National AI development and adult lifelong-learning participation: Evidence for knowledge-transfer policy in European countries
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DOIhttp://dx.doi.org/10.21511/kpm.10(2).2026.09
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Article InfoVolume 10 2026, Issue #2, pp. 143-165
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Type of the article: Research Article
Artificial intelligence has become a driver of knowledge transformation, skills renewal, and institutional change, making lifelong learning increasingly important for adapting to AI-driven labor markets and societies. This study aims to examine whether national AI development indicators are associated with realized participation in education and training across different adult age groups in European countries, and to discuss what these associations may imply for lifelong learning and knowledge transfer policies. The analysis is based on a panel of 18 European countries for 2017–2024 and applies two-way fixed-effects models with country and year effects, contemporaneous, one-year, and two-year lag specifications, and Driscoll–Kraay robustness checks. The results show that the total AI Vibrancy Score is not a statistically significant predictor of participation in education and training: the contemporaneous coefficients are 0.4822 for adults aged 18-74, 0.1054 for those aged 45-54, and 0.5006 for those aged 50-74. Descriptive statistics indicate that average lifelong-learning participation declines with age, from 20.09% among adults aged 18-74 to 14.82% among those aged 45-54, and 9.34% among those aged 50-74. The lagged structural models show that AI-related R&D is negatively associated with subsequent participation, with one-year lag coefficients of −1.2310, −0.9392, and −0.8911 for the three age groups, respectively. In contrast, AI-related Policy and Government activity has a positive two-year lagged association for adults aged 18-74 and 45-54, with coefficients of 0.6064 and 0.7346. This suggests that policy-related AI development, rather than national AI development alone, may be more relevant for observed adult participation in education and training.
Acknowledgments
This research was funded by an EU grant “Immersive Marketing in Education: Model Testing and Consumers’ Behavior” under project No. 09I03-03-V04-00522/2024/VA and by the Ministry of Education and Science of Ukraine “Modeling and forecasting of socioeconomic consequences of higher education and science reforms in wartime” (No. 0124U000545).
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JEL Classification (Paper profile tab)I23, I25, O33, J24
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References50
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Tables6
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Figures0
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- Table 1. Summary of main findings across age groups
- Table A1. Descriptive statistics of the variables
- Table B1. Fixed-effects estimates for AI development and participation in education and training among the population aged 18-74
- Table B2. Fixed-effects estimates for AI development and participation in education and training among the population aged 45-54
- Table B3. Fixed-effects estimates for AI development and participation in education and training among the population aged 50-74
- Table C1. Driscoll-Kraay robustness estimates for AI development and participation in education and training
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