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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