Machine teaching is a concept that enhances the process of machine learning (ML) by incorporating human expertise and context into the training data selection. This practice aims to generate more relevant outputs from ML algorithms. A key aspect of machine teaching is its accessibility to non-experts, enabling professionals like lawyers or nurses to guide intelligent systems without needing a background in computer science. Machine teaching differs from traditional ML by focusing on creating optimal training sets that efficiently guide learning algorithms. This approach allows ML systems to learn and improve autonomously. The ultimate goal is to make machine teaching tools user-friendly for subject matter experts, similar to how word processors and spreadsheets are used today​​.