AI’s Employment Promotion Mechanism for Chinese College Students in an Aging Society: A Dual-Perspective Study

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Abstract

This project addresses employment challenges university students face under China's delayed retirement policy and AI-driven economic transformation. Given an aging population, academic inflation, and intensified job competition, this study investigated how AI technologies can generate emerging professions (e.g. algorithm engineers, AI maintenance specialists), enhance skills development (personalized learning systems), and optimize job matching (intelligent platforms). Five recruitment experts were interviewed to identify how the AI-driven economic being transformed. The results found that mitigating the employment pressure on college graduates caused by delayed retirement policies, while also innovatively proving that AI is not a 'job displacer' but rather a 'bridge builder' for youth to break through to new careers in an era of dramatic demographic shifts and technological explosion. It is necessary to continuously promote the deepening of applied technologies and the construction of intergenerational collaboration mechanisms to transform population challenges into innovation dividends. All in all, this project leveraged artificial intelligence to address the employment difficulties of college students in an aging society, precisely targeting the triple challenges of national aging, youth employment, and technological transformation. It combined policy foresight and the promotion of practical value. It provided a demonstrative solution for national universities to tackle employment issues against the background of delayed retirement and promote the transformation from 'talent dividend' to 'innovation dividend.'

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Song, J., Liu, C., & Lau, A. S. M. (2025). AI’s Employment Promotion Mechanism for Chinese College Students in an Aging Society: A Dual-Perspective Study. In Frontiers in Artificial Intelligence and Applications (Vol. 412, pp. 336–341). IOS Press BV. https://doi.org/10.3233/FAIA250733

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