Knowledge-infused learning (KIL) is a class of AI techniques that enhance machine learning models by integrating structured external knowledge, such as facts, rules, and relationships from various sources. The method aims to improve a model's ability to understand complex concepts, generalize from limited data, and provide interpretable decisions. By combining the pattern recognition capabilities of traditional data-driven models and large language models (LLMs) with the reasoning power of knowledge-based systems, KiL seeks to create more robust, capable, and understandable AI systems, with applications including natural language processing, robotics, and healthcare applications.