Employment skill reconstruction for higher vocational artificial intelligence application students under AI technology evolution: A machine learning-based empirical study
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Abstract
This study investigates employment skill reconstruction for higher vocational students majoring in artificial intelligence applications under AI-driven technological transformation. A hybrid framework combining questionnaire analysis and machine learning was proposed using 547 valid samples. Four predictive models were developed, among which XGBoost achieved the highest accuracy (93.2%). Results indicate that AI tool usage, prompt engineering, and project experience are the most critical factors affecting employability. An AI-driven skill reconstruction framework is proposed to support vocational curriculum reform.
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