AI-driven Adaptive Human-Computer Interaction Models for Smart Control Systems
DOI:
https://doi.org/10.46610/IJAIMLECT.2026.v02i01.005Keywords:
Adaptive systems, Artificial intelligence, Big data analytics, Context-aware systems, Human-Computer Interaction (HCI), Internet of Things (IoT), Machine learning, Smart control systemsAbstract
The rapid evolution of intelligent systems and digital environments has significantly increased the complexity of Human-Computer Interaction (HCI), particularly in smart control systems where adaptability, real-time responsiveness, and decision support are critical. Traditional static HCI models remain inadequate for addressing dynamic contexts, user variability, and data-intensive environments. This study proposes an advanced AI-driven adaptive HCI model designed to enhance interaction efficiency, usability, and system intelligence. The proposed framework integrates artificial intelligence, machine learning, the Internet of Things, and big data analytics to dynamically adapt system interfaces and control mechanisms based on user behavior, environmental conditions, and system performance. A hybrid methodology combining conceptual modeling, simulation-based experimentation, and statistical analysis is employed to evaluate the effectiveness of the proposed approach. The results demonstrate significant improvements across key performance indicators, including a 64% reduction in response time, a 23% increase in user satisfaction, and notable gains in decision accuracy and system efficiency. In addition, the adaptive system reduces cognitive load and improves user engagement, confirming its effectiveness in enhancing both technical performance and user experience. This study contributes to the advancement of intelligent adaptive systems by providing a scalable and integrative HCI framework capable of supporting real-time, context-aware interaction. The proposed model has practical applicability in smart cities, industrial automation, healthcare systems, and cyber-physical environments, thereby offering a robust foundation for next-generation intelligent control systems.
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