Darwin’s theory of evolution describes how organisms adapt and improve through iterative processes of variation and selection. Inspired by this principle, self-improving artificial intelligence (AI) systems aim to enhance their own learning capabilities over time. This paper examines how artificial systems can learn to learn more effectively by developing a simple mathematical framework to model improvement dynamics, implementing a practical meta-learning system, and demonstrating simulated performance gains of up to 40% over standard approaches on few-shot classification tasks. Experimental results show that these systems can adaptively refine their learning strategies, achieving stronger performance with less data. Finally, I discuss practical limitations, ethical considerations and the ways self-improving AI mirrors human learning. The complete implementation is provided to ensure the results are fully reproducible.