Introduction The rapid integration of generative artificial intelligence (AI) in early childhood education presents both opportunities and challenges for preschool teachers, yet empirical investigations explicating adoption mechanisms and well-being implications remain scarce. This study developed and tested an integrated framework synthesizing the Technology Acceptance Model (TAM) and Job Demands-Resources (JD-R) theory to examine how cognitive evaluations, technostress dimensions, and contextual factors jointly determine AI adoption intentions and occupational well-being among preschool teachers. Methods Survey data from 300 Chinese preschool teachers, recruited via multistage stratified random sampling, were analyzed through covariance-based structural equation modeling. Results Results confirmed TAM’s core pathways, with perceived usefulness ( β = 0.365, p 0.001) and perceived ease of use (β = 0.250, p = 0.001) significantly predicting adoption intentions. The study empirically validated technostress’s dual-edged effects: challenge technostress facilitated adoption ( β = 0.182, p = 0.003) while hindrance technostress inhibited it ( β = −0.267, p 0.001). AI adoption intention positively predicted occupational well-being ( β = 0.198, p = 0.008). Organizational support buffered hindrance technostress’s negative effects on adoption intention, while workload intensified hindrance technostress’s detrimental impact on well-being. Discussion These findings advance understanding of technology adoption in early childhood education by demonstrating that technostress operates through dual pathways—facilitating and impeding adoption—moderated by organizational and personal resources. Practical implications include targeted support strategies that buffer hindrance stressors while cultivating challenge-oriented appraisals among preschool teachers.
Jia Hanze (Wed,) studied this question.