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The purpose of this study is to develop recommendations for improving the effectiveness of forecasting the employment rate of graduates in developing countries through the analysis of publication activity in the field of artificial intelligence (AI). This leads to the hypothesis of a statistically significant relationship between the prevalence of AI in the academic environment and the employment prospects of students in that academic environment across countries. For the analysis, open international data from Scopus [1], Web of Science [2], the International Labour Organization [3], the World Bank [4], UNESCO [5, 6], and the OECD [7] for the period 2018-2023 were used. The study covered 109 countries, of which 51 developing countries with a middle-income level, including Russia, were selected at the second stage of sampling. The methodology included the classification of countries by regional and economic characteristics, data collection and processing, as well as conducting correlation and regression analyses using SPSS and Python software. As a result, a positive relationship was identified between students’ scientific activity and the employment rate of graduates (rxy = 0.542; p < 0.001), confirming the proposed hypothesis. The developed regression model allows for the prediction of the employment rate depending on publication activity: with an increase of one unit in the number of AI-related publications, the employment rate increases by an average of 0,2%. The results can be used to improve educational programs, shape state policies in the field of youth employment, and develop corporate strategies for workforce preparation.
Keywords:artificial intelligence, AI, GAI, generative neural network, students’ scientific activity, graduates’ employment, developing countries, publication activity, labor market, educational strategies
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