Inspiration
💡 Inspiration
Students often struggle with fragmented learning resources, including textbooks, PDFs, lecture slides, and recorded lectures. Finding relevant information, understanding difficult concepts, and identifying knowledge gaps can make exam preparation time-consuming.
Although AI chatbots can explain concepts, their answers are not always grounded in a student's own study materials. We wanted to create a smarter learning companion that connects AI-powered tutoring with verifiable sources and personalized practice.
🚀 What is LearnLens AI?
LearnLens AI is an AI-powered personalized learning companion developed for Track D of the Multimodal AI Hackathon 2026.
Our vision is to transform educational resources into an interactive learning experience where students can understand concepts, verify explanations against their study materials, test their knowledge, and identify areas that need improvement.
The platform is designed around four key capabilities:
- Smart Study Library: Organize educational materials in one place.
- AI-Powered Tutoring: Get contextual explanations grounded in relevant study content.
- Source Citations: Trace answers to supporting document pages, presentation slides, or lecture transcript timestamps where available.
- Personalized Practice: Generate quizzes, evaluate answers, and recommend additional practice for topics that need improvement.
⚙️ How We Built It
LearnLens AI uses a modern web application architecture with a React-based frontend and a Python backend.
The planned technology stack includes React, TypeScript, Vite, FastAPI, and Google's Gemini AI for language generation. Document-processing tools extract educational content while preserving source information, allowing relevant passages to be retrieved when students ask questions.
The application connects document processing, contextual retrieval, AI-generated explanations, and assessments into a unified learning workflow.
🧠 Challenges We Address
One of our main technical challenges is maintaining reliable connections between AI-generated explanations and the original educational materials.
Another challenge is turning quiz results into useful learning recommendations rather than simply displaying a score.
We aim to address these problems through source-aware content retrieval, transparent citations, and learning recommendations based on assessment performance.
🌟 What's Next?
Our vision is to expand multimedia learning support, improve retrieval quality, enhance learning analytics, and make personalized AI tutoring more accessible to students.
LearnLens AI — Less time searching. More time understanding.
Built for the Multimodal AI Hackathon 2026 | Track D: Personalized Tutoring & Adaptive Learning.
