Course Content
MODULE 1: Introduction to AI in MEL
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Overview
Definition of AI and key types (ML, NLP, Computer Vision)
Definition of AI and key types (ML, NLP, Computer Vision) (copy)
Why AI matters in MEL: Volume, Velocity, and Veracity of data
AI Examples in MEL (Automated Transcription, Adaptive Learning Tools)
Risks and Benefits
Quiz: AI myths and facts
Reflective Prompt: ‘What’s one area in your MEL work that could benefit from AI?’
Downloads: Intro to AI primer, Glossary of AI terms
End of Module 1
End of Module 1 (copy)
MODULE 2: Practical Training in AI for MEL
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Overview
Case Studies: NLP, Predictive Analytics, Sentiment Mining, Dashboards
Hands-on Exercises: Upload and clean dataset, Tool selection worksheet
Downloads: Annotated datasets, tool guide, notebooks/APIs
Mentor Support: Live session option
End of Module 2
MODULE 3: Expert Insights & Global Case Studies
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Overview
WFP’s food security AI use
UNEP/NASA climate tracking via AI
Challenges in low-tech settings
Interactive map + Peer sharing forum
Downloads: Case study PDF, transcripts
Quiz
End of Module 3
MODULE 4: Ethical AI Guidelines for MEL
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Overview
Responsible AI Principles
Bias and data privacy
Avoiding data colonialism
Ethical Scenarios + Quizzes
Reflection Quiz
Downloads: Ethics checklist, bias audit form
Mentor Support: 1-on-1 ethics consult
End of Module 4
MODULE 5: AI Implementation Strategies in MEL
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Overview
Organizational readiness assessment
Roadmap development, team planning
Tools, Resources, Partnership Setup
Downloads: Templates and ToRs
Activities: Create Phased Roadmap
End of Module 5
MODULE 6: Wrap-Up & Extras
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Overview
Summary of key lessons
Tool Showcase + Forums
Final Assessment: Integration Plan
Certificate of Completion
Downloads: Tool Library, Certificate template
AI for MEL: Tutor-Led
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