RUCAI is an AI-based open-source software platform designed for humanities and social sciences educational workflows that require traceability to assigned course materials. RUCAI has a teacher-only workspace in which teachers can curate class exercises grounded in curriculum documents (articles, book chapters, and notes). From here, the teacher can set up workspaces grounded explicitly in curriculum materials, learning outcomes, and course requirements for students to use throughout the course. The system is set up to run on institution-controlled infrastructure using FastAPI, PostgreSQL, and a local LLM model serving via Ollama, and is intended to be deployable on a GPU-enabled virtual machine. RUCAI’s primary contributions are practical: (1) it presents a software architecture for local-first, source-grounded educational AI that treats the course as the primary software object; (2) it documents concrete implementation choices for reliable ingestion and retrieval across heterogeneous course materials, including a three-tier extraction pipeline, word-based chunking, and intent-aware retrieval; and (3) it describes a publish-to-student mechanism that lets teachers freeze course context and prompt constraints into student-facing instances. The paper also reports pilot observations, current limitations, and future development directions.