Teacher Resource Network is a full-stack educational search platform built to help teachers discover relevant classroom resources faster. I helped design and build a resource ingestion pipeline that normalized videos, articles, activities, simulations, and other teaching materials into searchable records. The system used AI-generated metadata, keyword extraction, standards alignment, and semantic embeddings to make resources searchable by instructional meaning instead of simple keyword matching.
Teacher Resource Network is an AI-powered search platform built to help teachers find high-quality classroom resources faster. Instead of relying on basic keyword matching, the system used AI-generated metadata, standards alignment, and semantic search to connect teachers with resources based on instructional meaning, learning goals, and classroom use case.
This project shows my ability to build AI-backed tools around messy, real-world information. I worked on the architecture behind resource ingestion, metadata generation, semantic retrieval, and full-stack implementation, turning a broad education problem into a searchable system teachers could actually use.