Aitutor

Description: An application with pleasing visuals and aesthetics that showcases different courses and lessons with quizzes to guide students and improve their learning experience.

Technical Implementation: Tools, frameworks, libraries, APIs, and hardware used. Frontend: React-Native Backend: Java Spring Boot, Docker, Cohere AI API Database: Firebase, Postman, Cloudinary

Inspiration: We saw how traditional education struggles to meet every learner’s unique pace, style, and needs. Causing some learners to struggle. We wanted to build a tool that makes learning personal, fun, and effective using the power of AI.

What it does: AiTutor is an AI-powered adaptive learning platform that personalizes lessons in real time based on a student’s progress, preferences, and learning style (visual, auditory, kinesthetic). It also equips teachers with an analytics dashboard to track and support student development effectively. It also has a chat function that allows instant QandA, making learning interactive and points systems to keep students motivated. Every user has to login to their account and this authentication will be done by firebase, ensuring data privacy. We created an interactive dashboard for teachers to monitor who is having troubles (stuck in remedial mode) so that human interactions can be done as some people may do better with kinesthetic learning style.

How we built it: We used a combination of machine learning for adaptive difficulty, real-time feedback analysis, and a React Native frontend for a smooth, engaging UX. The backend leverages Firebase for auth/data, and integrated Cohere API for content generation and assessments.

Challenges we ran into: Building a truly adaptive system that felt natural and not robotic Ensuring content remained engaging across multiple modalities Designing a dashboard that is powerful for teachers but easy to use Balancing real-time performance with personalization depth

Accomplishments that we're proud of: A functional prototype that adapts lesson flow in real time Seamless multimodal learning integration Live analytics dashboard that gives teachers actionable insights Positive feedback from test users on engagement and clarity

What we learned: How to fine-tune AI to adapt not just content, but how it's presented The importance of user testing in building intuitive UIs Real-time personalization is incredibly powerful—but also very complex Teachers love simplicity just as much as power

What's next for AiTutor: Expand content variety and AI feedback depth Add voice-based interactions and AR elements for kinesthetic learners Pilot testing with schools and educators Build out parent-facing insights for home learning support