Beginner10–136 weeks
AI Explorers
Pattern games, voice assistants, and "teach the computer" activities for ages 10–13.
EduTech
AI & Machine Learning
Teach intelligence, not just instructions
From pattern recognition with primary students to building classifiers with secondary and university cohorts, our AI & ML track makes abstract concepts tangible. Learners train models, explore real datasets, and discuss responsible AI — all with projects they can show parents and employers.
8
Week modules
15+
AI projects
Ages 10+
Age range
No PhD
Required to start
What you'll experience
Inside AI & ML
Hands-on AI and machine learning programs that demystify neural networks, data, and ethical AI for learners at every level.
Concept-first curriculum
We start with how humans learn patterns, then map that to machines — no jargon wall on day one.
Train real models
Image classifiers, sentiment tools, and chatbot prototypes using beginner-friendly tools.
Ethics & bias workshops
Every cohort discusses fairness, privacy, and when not to use AI.
Africa-relevant datasets
Projects use local languages, agriculture, and health contexts learners recognise.
Showcase day
Students present working demos to peers, teachers, and invited industry guests.
Teacher enablement
Facilitator guides and slide decks so schools can run modules independently.
Programs
Choose your cohort
Flexible formats for schools, holiday camps, and weekend academies — each with clear outcomes and facilitator support.
Intermediate14–1810 weeks
ML Builders
Train image and text models, evaluate accuracy, and deploy simple web demos.
All levelsEducators4 weeks
AI for Educators
Practical AI literacy for teachers — lesson planning, grading aids, and classroom policy.
Learning path
How the journey unfolds
1
Discover
What is AI? Where do we already use it? Interactive demos and group discussions.
2
Experiment
Label data, train simple models, and measure results with visual tools.
3
Build
Team projects: crop disease detector, language helper, or attendance predictor.
4
Present
Document methodology, discuss limitations, and pitch to a live audience.
Outcomes
Skills that stick after the program ends
We measure success by what learners can do independently — not by hours sat in a chair.
- Understand core ML concepts without advanced math prerequisites
- Build and demo at least one working AI prototype
- Articulate ethical considerations in plain language
- Continue learning with open-source tools and our resource library
Bring AI literacy to your school
We design cohorts for classrooms, clubs, and holiday intensives.
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