Volume 8,Issue 9
Generative Artificial Intelligence and Inequalities in University Students’ Employability: Socio-Technical Mechanisms, Structural Risks, and Educational Responses
Generative artificial intelligence is deeply involved in university students’ career exploration, competency training, job‑seeking expression, and career‑related decision‑making. Nevertheless, its popularization does not necessarily bring about equal distribution of employability gains. From a socio‑technical perspective, this paper situates employability inequality within the continuous interaction among technology, individuals, universities, and the labor market, and constructs an analytical framework of “structural resources — high‑quality access — affordance actualization — competency conversion — market recognition.” The study reveals that generative artificial intelligence can lower the costs of career‑related information and training services and thus produce opportunity‑compensating effects. Meanwhile, it may generate cumulative advantages and disadvantages through stratified models and services, disparities in artificial intelligence literacy, uneven institutional support, career‑signal inflation, and biases in algorithmic recruitment. Compared with the conventional digital divide, the generative‑AI‑driven employability divide is characterized by disparities in epistemic reliability, divergences in human‑AI task orchestration, disjuncture between generated outputs and authentic competencies, as well as bilateral algorithmic interactions between job‑seekers and recruiters. Accordingly, an inclusive human‑AI collaboration mechanism should be built at three levels: students’ critical artificial‑intelligence employability literacy, universities’ competency‑conversion systems, and transparent and accountable technology and recruitment governance.
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