SLAE

The Systems Literature Analysis Engine (SLAE) project aims to create a literature analysis engine, utilizing AI models like OpenAI’s GPT 4o to automatically extract and analyze relationships between specific concepts from academic papers.

We are working with Dr. Kristen Hassmiller Lich and the Gillings School of Global Public Health to build this system, with a target application in grounding and evaluating Group Model Building results in literature.
This task is time-consuming, labor-intensive, and difficult to standardize without automation support. With the process at least partially automated, key decision makers could leverage SLAE in order to inform medical and policy decision making specifically in the realm of systems thinking.

There are two main parts to the project; the application and the pipeline.
The application will be able to create a chart depicting the relationship between the variables in one or multiple papers, using the pipeline to classify the nature of the relationships with as high accuracy as possible.

The project should be helpful to academics in determining which areas are in need of more research, by helping to validate or invalidate hypotheses by community stakeholders and ground them in data.