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Course Outline

Introduction to Large Language Models (LLMs)

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

Personalisation in Education

  • The need for personalised learning.
  • Current approaches to personalisation.
  • 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 analysing 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 personalised 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

Requirements

  • A foundational understanding of machine learning concepts.
  • Programming experience in Python is recommended but not mandatory.
  • Familiarity with educational technology is beneficial.

Audience

  • Educators.
  • EdTech developers.
  • Researchers in the field of educational technology.
 14 Hours

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