Abstract: Food biochemistry is a core foundational course for food-related majors, yet its teaching is often challenged by abstract theoretical knowledge, microscopic biochemical processes, insufficient classroom interaction, and weak knowledge transfer. To address these problems, this study constructed a digitally supported progressive teaching model characterized by a “phenomenon-mechanism-application” based on the Chaoxing Learning Platform. Centered on core knowledge modules of food biochemistry, the model integrated resource push, AI-assisted Q&A, multimedia visualization, and virtual simulation into a connected teaching support system across pre-class, in-class, and post-class stages. A comparative teaching study was conducted with the 2024 experimental class and the 2023 control class in the food science and engineering program at Northwest Minzu University. The results showed that the model enhanced pre-class learning, classroom interaction, course assignments, stage tests, and overall course performance, with particularly notable effects on classroom engagement, depth of mechanistic understanding, and knowledge transfer ability. These findings suggested that the digital reform of food biochemistry should be grounded in core disciplinary content and supported by the coordinated use of digital platforms, multimedia technology, AI-assisted learning, and virtual simulation.

Key words: food biochemistry, digital teaching, teaching reform, instructional design, knowledge transfer