Published Paper Details:

The current study explores the effect of DeepSeek-based blended teaching mode on improving the comprehensive academic English ability of medical students. Through a quasi-experimental design, 36 clinical medicine students were divided into an experimental group (DeepSeek-enabled teaching) and a control group (traditional blended teaching) for an 18-week intervention. The experimental group integrated DeepSeek functions in the whole process of "before class-during class-after class", including personalized reading recommendations, intelligent correction of academic writing, and interactive training of terminology. Through standardized tests, learning log analysis and qualitative interviews, it was found that the experimental group was significantly better than the control group in academic writing (p<0.01) and literature reading (p<0.05), especially in the accuracy of terminology and the standardization of paper structure; but the improvement in oral ability was limited, which may be related to the insufficient adaptability of AI voice interaction to medical scenarios. The current study shows that DeepSeek can effectively empower blended teaching, solve the "reading and writing pain points" of medical EAP in a targeted manner, and provide practical references for intelligent medical English teaching. In the future, it is necessary to further optimize the discipline-specific corpus and multimodal interaction functions.