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Course Outline

Introduction to SLMs in Educational Technology

  • High-level overview of Small Language Models
  • The trajectory of AI integration in education
  • Advantages of SLMs in facilitating personalized learning

Designing Learning Experiences with SLMs

  • Identifying learner needs and preferences
  • Building adaptive learning pathways
  • Aligning SLMs with core instructional design frameworks

Implementing SLMs in Educational Settings

  • Configuring SLMs for both classroom and online environments
  • Crafting interactive content with SLM assistance
  • Strategies for sustaining high levels of student engagement

Evaluating SLMs in Learning Outcomes

  • Defining assessment methods for AI-supported learning
  • Utilizing data analysis and learning analytics
  • Implementing continuous improvement cycles and feedback mechanisms

Challenges and Ethical Considerations

  • Mitigating inherent biases in AI systems
  • Safeguarding data privacy and security standards
  • Ensuring fair and equitable access to AI resources

Project Work and Case Studies

  • Conceiving a mini-project utilizing SLMs
  • Analyzing real-world case studies of SLM deployment
  • Group presentations and peer review sessions

Summary and Next Steps

Requirements

  • Fundamental grasp of machine learning principles
  • Practical experience in educational technology or instructional design
  • Curiosity and interest in AI-powered educational solutions

Target Audience

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

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