I'm going back to school for AI
I've joined the September 2026 cohort of the B.Sc. in Artificial Intelligence at Miva Open University. Here's why, and how I plan to do it in public.
For four years my work has been getting developers and founders across Africa from curious to shipped. I've done that through workshops in Enugu, Benue and Ebonyi, hackathons in Lagos, and now as Celo's Regional Ambassador for Africa. In the last year I've also been building agent systems myself: agents that pay, agents that review code, agents that run on a server while I sleep.
Building them taught me where my edges are. I can wire up tools, prompts and payments. What I want is the layer underneath: the maths, the models and the evaluation. That's what lets you say why an agent misbehaved, not just that it did. So I applied for a degree, and I got in.
The programme
The B.Sc. Artificial Intelligence sits in Miva Open University's School of Computing. It runs over eight semesters and is taught in blended mode, so I can keep working while I study. The courses I'm most looking forward to are the ones that close gaps I already feel:
- The maths: linear algebra, probability and statistics. These are the foundations I skipped by learning on the job.
- Machine learning, deep learning and reinforcement learning, including multi-agent systems.
- MLOps, cloud deployment, and AI security and adversarial ML: how AI systems survive contact with real users.
- Agentic AI and blockchain technologies, which is my day job, now with a syllabus.
- Two industrial placements, where I'll do the work and not just study it.
Why a degree, and why now
Three reasons.
Depth. Tutorials teach you to call a model. A degree makes you derive the gradient. I want both.
A direction. I want to become an AI engineer and then a forward deployed engineer: someone who takes AI systems into an organisation, works next to the people who'll use them, and owns the outcome end to end. That's my DevRel work today, with a lot more engineering. The degree is how I build the engineering half properly.
Africa. Most AI is built far from the people and languages I work with. If I want to help Africa shape AI and not just adopt it, I need to understand it from the bottom up, and then teach it.
How I'll do it: study in public
Every course becomes something I ship. Everything I ship becomes a lesson. Every lesson goes back to the community.
Concretely:
- This log. The Student-Builder Log on this blog covers what I learned, what I built with it, and what confused me.
- Shipped work. Every couple of weeks I'll publish a repo, notebook or tool that comes out of a course: from-scratch implementations, evals, small agents.
- The AI Study Group. It's my free, open workspace for learning AI engineering together. The best of what I learn becomes lessons there, with quizzes and certificates, so you can learn alongside me.
Exam weeks will be quiet. Grades come first.
Thank you
The first person I told at work was Lena Hierzi, my lead at Celo. She has an academic background in ethical AI and has run excellent study groups of her own, and a lot of how I think about learning together comes from watching her do it. She was delighted. Her advice was to share the news, and to keep putting the work out where people can see it. This is me doing both.
Come along
If you're studying AI in Africa too, formally or not, I'd love to hear from you. Join the AI Study Group, follow along on X, or subscribe to this blog's RSS feed. Log 01 will be about week one, with something shipped.