The rapid diffusion of generative artificial intelligence (AI) is reshaping the labor market, yet it remains unclear whether students’ appraisal of AI-driven employment threat translates into employment anxiety in similar ways across cultural groups. This study administered an online questionnaire to 1,595 domestic students (1,095) and international students (500) enrolled at Central South University. Perceived AI replacement threat, AI optimism, and employment anxiety were assessed with 13 five-point Likert items. Information channels, coping behaviors, desired institutional support, and perceived coping relief were assessed as supplementary measures. Confirmatory factor analyses supported an acceptable three-factor structure in both groups. The distinction between threat and anxiety remained qualified due to their substantial association. Metric invariance was established, and partial scalar invariance was obtained after the intercept of Q21 was freed. Perceived AI replacement threat and employment anxiety remained empirically distinguishable, although their latent correlation was stronger among international students than among domestic students. In the structural model, threat was strongly associated with anxiety ( β = 0.764) and weakly associated with optimism ( β = 0.133). After threat was included, optimism showed a small negative conditional association with anxiety ( β = −0.152). The indirect association through optimism was negative but limited in magnitude, and equivalent cross-sectional specifications fit the data equally well. This pattern is best interpreted as an exploratory statistical finding compatible with suppression, rather than confirmed evidence of a directional psychological mechanism. Supplementary coping analyses were descriptive and exploratory. These findings provide qualified empirical support for a three-factor framework of students’ responses to AI-related employment uncertainty, and highlight differences in threat-anxiety coupling across student populations. The results carry implications for targeted student support services addressing AI-related career concerns.
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黄勇明 et al. (2026) studied this question.
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