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LearnFlow – AI-Powered Learning Path Recommender

Project Overview
  • Project Title: LearnFlow – AI-Powered Learning Path Recommender
  • Domain: EdTech
  • Technology Stack: Java 17, Spring Data JPA, MySQL, HTML, CSS, React
  • Duration: 3 Weeks
Project Description

LearnFlow is an AI-based platform that recommends personalized learning paths for users based on their current skill level, goals, learning behavior, and performance. It guides students or professionals through a step-by-step journey of courses, tutorials, and assessments tailored specifically for them. The system adapts over time using feedback and learning analytics, ensuring each user receives the most effective path to achieve their educational or career objectives.

Key Objective
  • Personalize Learning Journeys.
  • Optimize Learning Efficiency.
  • Analyze User Behavior & Performance.
  • Support Dynamic Path Adjustment.
  • Integrate Seamlessly With LMS.
  • Encourage Skill Progression.
  • Enhance Learner Retention & Outcomes.
Core Feature
  • Skill Assessment Engine: - Initial quizzes or surveys to evaluate current skill level and set goals.
  • AI Recommender System: - Suggests learning modules, courses, and practice tasks based on profile and performance.
  • Progress Tracking Dashboard: - Visual representation of completed modules, current position, and next recommendations.
  • Dynamic Re-Routing: - Changes the path if the user struggles or excels, ensuring the path stays optimal.
  • Feedback & Rating Mechanism: - Allows users to rate content and provide feedback, feeding back into the recommendation engine.
  • Goal-Based Suggestions: - Suggests content based on selected goals (e.g., "Become a Full Stack Developer" or "Crack Java Interviews").
  • Integration with External Platforms: - Pulls course data from sources like Coursera, Udemy, or in-house LMS APIs.

Tools & Technologies Used

Category Tools / Technologies
Language Java 17
Framework SpringBoot
ORM Spring Data JPA
AI/ML Layer Python (Scikit-Learn, TensorFlow via microservices or REST API)
Recommendation Engine Collaborative & Content-Based Filtering (via Python/ML)
Analytics & Tracking Google Analytics, custom event tracking.
API Documentation Swagger / SpringDoc OpenAPI
Authentication & Security Spring Security + JWT
Logging Log4j
Database MySQL
FrontEnd React.js
Visualization & Reporting JasperReports / Apache POI / Chart.js / Recharts.
Building Tools & Dependencies Maven, Docker, Git, Jenkins
Testing Mockito, Postman (API Test)
Cloud and Deployment AWS EC2, Docker, RDS (MySQL)

Prerequisites

💻 System Requirements
  • Operating System: Windows / macOS / Linux
  • RAM: Minimum 8 GB (Recommended 16 GB for AI/ML services)
  • Java JDK 17 installed and environment variable JAVA_HOME configured
  • Python 3.8+ installed (for AI/ML microservices)
  • Node.js and npm/yarn installed (for frontend)
  • Docker & Docker Compose (for microservices and deployment)
📦 Software Dependencies
  • Java 17 with Spring Boot Framework
  • MySQL Server for database management
  • Python Libraries: Scikit-Learn, TensorFlow, Flask/FastAPI (for recommendation microservices)
  • RabbitMQ or Kafka (optional for asynchronous communication)
  • Maven for backend dependency management
  • Postman for API testing
🧰 Tools & Services
  • IDE: IntelliJ IDEA / Eclipse for Java, PyCharm for Python, VS Code for frontend
  • Git & GitHub/GitLab for version control
  • Jenkins for CI/CD automation
  • Google Analytics account for tracking user interactions
  • AWS EC2 instances for application hosting, AWS RDS for MySQL database
  • Docker for containerization of backend, frontend, and ML services
🌐 Technical Knowledge
  • Strong understanding of Spring Boot, REST API development, and Spring Security (JWT)
  • Experience in Python-based AI/ML development (recommendation systems using Scikit-Learn, TensorFlow)
  • React.js (including hooks, context API, and router)
  • MySQL database design and advanced querying
  • Microservices architecture and Docker container management
  • Google Analytics event tracking setup and analysis
  • Basic CI/CD concepts and Jenkins pipeline creation
  • Experience with API documentation using Swagger or SpringDoc OpenAPI
  • TechnologyJava
  • TypeWeb Application
  • Duration3 weeks
  • ModeOnline/Offline
  • CertificateYes
  • Project ReviewIncluded
  • Doubt SupportLive & Chat Support
  • Career SupportResume & Interview Tips
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