On-Premises Multi-Course RAG Tutoring for Business Education: Hardware-Software Trade-offs in a Campus AI Tutor
arXiv cs.AIen
arXiv:2610.02510v1 Announce Type: new Abstract: Campus AI tutors based on retrieval-augmented generation (RAG) must ground answers in assigned course materials while keeping textbooks and student dialogue on institutional infrastructure. We present CourseChat, an on-premises, multi-course RAG tutor for undergraduate business education, deployed behind a campus web gateway and intended for use embedded in Moodle. Six isolated course offerings, each keyed by its own course reference number (CRN), share twin-edge AI hosts running a FastAPI service, a local vector database, and a local large language model (LLM) served by Ollama. We report two generation-model bake-off rounds, a separate fixed-e
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Meta
- Forskning
Related AI news
- World Action Modeling with Progressive Visual PlanningarXiv cs.AI · October 5, 2026
- How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI DebatearXiv cs.AI · October 5, 2026
- Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use AgentsarXiv cs.AI · October 5, 2026
- A Multi Method Importance and Performance Efficiency Analysis of Topological Metrics for Natural Visibility Graph Based Cyber Attack DetectionarXiv cs.AI · October 5, 2026
- MintFlow: Minimal Trajectory Intervention for Constrained Flow MatchingarXiv cs.AI · October 5, 2026
- Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent HarnessesarXiv cs.AI · October 5, 2026