DeepSeek-Driven Three-stage Teaching Reform Design in Plant Tannin Chemistry and Applications

Objective As a core course in light industry-related majors, Tannin Chemistry and Applications faces challenges such as content redundancy, uneven student knowledge levels, and difficulties in transferring knowledge to emerging fields. Driven by the dual imperatives of educational digitalization and the New Engineering 2.0 strategy, a transformative approach to the course is urgently needed.

Analysis This paper proposes a three-stage teaching reform centered on the large language model DeepSeek, which involves diagnosing knowledge gaps through intelligent assessment, precisely reinforcing foundational knowledge via AI-generated problem sets, and facilitating interdisciplinary discussions led by instructors to enhance knowledge transfer and innovative thinking.

Conclusion This paradigm is expected to deliver precision personalized instruction, eliminate content redundancy, spark students’ enthusiasm for interdisciplinary innovation, and provide a replicable model for the digital upgrade of traditional disciplines.