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 Duration 14 hours

Course Outline

Overview of Small Language Models in Educational Technology

  • Fundamentals of Small Language Models
  • Development of AI applications in education
  • Advantages of SLMs for personalized instruction

Designing Learning Environments with SLMs

  • Identifying learner requirements and preferences
  • Constructing adaptive learning pathways
  • Aligning SLMs with instructional design standards

Deploying SLMs in Educational Contexts

  • Configuration of SLMs for classroom and digital learning environments
  • Creation of interactive content utilizing SLMs
  • Best practices for sustaining student engagement

Assessing SLM Impact on Learning Outcomes

  • Evaluation methods for AI-supported learning
  • Data interpretation and learning analytics
  • Ongoing optimization and feedback mechanisms

Challenges and Ethical Implications

  • Mitigating bias within AI systems
  • Safeguarding data privacy and security
  • Facilitating equitable access to AI tools for government

Applied Projects and Case Analyses

  • Development of a mini-project using SLMs
  • Analytical review of SLM implementations
  • Group presentations and peer evaluation

Conclusion and Future Directions

Requirements

  • Familiarity with foundational machine learning concepts
  • Background in educational technology or instructional design
  • Interest in AI-driven educational solutions for government

Target Audience

  • Educational technologists
  • Instructional designers
  • AI developers in education

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