Advancing Research Translation with AI Stakeholder Insights (2025-2026)

This team explored how artificial intelligence and stakeholder engagement can help move university research from the laboratory into practical, real-world applications. While Duke researchers produce groundbreaking discoveries, many technologies never reach the people who could benefit from them because researchers often lack opportunities to engage with end-users during the research process. The team investigated whether combining AI-assisted use-case generation with customer discovery interviews could help identify promising pathways for translating emerging technologies into meaningful societal impact. 
 

Working with five Duke research teams spanning medical imaging, digital pathology, cellular therapy, biomanufacturing and agriculture, students developed plain-language technology summaries, used AI tools to generate more than 1,250 potential applications and conducted approximately 100 stakeholder interviews with clinicians, researchers, industry leaders, farmers and other end-users. These interviews helped validate the most promising use cases and identified practical factors, such as workflow integration, cost, performance requirements, and adoption barriers, that could influence successful implementation. In one case, stakeholder feedback prompted a research team to revise its commercialization strategy. 
 

The project produced five technology-specific reports that provided faculty and Ph.D. researchers with evidence-based recommendations for refining their research to better address stakeholder needs. The team also created a customer discovery dataset and a searchable database of AI-generated use cases that will serve as resources for future Duke researchers exploring pathways to societal impact. Together, these deliverables provide a repeatable framework for integrating stakeholder perspectives into research translation and strengthening the connection between scientific innovation and real-world impact. 
 

Timing 

Fall 2025 – Spring 2026 

Team Outputs 

Team Profile 

Technology-specific final reports (one per research technology) 

Structured customer-discovery dataset 

See related team, Advancing Research Translation with AI Stakeholder Insights (2026-2027)