LLMs for Personalized Education

• An understanding of basic machine learning concepts
• Experience with programming in Python is recommended but not required
• Familiarity with educational technology is beneficial

Audience

• Educators
• EdTech developers
• Researchers in the field of educational technology

Large Language Models (LLMs) are used for processing and generating human-like text.

This instructor-led, live training (online or onsite) is aimed at educators, EdTech professionals, and researchers with varying levels of experience and expertise who wish to leverage LLMs for creating personalized educational experiences.

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

• Understand the architecture and capabilities of LLMs.
• Identify opportunities for personalization in educational content using LLMs.
• Design adaptive learning platforms that utilize LLMs for content personalization.
• Implement LLM-driven strategies for enhancing student engagement and learning outcomes.
• Evaluate the effectiveness of LLMs in educational settings and make data-driven decisions for improvements.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Large Language Models (LLMs)

• Overview of LLMs
• Evolution of LLMs in educational technology
• Understanding the architecture of LLMs

Personalization in Education

• The need for personalized learning
• Current approaches to personalization
• Challenges and opportunities

LLMs and Content Adaptation

• LLMs in content creation and curation
• Adapting content to learning styles and levels
• Multitasking with LLMs for content adaptation

LLMs in Practice

• Case studies: Successful LLM applications in education
• Interactive session: LLMs at work

Designing Adaptive Learning Platforms

• Principles of adaptive learning platform design
• Incorporating LLMs into platform architecture
• User experience and interface considerations

Implementation and Testing

• Developing a prototype adaptive learning platform
• Testing and iteration
• Collecting and analyzing user feedback

Evaluating LLM Effectiveness

• Metrics for measuring LLM impact on learning
• Research methods for educational technology
• Case study analysis and discussion

Ethical Considerations and Future Directions

• Ethical implications of LLMs in education
• Ensuring inclusivity and fairness
• Predictions for the future of LLMs in personalized learning

Project and Assessment

• Designing and presenting a proposal for an LLM-based adaptive learning platform
• Peer reviews and group discussions
• Final assessment and feedback

Summary and Next Steps