*NERV* is an intelligent, game-based web tutoring platform powered by the Deepseek model. It is designed to make personalized learning interactive, efficient, and engaging through gamified experiences
NERV is an interactive, AI-driven tutoring platform built on the DeepSeek model, designed to personalize and gamify learning. It features:
Learn Mode: Adaptive topic explanations with levels (Basic to Expert) using text, visuals, and simulations.
Test Mode: Gamified quizzes with EXP rewards and hints to track performance and progress.
Doubt Zone: Instant AI support for student queries with history, analytics, and peer-shared doubts.
NERV includes streaks, badges, and progress graphs to drive engagement and provide visual learning feedback, making education both effective and fun.
During the hackathon, our team developed the core functionality of NERV, an AI-powered STEM tutoring platform built on the DeepSeek model. Key milestones achieved: Built a responsive React interface for Learn, Test, and Doubt modules with smooth animations and intuitive navigation. Integrated the DeepSeek R1 model via API to provide real-time explanations, step-by-step problem-solving, and concept walkthroughs tailored to different learning levels. Implemented Learn Mode with adaptive topic selection across Basic, Intermediate, Advanced, and Expert levels. Developed a functional Test Mode that delivers dynamically generated practice questions with instant feedback. Created a working prototype of the Doubt Zone where users can submit custom questions and receive AI-generated explanations. Deployed the frontend on Vercel and the backend on Streamlit Cloud for public access and demonstration.