learning paths, and user privacy is protected through differential privacy mechanisms. This approach offers a technically sound and educationally practical solution for developing AI-assisted writing tools across disciplines.