AI in Education: Implementing LLMs and RAG Systems

Educators and instructional designers are looking for practical ways to leverage artificial intelligence to personalize learning, yet general AI tools often lack specific curriculum knowledge. By building custom Retrieval-Augmented Generation (RAG) systems, you can create reliable, domain-specific AI assistants that support students effectively. In this course, you will transition from understanding basic generative AI concepts to designing functional AI tutor prototypes tailored to your subject area. You will learn how to structure prompts, organize educational content for vector retrieval, and establish clear guidelines for fair AI use in the classroom. What you'll learn: 1. Understand the foundational mechanics of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) in educational settings. 2. Master prompt engineering techniques specifically optimized for tutoring, scaffolding, and student guidance. 3. Design a custom AI tutor assistant using modern vector database concepts to retrieve accurate textbook and syllabus data. 4. Evaluate the quality, accuracy, and safety of AI responses using structured feedback loops. 5. Formulate institutional policies for academic integrity and the ethical, fair use of AI. 6. Plan the integration of AI tools with modern Learning Management Systems (LMS) to streamline student access. The course begins with core definitions of generative AI and RAG before guiding you step-by-step through prompt design, data structuring, system evaluation, and policy creation. You will read clear explanations and review practical text-based examples to build your implementation plan. This course is designed for educators, instructional designers, and educational technology specialists. No prior programming experience is required; we start with the absolute basics. Start building intelligent, reliable AI assistants for your students today.