Chenlin Gu

Scientific effect evaluation is a key link in testing the effectiveness of teaching reform, diagnosing existing problems and optimizing reform paths, while effective promotion strategies can extend the value of reform achievements from individual majors to broader fields. Focusing on the AI-enabled restructuring project of teaching content system for industry-education integration courses in universities, this study aims to construct a multi-dimensional effect evaluation system and propose systematic promotion strategies. Based on the four-level model of reaction, learning, behavior and results, the evaluation system integrates quantitative and qualitative indicators, and emphasizes the measurement of core dimensions such as content-industry matching degree, teaching efficiency improvement and students’ competency attainment. The promotion strategies are carried out from four levels: campus demonstration and guidance, inter-university collaborative sharing, industry-education alliance linkage, and standard solidification and output, exploring how to transform pilot experience into a replicable and scalable general model. This research provides a reference framework for similar universities to conduct effect inspection and achievement transformation of AI-enabled teaching reform.

AI Empowerment; Industry-Education Integration; Curriculum Reform; Effect Evaluation; Promotion Strategy

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