Optimizing DeepSeek Prompts for Cross-Cultural English Writing Instruction: A Practical Study of Transfer Competence.

Abstract

To address the limitation that traditional English writing instruction neglects systematic cultivation of language transfer and existing prompt frameworks fail to fit classroom teaching, this study constructs a hierarchical transfer training system based on the DeepSeek model covering four progressive task tiers: sentence, paragraph, discourse, and culture, with all tasks defined in triple form. A five-dimensional task library covering syntax, structure, culture, register, and logic is established, and targeted prompts are automatically generated from students' authentic writing drafts. Integrated with the model's Mixture of Experts (MoE) architecture, a nested prompt chain is developed to split complicated assignments into three sequential phases: semantic reorganization, linguistic revision, and cultural adaptation. Adopting a quasi-experimental design, this research recruits 60 second-year English majors from one university and assigns them to an experimental group and a control group of 30 students each for three weeks of staged intervention across three training rounds. Post-experiment results reveal that the experimental group's cultural transfer score rises from 2.8 to 4.5, compared with a mere 0.3 increment in the control group; its average teacher-assessed score improves from 59.1 to 74.4, compared with the control group's increase from 60.1 to 64.9. Most correlation coefficients between AI scoring and teacher evaluation exceed 0.8, and over 90% participants approve of the AI rewriting and structural optimization functions. Due to the small sample size in this trial, this optimization approach is feasible for implementation in university English writing classes.