The Optimal Education Threshold for AI-Enhanced Productivity in Professional Settings
Аннотация
The adaptation to artificial intelligence systems during professional education might enable higher cognitive productivity and might furthermore increase organizational performance by ensuring a sufficient threshold level of educational attainment for a specific work context. With the objective of improving understanding of optimal education effects in the digital workplace, we first analyzed descriptive statistics and variance parameters in cross-sectional situation on AI utilization time intervals by using the quadratic regression model. This study focuses on education–productivity interaction at the individual level and demonstrates that the skills, experience, and training levels of employees can be shaped by a U-shaped function on AI exposure in a professional domain. Key outcome measures for the empirical evaluation were the coefficients for the regression model, the explained variance, and the sensitivity of the four indicator variables used: education years, AI usage hours, task complexity, and learning adaptability. Through the multiple correlation analyses of the variables in the framework of AI-assisted productivity, we analyze the magnitude and direction of the effects in the context of education and automation. The results showed that there were three types of education thresholds: from low to moderate/high (optimal point), moderate/high without progression (plateau effect), and education only (diminishing returns). The turning point reached up to 12–14 years of education in AI-related jobs; the predictive power of the primary model variables ranged from approximately 0.42 to 0.68, the secondary interaction variables ranged from 0.29 to 0.51, and the residual explanatory variables ranged from 0.12 to 0.25. These results could provide valuable insights for policy design and organizational training systems.