Development and Evaluation of Human-Centered Intelligent Systems for Real-World Decision Support in Complex Applied Environments

Abstract

The increasing complexity of modern socio-technical systems has intensified the demand for intelligent decision-support solutions capable of operating effectively in dynamic, uncertain, and high-stakes environments. Human-centered intelligent systems (HCIS) represent a critical evolution in applied artificial intelligence, emphasizing collaboration between computational intelligence and human cognition rather than full automation. This paper presents a comprehensive investigation into the development, evaluation, and deployment of human-centered intelligent systems for real-world decision support across complex applied environments such as healthcare, infrastructure management, industrial operations, and public policy planning. The study examines theoretical foundations, system architectures, interaction design principles, and performance evaluation methodologies that ensure transparency, adaptability, and trustworthiness. Particular emphasis is placed on integrating explainable artificial intelligence, cognitive ergonomics, and contextual awareness to enhance human decision-making rather than replace it. Through analytical synthesis of existing implementations and applied system models, the paper demonstrates how human-centered intelligence improves decision quality, reduces cognitive overload, and increases system resilience. Key challenges related to bias, ethical accountability, scalability, and real-world validation are critically discussed. The findings establish human-centered intelligent systems as a foundational paradigm for sustainable and responsible deployment of intelligent technologies in complex applied environments.