MAYA AI LABSYOUR PRACTICAL AI LEARNING HUBAll resources →LESS GUESSWORK. MORE UNDERSTANDING.
Learn AI.
Put it to work.
Clear guides, hands-on learning paths, and practical toolkits for your classroom, business, or humanitarian work. No coding required.
LEARN WITH A LITTLE STRUCTURE
Choose your learning path.
Follow a sequence, build a skill, and apply what you learn.
Start with the basics
Daily Mastery
Your starting point for understanding AI—from the fundamentals to using it in everyday work.
Build a useful skill
Prompting Power Week
Learn to give clearer instructions, add useful context, and get more relevant responses from AI.
impactMasterclass series
Apply it to your sector
Humanitarian AI
Explore practical applications for nonprofit and humanitarian work, with human judgment at the center.
LEARN SOMETHING YOU CAN USE
A guide for the work in front of you.
Choose a real-world challenge and find a practical way forward.
FOR FOUNDERS
Market research with AI—without a big budget
Explore a focused approach to researching your market with AI.
Read the guide →classroom.Teaching · Learning · Possibility
FOR EDUCATORS
The AI-powered classroom
A practical guide for educators working in low-resource settings.
Read the guide →to decisions.Evidence · Understanding · Action
FOR NGO TEAMS
Make reporting more useful
Explore an AI workflow that connects report writing with decision-making.
Read the guide →RESOURCE SPOTLIGHT
The AI MEAL Professional Toolkit
Discover the toolkit for monitoring, evaluation, accountability, and learning professionals.
ABOUT MAYA AI LABS
AI learning that
starts with people.
Maya AI Labs makes AI more accessible through clear explanations, practical resources, and learning you can apply. Explore at your own pace, follow your curiosity, and keep human judgment at the center.
Our purpose and approach →Understand before you automate
Build a foundation so you can make thoughtful choices about using AI.
Learn through practical work
Connect each new skill to something you want to do better.
Use AI responsibly
Review outputs, protect sensitive information, and consider the people affected.
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