An NLP-Driven Interactive Guidance System for English Writing

Abstract

Academic English writing on mobile platforms has been constrained by limited screen-based interaction, insufficient adaptation to fragmented learning contexts, and feedback mechanisms that lack pedagogical depth and individual specificity. To address these challenges, a mobile-native interactive English writing guidance system integrating natural language processing (NLP) was designed. Three core innovations were introduced. First, a touchoptimized interaction paradigm was constructed to enable direct interaction between text and feedback. Second, an ontology-based explainable feedback mechanism was proposed to enhance feedback precision and instructional value. Third, a dynamic assessment-driven progressive feedback generation algorithm was developed to adaptively support learners at varying proficiency levels. The system with a multi-agent collaborative architecture integrates a lightweight academic writing ontology knowledge base with mobile-adapted NLP model optimization strategies. Experimental results demonstrated that the proposed system significantly outperformed mainstream baseline approaches in interaction efficiency, feedback quality, and writing performance improvement. Task completion time was reduced by 21.6%–24.1% compared with the control group, feedback precision reached 89.7%, and a 2.1-point improvement was observed in three-dimensional writing quality scores. Correlation analysis revealed a significant negative relationship between interaction efficiency and user dissatisfaction, while a significant positive relationship was identified between feedback explainability and error correction rates. Ablation experiments further confirmed the critical contribution of the three proposed modules to overall system performance. This study establishes a novel paradigm for intelligent writing guidance in mobile contexts and advances the deep integration of digital education and mobile artificial intelligence (AI).