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Course Outline

Overview of Small Language Models in Educational Technology

  • Introduction to Small Language Models
  • The historical development of artificial intelligence within the education sector
  • Advantages of utilizing Small Language Models for individualized instruction

Structuring Pedagogical Experiences with Small Language Models

  • Analyzing learner requirements and preferences
  • Developing adaptive educational pathways
  • Aligning Small Language Model integration with established instructional design frameworks

Deploying Small Language Models in Academic Environments

  • Configuring Small Language Models for both physical classrooms and remote learning contexts
  • Producing dynamic educational materials using Small Language Models
  • Established protocols for sustaining student engagement

Assessing the Impact of Small Language Models on Learning Outcomes

  • Evaluation methodologies for artificial intelligence-supported instruction
  • Analytics and data interpretation in educational settings
  • Strategies for continuous enhancement and iterative feedback mechanisms

Mitigation of Challenges and Ethical Standards

  • Mitigating algorithmic bias in artificial intelligence systems
  • Upholding data privacy and security standards
  • Advancing equitable access to artificial intelligence tools for government

Applied Projects and Case Study Analysis

  • Development of a prototype application utilizing Small Language Models
  • Examination of implemented Small Language Model case studies
  • Collaborative presentations and peer review processes

Conclusion and Future Directions

Requirements

  • Familiarity with foundational machine learning principles
  • Professional background in educational technology or instructional design
  • Commitment to advancing AI-powered learning solutions

Target Audience

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

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