Artificial intelligence (AI) is increasingly used to support decisions in areas such as healthcare, transportation, and environmental monitoring, yet many systems operate as black boxes, making it difficult for people to understand how decisions are made or to assess their reliability. This lack of transparency can lead to errors, reduce user confidence, and limit the safe and effective use of AI in situations where clear reasoning is essential. This project addresses these challenges by developing new AI methods that explain the decisions in straightforward, intuitive, and meaningful ways, enabling systems to identify and use recognizable patterns in data as building blocks for reasoning. By making AI more transparent and interpretable, the project will help professionals such as doctors and engineers make better-informed decisions, enhance safety in high-stakes applications, and strengthen public trust in AI technologies. In addition, the project includes education and outreach activities that train students in responsible AI development, helping to build a skilled workforce, broaden participation in technology innovation, and contribute to long-term economic growth and societal well-being. This project develops a novel framework for interpretable artificial intelligence (AI) that learns semantically meaningful data patterns and organizes them into transparent, compositional reasoning processes. The objective is to improve model interpretability by explicitly revealing h