Abstract

Self‑reflective artificial intelligence (AI) systems equipped with meta‑cognitive learning capabilities represent a frontier in intelligent agent design, enabling autonomous monitoring, regulation, and adaptation of internal cognitive processes. This paper examines conceptual foundations, cognitive architectures, and practical implementations of AI systems that embody reflective reasoning and meta‑cognitive control—defined here as the capacity of systems to “think about their own thinking” to optimize performance, adaptability, and ethical decision‑making. Such systems integrate object‑level task execution with meta‑level reflection mechanisms, including explicit self‑monitoring, performance modelling, and dynamic strategy revision. We analyze historical and contemporary contributions to meta‑cognition in AI, review architectural paradigms such as cognitive architectures, self‑improving frameworks, and reflective agents, and outline methodologies for evaluating self‑reflection metrics. The study highlights empirical advantages such as improved adaptability, explainability, and autonomous error correction, alongside challenges related to computational costs, ethical alignment, and scalability. We present experimental frameworks for measuring meta‑cognitive performance, discuss results from simulations and case studies, and propose directions for future work. This research underscores the significance of meta‑cognitive AI for next‑generation autonomous systems that are robust, transparent, and socially aligned.