LearnFlowAI is an intelligent, AI-powered learning platform designed to create personalized educational experiences. It helps users master new skills by providing tailored learning roadmaps, curated resources, adaptive quizzes, and on-demand tutoring.
The application is built with a modern web stack and leverages the power of Google's Gemini model through Genkit to deliver a dynamic and responsive user experience.
- 🗺️ Personalized Roadmaps: Receive step-by-step, AI-generated learning paths tailored to your specific career goals and existing skills.
- 📚 Skill Catalog & Learning Resources: Explore a vast library of skills. For each skill, the AI provides key concepts, high-quality articles, and relevant YouTube videos to guide your learning.
- 🧠 Adaptive Quizzes: Test your knowledge with smart quizzes generated on any topic. The quizzes provide instant feedback and explanations to reinforce learning.
- 💡 AI Project Recommendations: Apply your knowledge with practical project ideas that match your interests and skill level, helping you build a strong portfolio.
- 🤖 AI Chatbot Tutor: Get instant, personalized help and resources from an AI tutor, available 24/7 to answer questions and clarify concepts.
- 📊 Progress Tracking Dashboard: Visualize your learning journey with a detailed dashboard that tracks your completed courses, average quiz scores, and skill mastery over time.
- Framework: Next.js (App Router)
- Language: TypeScript
- UI Library: React
- Styling: Tailwind CSS
- UI Components: ShadCN UI
- AI Integration: Genkit (with Google's Gemini Model)
- Backend & Database: Firebase (Authentication, Firestore with offline persistence)
- Hosting: Firebase App Hosting
This project includes a special python_algorithms/ directory to meet academic requirements for demonstrating classic machine learning algorithms.
Important Note: These Python scripts are standalone demonstrations and are not functionally integrated into the live web application. The main application's AI features are powered exclusively by the Genkit framework. The scripts serve as clear, isolated examples of the underlying principles.
The demonstrated algorithms include:
decision_tree.py: A script that uses a Decision Tree to predict a learner's weak skill area based on sample performance data.bayesian_network.py: A script that models the probability of a learner's skill mastery using a Bayesian Network to handle uncertainty.reinforcement_learning.py: A script demonstrating a Q-Learning agent that learns the optimal difficulty level for questions to maximize a learner's engagement.
To get started with this project in Firebase Studio, simply describe the changes you'd like to make, and the AI assistant will help you build and modify the application.