EduAI

Inspiration

We were inspired by a simple question: "If you had the opportunity, would you go back to school and learn something new?" We asked 20 adults this question, and the response was overwhelmingly positive. This made us realize that the desire to learn does not disappear with age—but many people don't know what to learn, where to start, or how to connect learning to real opportunities.

We wanted to build something that could make personalized learning accessible to anyone, regardless of age, location, or background.

What it does

EduAI transforms a learner's interests, hobbies, strengths, and aspirations into a personalized journey from discovery to learning to opportunity.

The platform:

Creates an AI-powered learner profile based on the learner's interests and goals. Generates a personalized STEM learning pathway. Provides an AI tutor that explains lessons using examples connected to the learner's interests. Recommends exactly three relevant opportunities, projects, competitions, or career pathways. Provides a dashboard where learners can see their profile, course, skills, progress, and opportunities.

Our vision is: "From who you are, to what you can learn, to where your skills can take you."

How we built it

We built EduAI as a lightweight, hackathon-focused MVP using Next.js, TypeScript, and Tailwind CSS.

The application is structured around four logical AI agents:

Learner Profile Agent — understands who the learner is. Curriculum Agent — creates a personalized learning pathway. Tutor Agent — delivers personalized lessons and challenges. Opportunity Agent — connects the learner's interests and skills to relevant next steps.

We used Codex to accelerate development, provide coding guidance, and help us move from an empty repository to a working end-to-end prototype. The AI generation architecture is designed to use GPT-5.6 where available, with fallback demo data to ensure the experience remains usable during a hackathon demonstration.

Challenges we ran into

The biggest challenge was time. We discovered the OpenAI hackathon with only about seven hours remaining before submissions closed.

We had to rapidly move from an idea to a working product while balancing product design, AI architecture, coding, testing, and deployment considerations.

We focused on the most important part of the experience: creating a compelling end-to-end learner journey rather than trying to build a complex education platform with unnecessary features.

Accomplishments that we're proud of

We are proud that, within a very short timeframe, we transformed an empty repository into a working EduAI MVP that demonstrates the complete learner journey.

We built a platform that can take something as simple as a learner's hobbies, interests, favourite subjects, and aspirations and turn them into:

A learner profile → A personalized STEM course → An AI tutoring experience → Real-world opportunities.

We are especially proud of creating an experience that demonstrates how AI can make education feel personal, relevant, and connected to the individual learner.

What we learned

We learned that the biggest opportunity for AI in education may not simply be answering questions or generating content. It is the ability to understand the individual learner and continuously connect their identity, interests, learning journey, skills, and future opportunities.

We also learned how powerful AI-assisted development can be. Using Codex, we were able to accelerate the transition from concept to working prototype and focus more of our limited time on the product vision and user experience.

Most importantly, we learned that there are millions of potential learners who may be willing to learn—but they need a starting point, a personalized path, and a reason to believe that learning can lead somewhere.

What's next for EduAI

Our next step is to move EduAI from a hackathon prototype toward a platform that can serve underserved and unreached learners.

We want to expand the platform to support:

Learners in the African diaspora seeking new skills and career pathways. Out-of-school children who need alternative routes to education. Rehabilitated adults and people seeking a second chance to rebuild their careers. Learners in underserved communities with limited access to traditional education. People who want to reskill or transition into new careers, regardless of age.

Our long-term goal is to build an AI-powered learning ecosystem that doesn't just tell people what to learn, but helps them discover who they can become—and the opportunities that can take them there.

Built With

• codex
• githuub
• gpt
• next.js
• openai
• tailwind
• typescript
• vercel