Enhancing university education with AI: a Telegram bot leveraging RAG and external APIs for secure knowledge retrieval

This paper presents a novel AI-powered Telegram bot designed to enhance university information services by securely integrating external AI capabilities with institutional private data.The system leverages Retrieval-Augmented Generation (RAG) to transform structured university data (faculty profiles, schedules, lecture notes) into vectorized embeddings, which are dynamically retrieved and combined with responses from a general-purpose AI API (e.g., .This hybrid approach ensures accurate, contextaware answers while preserving data privacy -raw institutional information is never exposed directly to third-party systems.Implemented at Comtrade University, the bot demonstrates significant outperforming standalone AI models for domain-specific questions.Key innovations include a scalable pipeline for embedding private data, seamless Telegram-based access, and cost-efficient prompt engineering via RAG.The solution addresses critical challenges in educational technology: balancing AI augmentation with data security and providing 24/7 conversational access to institutional knowledge.We discuss architectural decisions, privacy safeguards, and empirical results, offering a replicable framework for other universities.