Academic Journal of Computing & Information Science, 2026, 9(7); doi: 10.25236/AJCIS.2026.090709.
Chenrui Lin1, Lufei Xu2, Yichen Jiang1
1School of Finance, Central University of Finance and Economics, Beijing, China
2School of International Trade and Economics, Central University of Finance and Economics, Beijing, China
As Generative Artificial Intelligence (Gen-AI) reshapes the labor market, the existing higher education curriculum system exhibits a significant lag, making it difficult to cope with multi-dimensional technological shocks. To quantitatively evaluate the heterogeneous impact of AI on different occupations and optimize curriculum configuration under limited credits, this study proposes a data-model dual-driven multi-dimensional decision analysis framework. First, a quantitative framework based on the Weighted Seniority-Efficiency Model and Analytic Hierarchy Process (WSEM-AHP) is constructed. Combined with the TF-IDF algorithm to mine real recruitment demands, it achieves precise measurement from the micro-level seniority structure to macro-level dimensions. Second, by introducing carbon neutrality responsibility and ethical baseline constraints, a multi-objective curriculum configuration optimization model is established, and Sequential Quadratic Programming (SQP) is employed to solve for the optimal credit allocation. Finally, simulation experiments on three typical occupations demonstrate that the model effectively identifies occupational heterogeneous characteristics: software engineers exhibit "responsible technological expansion," graphic designers tend towards an "ethical defense-dominated" approach, and electricians display "physical skill adherence." This study provides educational planners with a systematic quantitative decision-making tool that balances comprehensive utility maximization with sustainable development.
Gen-AI; multi-dimensional technological shocks; WSEM-AHP; TF-IDF algorithm; Sequential Quadratic Programming
Chenrui Lin, Lufei Xu, Yichen Jiang. Heterogeneous Occupational Assessment and Multi-Objective Curriculum Optimization under the Impact of AIGC: A WSEM-AHP and TF-IDF Approach. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 7: 66-74. https://doi.org/10.25236/AJCIS.2026.090709.
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