🎯 Multimodal Personal Tutor - Product Requirements Document

Executive Summary

Project: Multimodal Personal Tutor (MPT)
Platform: Desktop Web Application
Vision: An AI-powered learning platform optimized for desktop browsers that combines adaptive text, voice, and visual interactions with persistent personalization
Timeline: 24-hour hackathon implementation
Target: College students, self-learners, and educational institutions using desktop/laptop computers

🎯 Objectives

Primary Goals

• Desktop-Optimized Learning: Rich multimodal interface designed for large screens and keyboard/mouse interaction
• Web-Native Experience: Full-featured web application without mobile dependencies
• Multimodal Learning: Seamless integration of text, voice, and visual content in desktop layout
• Personalization: AI that remembers user preferences and adapts teaching style
• Real-time Interaction: Streaming voice responses and instant visual generation
• Scalable Architecture: Enterprise-ready foundation built from day one

Success Metrics

• ✅ Functional: 3 topics covered with interactive lessons and quizzes
• ✅ Performance: Voice plays inline with <500ms latency per sentence
• ✅ Quality: ≥80% judge satisfaction on clarity & adaptiveness
• ✅ Scale: Support 50+ concurrent desktop users
• ✅ Safety: Content filter blocks unsafe outputs 99% of the time
• ✅ Compatibility: Works on Chrome 80+, Firefox 78+, Safari 14+, Edge 80+

🏗️ System Architecture

Technology Stack

Backend (Python/Flask)

• Core Framework: Flask with SocketIO for WebSocket support
• AI Models: Claude Sonnet 4 (reasoning) + Hugging Face (fine-tuned adapters)
• Database: Firebase Firestore (user data) + Redis (sessions/cache)
• Voice: Streaming TTS via WebSocket (Deepgram/custom)
• Vision: Diffusers for educational diagram generation

Frontend (React)

• Framework: React 18 with desktop-optimized components
• State Management: Context API + Custom hooks
• Styling: Custom CSS optimized for desktop layouts
• Audio: Web Audio API for streaming playback
• Auth: Google OAuth 2.0 integration
• Layout: Fixed sidebar navigation with main content area

Infrastructure

• Containerization: Docker + Docker Compose
• Orchestration: Kubernetes manifests ready
• Monitoring: Prometheus + Grafana dashboards
• CI/CD: GitHub Actions workflows

🎭 User Personas

Primary: College Student (Sarah, 20)

• Needs: Quick understanding of complex topics before exams
• Preferences: Visual learner, prefers casual tone, uses mobile
• Pain Points: Dense textbooks, boring lectures, no personalization

Secondary: Self-Learner (David, 28)

• Needs: Career advancement through skill building
• Preferences: Structured learning, voice while multitasking
• Pain Points: Limited time, needs adaptive pacing

🔄 User Journey

1. Onboarding (2 minutes)

Google Sign-in → Profile Setup → Learning Preferences → First Lesson

2. Learning Session (15-30 minutes)

Topic Selection → AI Lesson Generation → Interactive Learning → 
Voice Explanations → Visual Diagrams → Quiz Assessment → Progress Tracking

3. Personalization Loop (Ongoing)

User Interactions → Memory Storage → Preference Learning → 
Adaptive Responses → Improved Experience

🎨 Core Features

MUST HAVE (MVP)

• ✅ Google OAuth Authentication
• ✅ Lesson Generation (Claude Sonnet 4 powered)
• ✅ Text-to-Speech Streaming (in-page playback)
• ✅ Image Generation (educational diagrams)
• ✅ User Memory (short-term + long-term)
• ✅ Interactive Chat (conversational tutoring)
• ✅ Progress Tracking (lesson completion)

SHOULD HAVE (Enhanced)

• 🔄 Fine-tuned Models (LoRA adapters for persona)
• 🔄 Embedding-based Memory (similarity search)
• 🔄 Advanced Quiz Engine (adaptive difficulty)
• 🔄 Multiple Voice Options (tone/accent selection)

COULD HAVE (Future)

• 🔮 Offline Mode (WASM TTS)
• 🔮 Video Explanations (clip generation)
• 🔮 Collaborative Learning (multi-user sessions)
• 🔮 Mobile App (React Native)

🎯 Technical Requirements

Functional Requirements

FR1: Lesson Generation

Input: { topic, user_profile, learning_level, tone }
Output: { title, objectives, segments[], metadata }
Time: <15s for Claude Sonnet 4 processing

FR2: Voice Streaming

Input: text content
Output: audio stream via WebSocket
Format: PCM/Opus chunks ≤100KB each
Latency: <500ms first chunk

FR3: Visual Generation

Input: educational prompt
Output: base64 PNG/SVG or URL
Display: inline rendering (no downloads)
Style: diagram/illustration focused

FR4: Memory Management

Storage: user opt-in required
Retrieval: embedding-based similarity search
Retention: configurable TTL + manual deletion
Context: 3-5 relevant items per prompt

Non-Functional Requirements

Performance

• API Response: <3s for simple prompts, <15s for Claude
• Audio Streaming: Continuous playback, no gaps
• Image Generation: <10s for educational diagrams
• Concurrent Users: 5+ simultaneous demo users

Security

• Authentication: JWT tokens, 24h expiry
• Content Filtering: Safety classifier for all outputs
• Data Privacy: GDPR-aware, minimal PII storage
• API Security: Rate limiting, input validation

🔌 API Specification

Authentication

POST /api/auth/google-login
POST /api/auth/refresh
GET  /api/auth/profile
PUT  /api/auth/preferences

Tutoring

POST /api/tutor/generate-lesson
POST /api/tutor/start-session
POST /api/tutor/chat
GET  /api/tutor/sessions

Multimodal

POST /api/image/generate
WebSocket /ws/tts (text → audio stream)
WebSocket /ws/chat (real-time conversation)

Memory

GET  /api/memory/retrieve
POST /api/memory/store
DELETE /api/memory/clear

🧪 Testing Strategy

Unit Testing

• ✅ Model validation and serialization
• ✅ Controller business logic
• ✅ Service integrations (mocked)

Integration Testing

• ✅ API endpoint workflows
• ✅ WebSocket communication
• ✅ Authentication flows

E2E Testing

• ✅ Complete user journey
• ✅ Multimodal content delivery
• ✅ Cross-browser compatibility

Performance Testing

• ✅ Concurrent user simulation
• ✅ Audio streaming latency measurement
• ✅ Memory usage optimization

🚀 Deployment Strategy

Development

docker-compose up -d

Production

kubectl apply -f infrastructure/kubernetes/

Monitoring

• Health Checks: /api/health endpoint
• Metrics: Prometheus + Grafana dashboards
• Logging: Structured JSON logs
• Alerts: Critical error notifications

📊 Analytics & Metrics

User Engagement

• Session duration and completion rates
• Feature usage (voice/images/memory)
• Learning topic popularity
• User retention and return visits

System Performance

• API response times and error rates
• WebSocket connection stability
• Audio streaming quality metrics
• Resource utilization (CPU/memory)

Learning Effectiveness

• Quiz completion and accuracy rates
• User feedback and satisfaction scores
• Learning objective achievement
• Adaptive response quality

🔒 Privacy & Ethics

Data Protection

• Minimal Collection: Only necessary learning data
• User Control: Memory on/off toggle, data deletion
• Transparency: Clear data usage explanation
• Compliance: GDPR-ready with consent management

AI Safety

• Content Filtering: Multi-layer safety checks
• Bias Mitigation: Diverse training data and validation
• Transparency: AI decision explanation when requested
• Human Oversight: Manual review for sensitive content

📈 Roadmap

Phase 1: MVP (24 hours)

• ✅ Core multimodal functionality
• ✅ Google authentication
• ✅ Basic personalization
• ✅ Docker deployment

Phase 2: Enhancement (Week 1)

• 🔄 Fine-tuned model integration
• 🔄 Advanced memory system
• 🔄 Performance optimization
• 🔄 Comprehensive testing

Phase 3: Scale (Month 1)

• 🔮 Kubernetes production deployment
• 🔮 Advanced analytics
• 🔮 Multi-tenant architecture
• 🔮 Mobile application

Phase 4: Enterprise (Month 3)

• 🔮 SSO integration
• 🔮 Admin dashboard
• 🔮 Custom model training
• 🔮 API marketplace

🎯 Success Criteria

Hackathon Demo

• ✅ Live Demo: End-to-end user journey in <5 minutes
• ✅ Technical Depth: Architecture diagram and code walkthrough
• ✅ Innovation: Unique multimodal personalization approach
• ✅ Scalability: Production-ready foundation demonstrated

Post-Hackathon

• 📈 User Adoption: 100+ beta users within 2 weeks
• 📈 Engagement: 70%+ session completion rate
• 📈 Satisfaction: 4.5+ star rating from users
• 📈 Technical: 99.9% uptime with <500ms response times

---

This PRD serves as the definitive guide for building the Multimodal Personal Tutor. All implementation decisions should align with these requirements and success criteria.