Overview
Aether LMS takes inspiration from traditional school-centric frameworks like Skool but elevates the experience by blending AI-powered personalization with modern web technologies. Designed as a platform for creating connected and dynamic learning communities, it features an interactive interface, custom learning paths, and collaboration tools. Backed by robust AI algorithms and scalable architecture, it provides adaptable learning journeys tailored to individual progress and goals.
Technologies Used
Next.jsTailwind CSSPostgreSQLNeonDBBunJSTypeScriptPrismaTensorFlow.jsRedis
Key Features
- AI-Powered Recommendation System: Learners get curated content and resources tailored to their progress.
- Interactive Dashboards: Real-time insights into learning performance and metrics.
- Dynamic Content Delivery: Lessons adapt to individual learning patterns and needs.
- Scalable Database: NeonDB as the backbone for handling large user bases.
- Gamified Progression: Achievements, badges, and progress levels for learner engagement.
- Community Collaboration: Discussion boards, group learning sessions, and resource sharing.
- Real-time Feedback: Track user satisfaction and lesson performance metrics.
- Secure and Scalable: Built with Redis caching, Prisma, and BunJS for speed.
Key Highlights
- 1Inspired by Skool but redefined with AI-driven personalization.
- 2Built with BunJS for ultra-fast operations and better server performance.
- 3Integrates TensorFlow.js for real-time adaptive content recommendation.
- 4Uses NeonDB and Redis for scalable real-time features.
Challenges Overcome
- Creating a scalable recommendation engine synchronized in real-time.
- Optimizing performance for large datasets and simultaneous users.
- Maintaining seamless real-time collaboration while minimizing latency.
- Balancing robust personalization tools with overall platform stability.
Screenshots
Screenshot 1
Aether LMS Landing Page
Development Metrics
time To Complete
2 weeks
commit Count
10
bugs Fixed
6
features Delivered
12
Learning Journey
New Technologies
TensorFlow.js for ML-powered recommendationsRedis caching for real-time data updatesNeonDB for scalable cloud databasesPrisma for schema-first database interactions
Time Investment
Research
10 hours
Implementation
50 hours
Future Development
- AI-Powered Assessment: Build an engine that adapts questions based on user responses.
- Gamification Expansion: Multi-level challenges tied with classroom activities.
- Blockchain Integration: Use blockchain to validate and secure credentials.
- Global Scalability: Implement edge-based caching to support worldwide learning communities.
- Seamless Collaboration Tools: Real-time group chats, video classrooms, and whiteboards.
- Complete Offline Mode: Deliver educational experiences even without internet connectivity.