Too often, valuable STEM education research knowledge and products fail to reach classrooms. Improving the translation and diffusion of education research knowledge requires improving the understanding of how to map the movement of people, ideas, and products along a continuum between basic research and practice. Until recently, such mapping of a field was time-consuming and technically difficult. This study will develop and evaluate an AI-assisted approach for mapping complex relationships in educational research in ways that are both meaningful and efficient across a large number of studies. The study will characterize the movement of people and knowledge across a sample of approximately 2,200 K-12 STEM research projects supported by NSF through the ECR and DRK-12 funding programs between 2013-2027. Ultimately, this study's findings will contribute to improved strategies for organizing education research to enhance connectivity across study types, topics, and researchers. The blend of broad AI-assisted approaches with more qualitative network analyses will offer a methodological framework that can be applied to other fields of education research, enabling broader insights into how scientific knowledge grows, evolves, and informs practice, especially as it moves from fundamental research towards more applied research and development and eventually studies of interventions at scale. The study will characterize the movement of people and knowledge across a sample of appro