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NeuroLearn AI
Inspiration : We were inspired by the way students today struggle with unorganized learning resources, lack of practice tools, and no clear feedback on their progress. We wanted to build a system that makes learning more interactive, structured, and motivating using AI and cloud technologies like AWS.
What it does : NeuroLearn AI is an AI-powered learning platform that helps students study smarter. It provides an AI tutor for explanations, generates quizzes on any topic, allows document-based Q&A from PDFs/DOCs, and tracks user progress through XP, levels, streaks, and achievements stored in AWS DynamoDB.
How we built it : We built the backend using Flask and integrated Groq LLM for AI responses. AWS DynamoDB is used for storing users, quizzes, and progress data. Authentication is handled with bcrypt, and file processing is done using PyPDF2 and python-docx. The frontend uses HTML, CSS, and JavaScript with a gamified dashboard experience.
Challenges we ran into : We faced issues integrating AWS DynamoDB correctly, especially handling user identity consistency between login, quiz storage, and progress tracking. Another challenge was syncing multiple databases (JSON fallback and AWS), which initially caused conflicts.
Accomplishments that we're proud of : We successfully built a full-stack AI learning system with real-time quiz generation, progress tracking, and document-based Q&A. Integrating AWS with AI features and maintaining user-specific learning history was a major achievement.
What we learned : We learned how to integrate AWS DynamoDB with Flask, manage authentication securely, structure AI prompts effectively, and design gamified learning systems that improve user engagement.
What's next for NeuroLearn AI : Next, we plan to add personalized AI learning paths, leaderboard-based competition, spaced repetition revision system, and deploy a fully polished production version with a modern UI and scalable cloud architecture.
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