Collaborative Research: An Incubation Project to Build Generative Artificial Intelligence Approaches for Research, Teaching, and Practice

Generative artificial intelligence (GenAI) tools are often viewed as transformative for social science research, education, and real-world decision-making. Despite their capabilities, GenAI tools might produce results that are not accurate or impartial. This project aims to address this challenge by equipping researchers, educators, students, and others with the tools and knowledge needed to apply GenAI in their research activities responsibly and rigorously. The project seeks to enable research communities to capture the benefits of using GenAI. The project develops and tests new human-in-the-loop approaches, relying on human expertise, judgment, and critical thinking, to integrate GenAI tools into document analysis and synthesis (DAS). DAS is a widely used but labor-intensive method in the social sciences. The goal of the project is to ensure accuracy and impartiality while supporting usability and broader adoption. The project team will create a generalizable methodological framework for GenAI-enhanced DAS workflows, outlining use cases and guiding key decisions, including how to structure context, prompt models, and validate results. The framework is tested through classroom-based research in a set of undergraduate and graduate courses. Students will engage in structured problem-solving exercises and participate in focus groups. The project team will analyze interactions between the students and GenAI tools, along with student reflections, to examine decision-making, performance, and best practices. The project clarifies and demonstrates how GenAI can be responsibly and rigorously embedded within established social science research methods. 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.