<b>An Empirical Study on the Impact of AIGC on Labor Literacy: Construction and Validation of a SEM</b>IGC, Labor Literacy, Structural Equation Modeling, Perceived Usefulness

This empirical study investigates the use of AI-Generated Content (AIGC) in labor literacy courses by analyzing relevant student data. It further examines students' labor literacy levels after completing these courses. By employing the Partial Least Squares Structural Equation Modeling (PLS-SEM) tool, this study establishes a structural equation model between students' perceived usefulness of AIGC and their labor literacy. The findings confirm a positive correlation between AIGC’s practice, perceived usefulness, critical cognition and students’ labor literacy, providing empirical support for the application of AIGC in enhancing labor literacy.本实证研究通过分析相关学生数据，探究人工智能生成内容（AI-Generated Content，AIGC）在劳动素养课程中的应用情况，并进一步考察学生修完此类课程后的劳动素养水平。本研究采用偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modeling，PLS-SEM）工具，构建了学生对人工智能生成内容的感知有用性与其劳动素养之间的结构方程模型。研究结果证实，人工智能生成内容的应用、感知有用性与批判性认知均与学生的劳动素养呈显著正相关，为人工智能生成内容在提升劳动素养领域的应用提供了实证支撑。