Abstract To address the core challenges in food science and engineering experimental teaching,including curriculum content,practical pathways,and personalized guidance,this study proposes an“AI-Driven Triple-Pillar Mode for Experimental Teaching”,aiming to shift the pedagogical paradigm from tool-assisted learning to systemic restructuring.Grounded in the principles of outcome-based education(OBE),the mode establishes three core pillars:dynamic knowledge provision,immersive practical training,and personalized cognitive guidance.Empirical results demonstrate that the mode effectively enhances students’ knowledge integration and innovation capabilities.In project-based learning,over 72% of students exhibited systematic cross-curricular knowledge integration skills.Immersive practical training increased the first-time success rate of key operational tasks from 52% to 78%,while intelligent cognitive guidance significantly raised the proportion of high-order thinking questions from 15% to 40%.This study confirms that artificial intelligence can structurally reshape experimental teaching systems,providing an empirically validated solution to transition from“knowledge verification”to“capability development”.
ZHANG Ling-Zhi, XIE Yun-Fei. AI-Enabled Experiment Teaching Mechanisms for Cultivating Engineering Competence Based on OBE:Empirical Study in Food Science Experimental Teaching[J]. Chinese Journal of Chemical Education, 2026, 47(10): 71-76.
