American International Journal of Research and Innovation

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Adaptive Artificial Intelligence Frameworks for Human-Centered Decision Intelligence Systems

Author(s) Konstantia Zarkogianni
Country United States
Abstract Artificial Intelligence (AI) has evolved from a computational automation technology into a strategic decision-support capability that influences complex organizational, social, and operational environments. However, conventional AI-driven decision systems frequently face challenges related to transparency, adaptability, contextual understanding, and alignment with human preferences. Human-centered decision intelligence represents a progressive approach that combines advanced artificial intelligence techniques with human expertise, ethical reasoning, and adaptive learning mechanisms to support reliable and explainable decisions.This study explores adaptive artificial intelligence frameworks designed to enhance decision intelligence systems by integrating machine learning, explainable AI, contextual analytics, and human feedback mechanisms.
The research develops a conceptual framework for understanding how adaptive AI architectures can improve decision quality while maintaining human oversight and responsible technology adoption. A simulation-based analytical approach is applied to evaluate improvements in decision accuracy, adaptability, interpretability, and user confidence across conventional AI systems and human-centered adaptive AI frameworks.
The findings indicate that adaptive AI models demonstrate improved decision-support capabilities due to continuous learning, contextual awareness, and collaborative interaction between humans and intelligent systems. The study contributes to emerging discussions on responsible AI development by proposing a balanced framework where automation enhances, rather than replaces, human judgment.
Keywords Adaptive Artificial Intelligence, Human-Centered AI, Decision Intelligence, Explainable AI, Machine Learning Frameworks, Responsible AI, Intelligent Decision Systems.
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-02
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