ADAPTIVE ARTIFICIAL INTELLIGENCE IN INDUSTRIAL QUALITY INSPECTION: A CONCEPTUAL FRAMEWORK

Traditional artificial intelligence (AI)-based inspection systems in manufacturing are typically deployed as static classifiers, trained on historical datasets and rarely updated to reflect evolving production conditions. However, contemporary manufacturing environments are characterized by concept drift, rare defect emergence, process variability, and increasing regulatory scrutiny, rendering static inspection architectures inadequate. This study proposes a socio-technical framework of Adaptive Artificial Intelligence (AAI) for industrial quality inspection. We conceptualize AAI as an enterprise-level dynamic capability that integrates adaptive learning mechanisms, uncertainty-aware decision engines, dynamic inspection policies, human-in-the-loop governance, and enterprise system alignment into a unified architecture. Grounded in adaptive systems theory, dynamic capabilities theory, socio-technical systems research, and cyber-physical systems literature, the framework formalizes five interrelated constructs and articulates their structural relationships through moderated and mediated theoretical propositions. Specifically, adaptive learning moderates the relationship between production volatility and inspection robustness, while dynamic inspection policy mediates the impact of uncertainty-aware decision signals on cost-quality efficiency outcomes. Governance and enterprise alignment act as cross-cutting enablers influencing organizational trust and learning. The paper further redefines inspection performance through resilience-oriented metrics, including robustness under drift, adaptive capacity, inspection elasticity, cost-quality efficiency, and trust-based compliance. Cross-industry illustrations from automotive, electronics, and pharmaceutical manufacturing demonstrate the applicability of the framework. By repositioning inspection as a dynamic, enterprise-embedded capability rather than a static algorithmic function, this study contributes to operations and AI research and advances the theoretical foundations of human-centric Industry 5.0 quality systems.