Generative AI-Driven Pathways for Optimizing English Writing Quality: An Empirical Study in Applied Universities

Generative AI shows remarkable potential in improving English writing quality but encounters ethical challenges such as semantic bias, cultural adaptability (e.g., differences in high- and low-context tones), and risks to academic integrity, necessitating systematic approaches to enhance teaching outcomes. This study proposes a four-dimensional optimization framework: technical, educational, ethical, and cross-cultural, to guide the application of generative AI in English writing at applied universities. Through theoretical review, case studies, and quantitative analysis, it examines how generative AI improves complexity, accuracy, lexical richness, and fluency (CALF). Analysis of 74 short paragraphs from 37 CET-4 level students reveals that generative AI significantly enhances CALF metrics in English writing at applied universities. ChatGPT effectively boosts discourse complexity, DeepSeek improves politeness in expression, and Grammarly focuses on grammar correction. However, short paragraph tasks limit the depth of discourse analysis, highlighting the need for human-AI collaboration to optimize writing quality. The fourdimensional framework integrates teaching practices in Chinese applied universities, enhancing students' writing skills and providing guidance for standardized applications.