From Access to Adaptation: Behavioral Pathways in AI-Enabled Public Service Use Across Urban–Rural Contexts in the Global South

Artificial Intelligence (AI) systems are increasingly embedded in development contexts across the Global South, yet limited evidence explains how individuals within marginalized communities behaviorally adapt to these technologies beyond structural access and governance conditions. Building on prior framework-based analysis, this study examines the micro-level processes through which users internalize and operationalize AI-enabled systems in everyday livelihood and learning activities. A mixed-method sequential explanatory design was employed using the same population across urban, peri-urban, and rural settings, integrating structured surveys with ethnographic observations, digital usage tracing, and behavioral mapping. The findings identify three dominant adaptation pathways: instrumental adoption driven by efficiency gains, socially negotiated use shaped by contextual constraints, and reflexive adaptation linked to learning and trust formation. Quantitative analysis indicates that user agency significantly mediates the relationship between access and effective utilization, while qualitative insights reveal that learning styles and socio-cultural conditions influence the depth and sustainability of engagement. The study concludes that inclusive AI outcomes depend not only on infrastructure and governance but also on dynamic human–technology interactions, where cognitive engagement and iterative feedback mechanisms play a central role. These findings extend existing models by introducing a behavioral adaptation dimension critical for designing context-sensitive and sustainable AI interventions.