Artificial Intelligence, Techno-Stress, and the Digital Divide in Early Childhood Classrooms: Policy Insights on Teacher Well-Being in Pakistan

Background: Artificial intelligence (AI) is increasingly integrated into early childhood education (ECE), transforming teaching practices while introducing new psychological demands for educators. AI, though intended to enhance efficiency and personalized learning, may inadvertently increase workload, blur work–life boundaries, and elevate techno-stress, especially in low-resource educational contexts. Objectives: This study, grounded in the Job Demands–Resources (JD-R) model, investigates the longitudinal impact of AI-integration intensity on teacher techno-stress in Pakistan, examining the moderating roles of the Digital Tier Divide (Structural) and techno-resilience (individual psychological adaptive capacity). Methodology: A 3-wave longitudinal design was employed over six months, sampling N=382 ECE teachers from Lahore and Islamabad. Data were analyzed using the PROCESS Macro in SPSS. Results: Findings reveal that AI integration significantly predicts longitudinal techno-stress (p < .001). The Digital Tier Divide significantly exacerbates this relationship, with teachers in low-resource schools experiencing higher stress increments. Conversely, Techno-Resilience acts as a significant buffer, flattening the stress trajectory even in high-demand environments. Conclusion/Policy Implications: The findings extend JD-R theory to AI-driven classrooms and emphasize the need for well-being-centered policies, including equitable infrastructure and resilience training, to ensure sustainable AI adoption in early childhood education.