NeuroLink AI

Inspiration

NeuroLink AI was inspired by the challenge that students have different learning abilities, strengths, weaknesses, and learning speeds. Many existing learning platforms provide the same experience to every student, making it difficult for learners to understand their individual progress and know what they should focus on next.

We wanted to create a smarter and more personalized learning experience. This inspired us to build the concept of a Digital Learning Twin—an intelligent representation of a student's learning journey that can help understand their progress and provide personalized guidance.

What it does

NeuroLink AI is an AI-powered personalized learning platform that helps students understand and improve their learning journey.

The platform allows students to create accounts, securely log in, access a personalized dashboard, view their subject mastery, track their learning profiles, and receive personalized recommendations.

The system also introduces a Digital Learning Twin, which provides an interactive representation of the student's learning progress. By combining student data, AI-powered insights, and personalized recommendations, NeuroLink AI aims to help learners identify areas that need improvement and make better decisions about what to learn next.

How we built it

We built NeuroLink AI as a full-stack web application.

The frontend was developed using React, Vite, TypeScript, Tailwind CSS, React Router, Framer Motion, and React Three Fiber. React Three Fiber was used to create the interactive Digital Learning Twin and make the learning experience more engaging.

The backend was developed using Node.js, Express, and TypeScript. We implemented user authentication using JWT and bcrypt, allowing students to securely register and log in.

For database management, we used Prisma ORM with SQLite. The database stores student accounts and their learning profiles, including subjects, mastery levels, and personalized recommendations.

The frontend communicates with the backend through REST APIs using Axios. We also used GitHub for version control and worked on deploying the backend through Render.

Challenges we ran into

One of the biggest challenges was configuring the application for production deployment. We encountered several issues involving Prisma 7, Prisma Client generation, SQLite database configuration, TypeScript compilation, and deployment environments.

We also faced runtime errors caused by missing or incorrectly exported Express route handlers. These issues required us to carefully trace the application from the routes to the controllers and ensure that every endpoint was correctly connected.

Another challenge was making sure that the frontend and backend communicated correctly in both the local development environment and the production environment.

These challenges taught us that building a complete application requires more than developing features. Proper configuration, testing, debugging, and deployment are equally important.

Accomplishments that we're proud of

We are proud to have built a working full-stack personalized learning platform with a modern and interactive user experience.

Some of our key accomplishments include:

Building a complete student registration and login system. Implementing secure authentication using JWT and bcrypt. Creating a personalized student dashboard. Developing student learning profiles with subject mastery information. Implementing personalized learning recommendations. Building an interactive Digital Learning Twin using 3D technologies. Connecting the React frontend to a TypeScript and Express backend. Creating a relational database structure using Prisma. Building protected API routes for student data. Successfully generating the Prisma Client and preparing the backend for production deployment.

Most importantly, we are proud that NeuroLink AI is focused on solving a real educational problem rather than simply creating another technology demonstration.

What we learned

Through this project, we learned how to build and connect different parts of a modern full-stack application.

We gained practical experience with React, TypeScript, Express, Prisma, SQLite, authentication, REST APIs, 3D web development, Git, GitHub, and cloud deployment.

We also learned how important it is to properly structure a project and test each component before moving to production. Debugging deployment issues helped us understand the differences between local development and production environments.

Most importantly, we learned that technology can be used to create more personalized educational experiences by using data and AI to help students understand their learning progress.

What's next for NeuroLink AI

Our next goal is to make NeuroLink AI more intelligent, adaptive, and useful for students and educators.

Future improvements include:

More advanced AI-powered learning recommendations. Real-time analysis of student learning behavior. Personalized learning paths for individual students. Improved prediction of learning difficulties. AI-powered tutoring and explanations. More interactive Digital Learning Twin capabilities. Integration with schools and existing learning management systems. Teacher and administrator dashboards. Support for more subjects and learning resources. Improved analytics and progress tracking.

Our long-term vision is to create a platform where every learner has an intelligent Digital Learning Twin that continuously learns from their progress and helps them identify challenges, discover their strengths, and determine the next best step in their educational journey.

Built With

• axios
• bcrypt
• css
• express.js
• fiber
• framer
• github
• jwt
• motion
• node.js
• prisma
• react
• sqlite
• tailwind
• three
• three.js
• typescript
• vite