How to Build an Adaptive Keyboard Shortcut Learning System

Simple Summary

An intelligent keyboard shortcut trainer that adapts to users' learning pace and provides personalized practice sessions to improve productivity.

Product Requirements Document (PRD)

Goals:

• Develop an intelligent system to teach keyboard shortcuts efficiently
• Personalize learning experiences based on user progress and preferences
• Increase user productivity by mastering application-specific shortcuts

Target Audience:

• Knowledge workers
• Software developers
• Students
• Anyone looking to improve their computer efficiency

Key Features:

1. Personalized learning paths
2. Adaptive difficulty levels
3. Progress tracking and analytics
4. Multi-application support (e.g., VS Code, Photoshop, Excel)
5. Gamification elements (achievements, leaderboards)
6. Spaced repetition for better retention
7. Real-time feedback on shortcut usage

User Requirements:

• Intuitive onboarding process
• Customizable practice sessions
• Performance insights and improvement suggestions
• Cross-platform availability (web, desktop, mobile)
• Offline mode for practice without internet connection

User Flows

1. New User Registration and Onboarding:

   • Sign up with email or social media
   • Complete initial skill assessment
   • Select applications for shortcut training
   • Receive personalized learning plan

2. Daily Practice Session:

   • Log in to the application
   • View daily goals and challenges
   • Complete adaptive practice exercises
   • Receive immediate feedback and progress update

3. Performance Review and Customization:

   • Access personal dashboard
   • Review progress statistics and achievements
   • Adjust learning preferences and difficulty
   • Explore new shortcut sets for different applications

Technical Specifications

Frontend:

• React for web application
• React Native for mobile apps
• Redux for state management
• Styled-components for styling

Backend:

• Node.js with Express.js
• PostgreSQL for relational data
• Redis for caching and real-time features
• JWT for authentication

AI/ML:

• TensorFlow.js for adaptive learning algorithms
• Python with scikit-learn for data analysis and model training

DevOps:

• Docker for containerization
• GitHub Actions for CI/CD
• AWS for cloud hosting

API Endpoints

• POST /api/auth/register
• POST /api/auth/login
• GET /api/users/:id/progress
• POST /api/practice/start
• PUT /api/practice/complete
• GET /api/shortcuts/:application
• POST /api/feedback
• GET /api/leaderboard

Database Schema

Users:

• id (PK)
• email
• password_hash
• name
• created_at
• last_login

UserProgress:

• id (PK)
• user_id (FK)
• application
• shortcut_id
• proficiency_level
• last_practiced

Shortcuts:

• id (PK)
• application
• key_combination
• description
• difficulty

PracticeSessions:

• id (PK)
• user_id (FK)
• start_time
• end_time
• score
• shortcuts_practiced

File Structure

Implementation Plan

1. Project Setup (1 week)

   • Initialize React project
   • Set up Node.js backend
   • Configure database and ORM
   • Implement basic authentication

2. Core Functionality (3 weeks)

   • Develop shortcut database and API
   • Create practice session logic
   • Implement basic adaptive algorithm

3. User Interface (2 weeks)

   • Design and implement main components
   • Create responsive layouts
   • Develop user dashboard

4. AI/ML Integration (2 weeks)

   • Refine adaptive learning algorithm
   • Implement personalized recommendations
   • Integrate ML models with backend

5. Gamification and Social Features (1 week)

   • Add achievements and rewards system
   • Implement leaderboards
   • Create social sharing functionality

6. Testing and Optimization (2 weeks)

   • Conduct unit and integration tests
   • Perform usability testing
   • Optimize performance and load times

7. Deployment and Launch Preparation (1 week)

   • Set up production environment
   • Configure monitoring and logging
   • Prepare marketing materials and documentation

Deployment Strategy

1. Use AWS Elastic Beanstalk for backend deployment
2. Deploy frontend to AWS S3 with CloudFront for CDN
3. Set up Amazon RDS for PostgreSQL database
4. Use Amazon ElastiCache for Redis
5. Implement AWS Lambda for serverless ML model serving
6. Configure AWS CloudWatch for monitoring and alerts
7. Use AWS Route 53 for DNS management
8. Implement blue-green deployment for zero-downtime updates
9. Set up daily database backups to S3
10. Use AWS WAF for additional security layer

Design Rationale

The adaptive keyboard shortcut trainer is designed with a focus on personalization and engagement. React and Node.js were chosen for their performance and vast ecosystem, enabling rapid development and easy scaling. The use of AI/ML allows for a truly personalized experience, adapting to each user's learning pace. The gamification elements are included to increase user engagement and motivation. A microservices architecture with containerization allows for easier updates and scaling of individual components. The cloud-based deployment strategy ensures high availability and performance while providing the necessary tools for monitoring and quick iterations based on user feedback.