A universitythat comesto you.
Hanover, NH · April 2026 · Josué Godeme
Personalized education is a loaded idea, especially in the United States. I am not trying to win that argument. The place I am building for is different. I am thinking about Benin, West Africa, and the parts of the world where a student is often not choosing between a human teacher and an AI tutor. Many students are choosing between some access and no access at all.
Let me give you a little context.
In the context I care about, the work is more basic than that. It is access. It is teacher training. It is infrastructure. It is giving an existing classroom, a learning center, or a small kiosk a tutor that can sit on a cheap device and work even when the internet does not.
I am not saying every child should learn alone with a machine. I am saying many children are already learning with too little support. If AI can help close that gap, then the question is not whether it sounds fashionable. The question is whether it helps a real student learn.
What I’m building
I am building an AI-native university, but the first version is not a university in the traditional sense. It starts smaller: students around sixth to ninth grade, where weak foundations in math, reading, science, and language start to compound.
The first prototype can start in English because that is the fastest way to test the tutoring experience, the voice interface, and the offline setup. But English is not the destination. Once the core works, it has to move toward local languages. A student should learn in the language she thinks in, not the language the internet happens to support.
The system works like this: a student speaks. The tutor listens. It explains, asks questions, gives practice, and adapts. It runs offline or close to offline on cheap hardware. No perfect Wi-Fi. No permanent English requirement.
What has to be true
I think this takes three things. First, language. Any product can start in English, but it cannot stop there. If the student thinks in Fon, Yoruba, Mina, Dendi, Bariba, or French mixed with a local language, the system has to respect that.
Second, voice. A lot of students will not learn by typing into a laptop. They need to speak, listen, ask again, and be understood. That means voice AI agents that can perform well without depending on perfect internet.
Third, measurement. Not surveillance. Not dashboards for their own sake. Just a clear record of what the student understands, where they are stuck, and what a teacher, parent, or mentor should do next. Without that, it is just a chatbot. With it, it can become a learning system.
Why me
I grew up in Benin. I study at Dartmouth. I am going to Tsinghua next. Education has carried me across countries, languages, and institutions. I ranked first among about 80,000 students on my country’s national exam, and every major room I have entered since then has been opened by a scholarship. That shaped how I see education. Talent is everywhere. Access is not.
I have also already started on one of the hardest parts: voice AI for low-resource languages with many speakers and very little data online. Now the work is operational. How do you get this out of the lab and into the hands of students, teachers, and communities on the ground?
The question is not whether AI can make education sound impressive. The question is whether it can help a real student learn tomorrow.
There are many ways this can fail. I do not know yet what the sustainable business model is. I do not want to pretend that part is solved. But I am willing to take that risk and figure it out in the open. The vision is a new kind of school: sometimes on a student’s own device, sometimes inside a cheap learning kiosk, sometimes inside an existing classroom. The point is the same. Bring the best education we can build to people who were never supposed to have access to it.
- Josué