Rightal Learn
AI-powered study app that walks students through their textbooks, lecture notes, and academic materials like a smart tutor would. Features interactive PDF reading, AI-powered explanations, flashcards, quizzes, and comprehensive study analytics.
Study with AI - Your personal AI tutor for PDF learning
Technology Stack
Frontend
Backend
Database & Storage
Additional Technologies
App Screenshots
Welcome Screen
App introduction and onboarding
PDF Reader
Interactive PDF reading interface
AI Chat
Ask questions about your content
Study Dashboard
Overview of learning progress
Flashcards
Smart flashcard generation
Quiz Mode
Generated quizzes from content
Library
Detailed study analytics
Course screen
Organize study materials
Quiz session screen
User profile and achievements
Quiz result screen
Set and track learning goals
quiz correction screen
quiz correction screen
Flashcard screen
Flashcard screen
Architecture & Design
Architecture Pattern
MVVM (Model-View-ViewModel) with BLoC state management and feature-first approach. Clean architecture principles ensure separation of concerns and maintainable code.
Code Quality
Comprehensive error handling, logging system, hydrated state management, and background processing for optimal user experience.
System Architecture
Architecture Pattern
MVVM (Model-View-ViewModel) with BLoC state management and feature-first approach
Key Features
AI-Powered PDF Reading
Interactive PDF reader with AI explanations, highlighting, and intelligent content analysis
Smart Flashcards & Quizzes
Generate flashcards and quizzes from your study materials with spaced repetition learning
Study Analytics
Track reading time, progress, and learning patterns with comprehensive analytics dashboard
Course Organization
Organize materials by courses, create study goals, and manage your learning journey effectively
Challenges & Solutions
Challenge: Complex PDF Processing & AI Integration
Complex PDF processing and AI integration while maintaining smooth user experience posed significant technical challenges, especially with large documents and real-time AI responses.
Solution:
Implemented background processing with Workmanager, optimized PDF rendering with pdfrx, and integrated Google Generative AI with efficient caching mechanisms for seamless performance.
Challenge: Complex State Management & Data Persistence
Managing complex state across multiple features while ensuring data persistence across app sessions and handling offline scenarios effectively.
Solution:
Adopted BLoC pattern with Hydrated Bloc for state persistence, implemented comprehensive error handling, and used feature-first architecture for maintainability and scalability.
Project Timeline
Research & Planning
Market research, user needs analysis, and technical architecture planning
Core Development
PDF reader implementation, basic UI/UX, and user authentication system
AI Integration
Google Generative AI integration, chat functionality, and content analysis features
Advanced Features
Flashcards, quizzes, study analytics, and goal tracking implementation
Testing & Optimization
Performance optimization, bug fixes, and user experience improvements
Launch & Iteration
Play Store launch, user feedback incorporation, and continuous feature development
Feature Architecture
Clean architecture pattern used for each feature module with clear separation of concerns
lib/features/[feature_name]/
├── di/
│ └── [feature_name]_module.dart
├── data/
│ └── [feature_name]_repository.dart
├── domain/
│ └── i_[feature_name]_repository.dart
├── presentation/
│ ├── bloc/ # Main feature state management
│ │ ├── [feature_name]_bloc.dart
│ │ ├── [feature_name]_event.dart
│ │ └── [feature_name]_state.dart
│ ├── cubit/ # Additional state management (sync, etc.)
│ │ ├── [feature_name]_sync_cubit.dart
│ │ └── [feature_name]_sync_state.dart
│ ├── views/
│ │ ├── [feature_name]_list_view.dart
│ │ ├── [feature_name]_detail_view.dart
│ │ ├── add_[feature_name]_view.dart
│ │ └── [feature_name]_sync_panel.dart
│ └── widgets/
│ ├── [feature_name]_card.dart
│ └── [feature_name]_form.dart
├── services/
│ ├── local_[feature_name]_service.dart
│ ├── [feature_name]_sync_service.dart
│ └── ai_[feature_name]_service.dart # If AI features
└── utils/
└── [feature_name]_utils.dart
Key Learnings & Future Improvements
Key Learnings
- • Background PDF processing significantly improves UX for large documents
- • BLoC pattern with feature-first architecture provides excellent scalability
- • AI integration requires careful balance between functionality and performance
- • User analytics and study tracking are crucial for educational app engagement
Future Improvements
- • Voice-to-text note-taking and audio summaries for accessibility
- • Collaborative study groups and peer-to-peer learning features
- • Advanced AI tutoring with personalized learning paths
- • Offline AI capabilities and enhanced PDF annotation tools
Interested in This Project?
Want to learn more about the technical implementation of AI-powered educational apps or discuss similar projects? I'd love to hear from you!