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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