Adaptive AI Frameworks for Personalized Human-Computer Interaction in Smart Environments

Abstract

The rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT) technologies
has facilitated the emergence of smart environments capable of adaptive human-computer
interaction (HCI). This paper explores adaptive AI frameworks that enhance personalized
interactions in smart environments. The research delves into various AI techniques such as
machine learning, deep learning, and reinforcement learning, which enable systems to
dynamically respond to user preferences and behaviors. A detailed literature review highlights
existing challenges and gaps, including data privacy concerns, real-time adaptability, and user
satisfaction. The methodology section presents a conceptual framework integrating AI models,
sensor networks, and cloud computing. Results indicate improved interaction efficiency, reduced
cognitive load, and enhanced user satisfaction. The study concludes that adaptive AI frameworks
are essential for making HCI more intuitive and efficient, paving the way for smarter living and
working environments.