Analysis of the Impact of Generative Artificial Intelligence on High-Quality Employment and Corresponding Strategies

Tianyu Zhu

doi:10.54097/11j3vg66

Authors

Tianyu Zhu

DOI:

https://doi.org/10.54097/11j3vg66

Keywords:

Generative Artificial Intelligence; Labor Force; High-Quality Employment.

Abstract

Achieving high-quality employment for the labor force is a key area of national concern. The emergence of generative artificial intelligence (GenAI) presents both opportunities and challenges in promoting high-quality employment. This paper explores the impact of GenAI from both macro and micro perspectives. At the macro level, it examines changes in employment structures and the social security system. At the micro level, it analyzes effects on income, job stability, working conditions, and career development. The paper identifies key challenges posed by GenAI in promoting high-quality employment, including labor skill mismatches, structural adjustments in employment, and institutional barriers in the labor market. To address these issues, the study proposes strategies at four levels: government, universities, enterprises, and university students themselves. The goal is to support the labor force in adapting to changes brought by GenAI and to ultimately achieve high-quality employment.

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References

[1] Han, Q., & Chen, Y. (2025). The value, challenges, and strategies of generative AI in empowering ideological and political education in higher education. Modern Business and Industry, (10), 46–48.

[2] Huang, X., Liu, H., & Yan, X. (2024). Employment effects of generative artificial intelligence and strategic responses. Contemporary Economic Management, (11).

[3] Fan, Y., & Zheng, H. (2024). Digital economy, labor resource allocation, and high-quality employment. World Survey, (06), 41–51.

[4] Shi, Z. (2022). Does the application of information technology promote high-quality employment? Studies in Science of Science, 40(11), 1947–1956 + 1967.

[5] Kong, W., Lian, Y., & Liu, C. (2019). Human capital investment, effective labor supply, and high-quality employment. Economic Issues, (05), 9–18.

[6] Qi, Y., Liu, C., & Ding, S. (2020). Digital economic development, employment structure optimization, and employment quality improvement. Economic Trends, (11), 17–35.

[7] Sui, S., & Xia, Z. (2024). Employment quality differentiation under digital economic development: A skill and regional heterogeneity perspective. Journal of Chongqing University (Social Science Edition), (12).

[8] Lai, D., Su, L., Meng, D., & Li, C. (2011). Measuring and evaluating employment quality across China’s regions. Economic Theory and Business Management, (11), 88–99.

[9] Huang, X., & Dong, Z. (2023). How can AI promote high-quality development and employment? Journal of Central University of Finance and Economics, (11), 3–18.

[10] Wang, C., Zheng, X., Wang, S., & Wang, Z. (2024). Artificial intelligence and high-quality, sufficient employment. New Economy Journal, (12), 85–91.

[11] Li, L., He, Y., & Qian, Y. (2024). Competing or collaborating with machines? Generative AI, employment scale, and labor income share. Financial Market Research, (03), 51–60.

[12] Chen, Z., Cheng, C., & Chen, A. (2022). Research on AI’s promotion of high-quality employment in China. Economic Issues, (09), 41–51.

[13] Qi, L., & Tao, J. (2023). Industrial intelligence and its impact mechanism on migrant workers’ employment quality. Journal of Huazhong Agricultural University (Social Sciences Edition), (01), 34–46.

[14] Han, R. (2024). Analysis of the impact of generative AI on employment structure and policy recommendations. Economic Issues, (07), 70–72.

[15] Acemoglu, D. (2002). Technical change, inequality, and the labor market. Journal of Economic Literature, 40(1), 7–72.

[16] Li, J., & Liu, R. (2024). Generative AI and building an employment-friendly tax system. Contemporary Finance & Economics.

[17] Han, W., Kang, L., & Zhang, R. (2025). The triple effects of generative AI on employment and responses. Journal of CPC Hangzhou Party School, (01), 46–55.

[18] Feng, Y., & Sui, Y. (2018). Innovations and improvements in labor law from a postmodern perspective. Journal of Nantong University (Social Sciences Edition), 34(4), 62–68.

[19] Tian, S., & Liu, Z. (2020). AI-driven unemployment: Social challenges and legal responses. Chongqing Social Sciences, 311(10), 32–43.

[20] Wu, B., Zhou, L., & Yue, C. (2023). ChatGPT/generative AI and job displacement: A supply-demand analysis from the perspective of college student capabilities. Educational Development Research, 43(19), 40–48.