Course Outline

Introduction to SLMs in Educational Technology

  • Overview of Small Language Models
  • The evolution of AI in education
  • Benefits of SLMs for personalized learning

Designing Learning Experiences with SLMs

  • Understanding learner needs and preferences
  • Creating adaptive learning pathways
  • Integrating SLMs with instructional design principles

Implementing SLMs in Educational Settings

  • Setting up SLMs for classroom and online learning
  • Developing interactive content with SLMs
  • Best practices for maintaining student engagement

Evaluating SLMs in Learning Outcomes

  • Assessment strategies for AI-driven learning
  • Data analysis and learning analytics
  • Continuous improvement and feedback loops

Challenges and Ethical Considerations

  • Addressing biases in AI
  • Ensuring data privacy and security
  • Promoting equitable access to AI resources

Project Work and Case Studies

  • Designing a mini-project using SLMs
  • Case study analysis of SLMs in action
  • Group presentations and peer feedback

Summary and Next Steps

Requirements

  • Basic understanding of machine learning concepts
  • Experience in educational technology or instructional design
  • Interest in AI-driven educational solutions

Audience

  • Educational technologists
  • Instructional designers
  • AI developers in education
 14 Hours

Number of participants


Price per participant

Open Training Courses require 5+ participants.

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