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Advanced Retrieval-Augmented Generation/Large Language Models for Personalized Learning

Build web-based applications that integrate LLMs

Course Information

Title: Advanced Retrieval-Augmented Generation/Large Language Models for Personalized Learning
Instructor: Sujee Maniyam, Node 51
Time: 11 a.m. – 5 p.m. ET, Feb. 10-13, 2025
Duration: 6 hours
Cost: $500 for NET+ AWS and GCP subscribers; $750 for Internet2 members; $950 for non-members
Delivery: Virtual via Zoom
Registration: Enroll Today

Course Overview

This advanced course is designed for educators, instructional designers, and technologists interested in harnessing Retrieval-Augmented Generation (RAG) to create personalized learning experiences.

Participants will explore how to develop intelligent, AI-driven educational applications that leverage RAG to adapt to individual learner needs. This course combines theory, hands-on exercises, and case studies to enable participants to design and implement advanced RAG models for customized learning paths in higher education.

Target Audience

Instructors in higher education who want to incorporate personalized learning models into their teaching using advanced AI tools.
Instructional designers interested in creating adaptive learning paths and resources for diverse student needs.
Learning technologists and AI specialists in education who seek to integrate RAG with educational technologies like LMS platforms.

Course Objectives

By the end of this course, participants will be able to:

Understand the foundations of RAG for education: Gain a strong foundation in RAG and how it can be applied in personalized learning for various educational settings.
Develop custom RAG pipelines: Learn to design, build, and implement RAG pipelines that provide personalized content retrieval and adaptive learning experiences based on learner data.
Leverage Large Language Models (LLMs) in education: Fine-tune and integrate large language models to enhance RAG capabilities, ensuring educational relevance and content accuracy.
Create real-time personalized assessments: Develop tools and applications for personalized, real-time assessments that adapt to individual student progress and provide feedback.
Address ethical and practical concerns: Understand and apply ethical principles to ensure data privacy, mitigate bias, and uphold transparency in AI-driven educational applications.
Deploy and scale RAG systems institutionally: Plan and implement scalable RAG systems for broader use across educational institutions while maintaining the ability to personalize at scale.
Design a personalized learning tool prototype: Complete a capstone project and create a prototype of a personalized learning tool or application using RAG, demonstrating a practical application of course knowledge.

Course Outcomes

By the end of the course, participants will:

Gain a comprehensive understanding of RAG and its role in personalized learning.
Develop skills in building, customizing, and deploying RAG pipelines.
Understand the ethical considerations of using RAG in education.
Have a prototype application that demonstrates personalized learning using RAG.

This course equips educators, instructional designers, and technology leaders with the advanced skills needed to harness RAG for innovative, personalized educational experiences.

About the Instructor

Sujee Maniyam is a hands-on AI practitioner, speaker, and educator passionate about making complex AI accessible to everyone. As a co-founder of Elephant Scale and a seasoned practitioner, Sujee has extensive experience deploying AI-driven solutions at scale. He is uniquely qualified to guide learners through building intelligent applications using large language models.

Course Details

- Objective: Understand the role of RAG in creating personalized learning experiences and how it differs from traditional AI-driven personalization.
- Topics Covered:
  - Overview of RAG and its application in education
  - Personalized learning fundamentals
  - Examples of successful RAG-based personalization in education
- Activities:
  - Group discussions on current personalized learning models
  - Case study review of a RAG application in a classroom setting
- Objective: Dive deep into the core components of RAG models, including knowledge retrieval systems, natural language generation, and interaction with educational content.
- Topics Covered:
  - Structure and workflow of RAG models
  - Key technologies: knowledge bases, embeddings, and retrieval systems
  - Incorporating domain-specific knowledge for accurate content retrieval
- Activities:
  - Hands-on lab: Setting up a basic RAG pipeline
  - Exercise: Developing prompts for personalized learning contexts
- Objective: Learn to design RAG pipelines tailored for adaptive learning environments that respond dynamically to student needs.
- Topics Covered:
  - Pipeline customization for real-time learner adaptation
  - Integrating learner data with RAG for better personalization
  - Best practices for maintaining relevance and accuracy in content delivery
- Activities:
  - Lab session: Building and testing a RAG pipeline with adaptive features
  - Workshop: Mapping personalized learning paths using RAG
- Objective: Understand how RAG can be integrated into assessments to provide real-time, personalized feedback and guidance.
- Topics Covered:
  - Personalized assessment frameworks using RAG
  - Integrating knowledge retrieval for real-time feedback
  - Techniques for adaptive testing and personalized quizzes
- Activities:
  - Hands-on lab: Creating a real-time assessment tool using RAG
  - Exercise: Designing personalized quiz modules for different learning styles
- Objective: Explore ethical concerns around data privacy, bias, and transparency in RAG applications for education.
- Topics Covered:
  - Ensuring data privacy and maintaining ethical standards
  - Recognizing and mitigating bias in personalized learning content
  - Best practices for transparency and accountability in AI-driven applications
- Activities:
  - Group discussion: Ethical scenarios in AI-driven personalized learning
  - Workshop: Developing a code of conduct for responsible RAG usage in education
- Objective: Learn to deploy, monitor, and scale RAG solutions for broader institutional use while maintaining personalization at scale.
- Topics Covered:
  - Deploying RAG pipelines on cloud platforms for scalability
  - Monitoring performance and adapting RAG models based on student engagement
  - Institutional challenges and solutions for implementing RAG at scale
- Activities:
  - Lab: Deploying a RAG application on a cloud platform
  - Workshop: Building a scalable strategy for RAG deployment across multiple courses
- Objective: Synthesize learning by designing a prototype for a personalized learning tool or application using RAG.
- Project Components:
  - Define educational goals and personalize content
  - Design and build a custom RAG pipeline
  - Implement data retrieval and real-time personalization features
  - Consider ethical implications and propose a scalability plan
- Activities:
  - Project work: Team-based design and implementation of a personalized learning application
  - Presentation: Showcase and receive feedback on the prototype

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Advanced Retrieval-Augmented Generation/Large Language Models for Personalized Learning

Course Information

Course Overview

Course Details

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