AI anxiety, digital-life stress, and well-being: A gender-stratified network analysis

Artificial intelligence (AI) is increasingly embedded in everyday learning, work, and information environments, yet little is known about how distinct forms of AI anxiety are organized with digital-life stress and well-being or whether these associations differ by gender. Using data from 3175 participants (1017 men, 2158 women; ages 13–58), we estimated gender-stratified six-node EBICglasso networks comprising AI fairness anxiety, AI work/learning anxiety, AI opacity anxiety, information overload, time poverty, and well-being. Women reported higher mean levels of all three AI anxiety dimensions, information overload, and time poverty, and slightly lower well-being. However, the male network showed greater global strength, driven primarily by stronger cross-domain connections rather than within-domain connectivity. Information overload had the highest bridge strength overall, while AI work/learning anxiety was the strongest bridge among the AI anxiety dimensions in both gender-specific networks. Stability, robustness, and split-half consistency analyses supported the main pattern. These findings show that gender differences in AI-related psychological experiences involve not only average levels but also the organization of associations among AI anxiety, digital-life stress, and well-being. A multidimensional network perspective may therefore clarify psychological adaptation to AI-related concerns in digital life.