Many scientific breakthroughs happen when a researcher notices that a technique from one field could solve a problem in another — a discovery in materials science, for example, might share a deep structural similarity with a finding in a different field. Yet with hundreds of new papers appearing each month even within narrow specialties, no individual can read broadly enough to spot these cross-cutting connections reliably. This project develops artificial intelligence systems that help researchers uncover such hidden connections across the scientific literature — systems that can recognize structural parallels between ideas in different domains. Critically, creative reasoning by AI carries risks: a connection that looks insightful may turn out to be unfounded. To address this, the systems developed in this project are designed to be transparent, showing researchers the evidence behind every suggestion so they can verify claims and distinguish well-supported insights from speculation. This project serves the national interest by promoting the progress of science. By helping researchers identify promising new directions more quickly this work has the potential to accelerate discoveries that directly improve quality of life. All tools and datasets will be released publicly, and in partnership with major research conferences, these tools will be integrated into the peer-review process to support real-world scientific evaluation. The project also includes substantial educational efforts: paid summer research internships for Baltimore high school students, a new AI literacy course for pre-college students, integration of research tools into undergraduate teaching, and inclusive workshops and mentoring to broaden participation in computing and artificial intelligence. This project develops interpretable, large language model (LLM)-based frameworks that support creative scientific discovery while ensuring transparency and grounding in evidence. The research is organized