SLMs for Educational Tech: Tailoring AI for Learning and Development

• Basic understanding of machine learning concepts
• Experience in educational technology or instructional design
• Interest in AI-driven educational solutions

Audience

• Educational technologists
• Instructional designers
• AI developers in education

Small Language Models (SLMs) are transforming the educational technology landscape by providing personalized learning experiences.

This instructor-led, live training (online or onsite) is aimed at intermediate-level educational technologists, instructional designers, and AI developers in education who wish to integrate Small Language Models (SLMs) into educational platforms to enhance teaching and learning processes.

By the end of this training, participants will be able to:

• Understand the role of SLMs in educational technology.
• Design AI-driven learning experiences using SLMs.
• Implement SLMs in various educational settings.
• Evaluate the effectiveness of SLMs in learning outcomes.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to SLMs in Educational Technology

• Overview of Small Language Models
• The evolution of AI in education
• Benefits of SLMs for personalized learning

Designing Learning Experiences with SLMs

• Understanding learner needs and preferences
• Creating adaptive learning pathways
• Integrating SLMs with instructional design principles

Implementing SLMs in Educational Settings

• Setting up SLMs for classroom and online learning
• Developing interactive content with SLMs
• Best practices for maintaining student engagement

Evaluating SLMs in Learning Outcomes

• Assessment strategies for AI-driven learning
• Data analysis and learning analytics
• Continuous improvement and feedback loops

Challenges and Ethical Considerations

• Addressing biases in AI
• Ensuring data privacy and security
• Promoting equitable access to AI resources

Project Work and Case Studies

• Designing a mini-project using SLMs
• Case study analysis of SLMs in action
• Group presentations and peer feedback

Summary and Next Steps