Clustering analysis of workers’ AI literacy development mechanisms and group differences

The revolutionary penetration of artificial intelligence (AI) technology is reshaping global value chains, transforming its role from a traditional tool into a core driver of industrial competitiveness. Enhancing workers’ AI literacy has become a critical factor in advancing organizational digital-intelligent transformation and innovation. Grounded in the psychological engagement theory and the active learning model, this paper adopted a micro-level perspective to analyze data from 340 employees using the K-means clustering method. Four distinct AI literacy cultivation groups are identified, which includes foundational activation group, the technological catch-up group, the benchmark leadership group, and the balanced and stable group. Based on each group’s characteristics and development needs, this research proposes differentiated, tiered training strategies: enhancing cognitive awareness for the foundational activation group, focusing on practical skills for the technological catch-up group, fostering innovation for the benchmark leadership group, and strengthening resource integration for the balanced and stable group. The paper offers theoretical insights and practical guidance for building targeted and effective AI talent development systems, aiming to enhance human capital efficiency and support organizations in achieving sustainable competitive advantages in the intelligent era.