A Dual-Loop Intelligent Feedback System for English Writing: Design, Application, and Optimization in Web-Based Instruction

This study introduces a dual-loop intelligent feedback system designed for web-based English writing instruction. The system's state-aware mechanism dynamically integrates automated scoring with teacher intervention. A semester-long quasi-experiment involving 100 undergraduates demonstrated its effectiveness: average feedback latency was reduced by half, and student engagement in proactive revisions increased substantially. Multimodal data analysis showed that the intervention not only improved surface-level writing features but also prompted a structural shift in writing competency. Students achieved balanced progress in higher-order dimensions such as content and organization with teacher-initiated feedback acting as a catalyst for competency leaps, particularly among lower-proficiency learners. These findings indicate that intelligently orchestrated, data-informed feedback pathways can effectively reconcile instructional scalability with deep, personalized writing development in digital learning environments.