HBCU Frontiers in Research, Opportunity, Networks, Transformational Intelligence, Evidence, Reflection, and Strategy

Artificial intelligence (AI) is changing how colleges and universities educate students, prepare the future workforce, and improve institutional effectiveness. However, the usefulness of AI technologies depends on the quality and range of knowledge used to develop and apply them. Historically Black Colleges and Universities have long demonstrated success in preparing students for careers in science, technology, engineering, and mathematics while advancing innovative approaches to teaching, learning, and institutional improvement. The proposed Frontiers in Research, Opportunity, Networks, Transformational Intelligence, Evidence, Reflection, and Strategy (FRONTIERS) project seeks to capture and organize the knowledge generated through HBCUs and strengthen future applications of AI. By combining knowledge of institutional experiences, quantitative measures of success, and evidence from educational leaders, the project will create a strong foundation for understanding how successful workforce development occurs in different institutional settings across the HBCU ecosystem. The resulting knowledge will support stronger decision-making, expand opportunities for continuous improvement across higher education, and contribute to the nation's capacity to prepare a competitive science, technology, engineering, and mathematics workforce. The project will design, implement, and validate AI-enabled evidence and learning infrastructure that integrates structured quantitative program measures with large-scale qualitative evidence generated through principal investigator engagement. The research advances a mixed-methods analytic framework in which conversational data, structured institutional evidence, and quantitative indicators are transformed into a unified evidence architecture that supports systematic cross-case analysis. AI tools will be configured to perform qualitative coding support, semantic pattern recognition, thematic classification, evidence synthesis, and integrated analysis across heterogeneous data sources, while trained researchers conduct human-in-the-loop verification to establish analytic validity, consistency, and inter-rater reliability. The project will evaluate the extent to which the configured system reliably captures implementation knowledge, identifies relationships among institutional conditions, leadership practices, and workforce development outcomes, and produces interpretable evidence suitable for institutional decision-making. The resulting methodological framework contributes a novel approach for combining AI with rigorous qualitative and quantitative inquiry to generate scalable implementation intelligence that extends beyond conventional program evaluation and advances research on institutional transformation, STEM workforce development, and evidence-informed AI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.