AI-Driven Operating Systems: Towards Autonomous and Adaptive Computing

Abstract

The rapid evolution of artificial intelligence (AI) has begun reshaping the way operating systems (OS) function. Traditional OS architectures rely on static rules and human-engineered algorithms for scheduling, memory management, security, and user interaction. However, with increasing computational complexity, diverse workloads, and evolving cybersecurity threats, conventional approaches are proving inadequate. This paper introduces the concept of an AI-driven Operating System (AI-OS), an intelligent framework that integrates machine learning, reinforcement learning, and predictive analytics into the core of OS management. The proposed AI-OS adapts dynamically to user behavior, optimizes resource allocation in real time, predicts and prevents failures, and enhances security through anomaly detection. By merging classical OS kernels with AI models, the system moves toward becoming an autonomous, self-healing, and personalized computing environment. This paper explores related research, highlights challenges, and proposes a conceptual architecture for AI-OS that can serve as a foundation for future autonomous computing systems.