Socio-Technical Integration of AI Decision Support in Autism Care: Implications for Caregiver Workflows, Trust, and Workforce Sustainability

Abstract

The integration of artificial intelligence into autism spectrum disorder care promises improved behavioral monitoring, early escalation detection, and enhanced decision support for caregivers. However, the success of such systems depends not only on technical accuracy but also on their alignment with human workflows, trust dynamics, and workforce sustainability. This study examines the socio-technical dimensions of AI deployment in autism care, focusing on how AI decision support systems interact with caregiver roles, responsibilities, and organizational contexts. We propose a human-centered socio-technical framework that integrates AI capabilities with caregiver workflows, trust calibration, and governance mechanisms. Using simulated care environments informed by empirical studies, the framework is evaluated in terms of caregiver workload, decision confidence, trust, and system acceptance. Results suggest that socio-technical alignment significantly improves caregiver engagement and long-term system sustainability. This research highlights the necessity of designing AI systems that support—not disrupt—the human foundations of autism care.