AI-Driven Environmental Scanning for Education Policy and Practice

Thomas M. Menino Convention & Exhibition Center 

This project develops an AI enabled environmental scanning system to help the National Comprehensive Center (NCC) monitor emerging education issues. Funded by the U.S. Department of Education, the system automates collection of public information from news outlets and state, territorial, and tribal education agency websites. Using LLM based topic extraction aligned to a domain–theme–topic hierarchy, the system organizes unstructured text into actionable categories for technical assistance (TA) decision makers. A human in the loop process ensures quality, with reviewers refining prompts and schema definitions to stabilize outputs. The system processes information from about 60 states (including territories) and is expected to handle roughly 1,000 items per month. Early results show recurring patterns across states-for example, reports related to issues such as teacher shortages-illustrating how the system surfaces trends that support TA leads in identifying needs and coordinating assistance. By reducing manual scanning and providing structured, near real time insights, this approach strengthens the NCC's ability to detect priorities and improve cross center coordination.

Human-in-the-loop

Landscape scan

Trend detection

Automation 

Social Statistics Section