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Product Requirements Document (PRD)
Product Name
SkillSprout
Purpose
SkillSprout is an AI-powered microlearning platform designed to help users learn new skills through bite-sized lessons and adaptive quizzes. The platform leverages Azure OpenAI for content generation, Gradio for user interaction, and Model Context Protocol (MCP) for agent interoperability.
1. Objectives
- Deliver Personalized Microlearning: Provide users with concise, high-quality lessons and adaptive quizzes tailored to their chosen skill.
- Showcase Agentic Workflows: Demonstrate how multiple AI agents (lesson generator, quiz generator, progress tracker) can collaborate to enhance learning.
- Enable Interoperability via MCP: Allow external agents and applications to interact with the learning modules and user progress through MCP endpoints.
- Offer a Polished, User-Friendly Interface: Use Gradio to deliver an intuitive, engaging, and accessible experience.
2. Target Users
- Lifelong Learners: Individuals seeking to acquire or reinforce skills in short, focused sessions.
- Hackathon Participants: Developers and researchers interested in agentic workflows and MCP integration.
- Educational Institutions: Teachers and trainers looking for AI-driven microlearning tools.
- Integration Developers: Teams building apps that could benefit from plug-and-play learning modules.
3. Features & Requirements
3.1 Core Features
3.1.1 Skill Selection
- Users can select from a list of predefined skills (e.g., Python, Spanish, Public Speaking) or enter a custom skill/topic.
3.1.2 Micro-Lesson Delivery
- For the chosen skill, the system generates and presents a concise, focused lesson (text, optionally with links to videos or code snippets).
- Lessons are generated dynamically using Azure OpenAI.
3.1.3 Adaptive Quiz
- After each lesson, users receive a short quiz (e.g., multiple choice, fill-in-the-blank) tailored to the lesson content.
- The quiz adapts in difficulty based on user performance over time.
3.1.4 Progress Tracking
- The system tracks user progress (e.g., lessons completed, quiz accuracy, streaks).
- Progress is displayed visually (e.g., progress bars, charts).
3.1.5 Recommendations
- Based on performance, the system recommends the next lesson, a review session, or an increased difficulty level.
3.2 Enhanced Features
3.2.1 Voice Narration System
- AI-Powered Audio: Convert lesson content to natural-sounding speech using Azure Speech Services
- Multi-language Support: Neural voices supporting various languages and accents
- Voice Selection: Allow users to choose from different voice personalities
- Audio Export: Enable users to download narration files for offline learning
- Accessibility Enhancement: Provide audio-first learning for visually impaired users
3.2.2 Gamification System
- Achievement System: Unlock badges and achievements for various learning milestones
- Points & Levels: Experience points system with automatic level progression
- Progress Visualization: Enhanced progress bars, completion metrics, and visual feedback
- Streak Tracking: Monitor and reward consistent daily learning habits
- Skill Mastery: Calculate and display mastery percentage for each skill area
3.3 Agentic Architecture
- Lesson Agent: Generates concise lessons for the selected skill.
- Quiz Agent: Creates contextually relevant quizzes based on the lesson.
- Progress Agent: Monitors and updates user progress, provides feedback, and recommends next steps.
- Orchestrator: Coordinates the flow between agents and the user interface.
3.4 MCP Integration
- Expose endpoints for:
- Fetching the next lesson for a user/skill.
- Retrieving user progress data.
- Submitting quiz results.
- Ensure endpoints are documented and compatible with the Model Context Protocol.
3.5 User Interface
- Built with Gradio:
- Step-by-step workflow: Skill selection → Lesson → Quiz → Feedback/Progress.
- Clean, accessible design with clear navigation.
- Responsive for desktop and mobile.
4. Non-Functional Requirements
- Performance: Lessons and quizzes should be generated in under 5 seconds.
- Scalability: Support at least 100 concurrent users for demo purposes.
- Security: User data (progress, answers) is stored securely and not shared without consent.
- Accessibility: UI should be usable with screen readers and keyboard navigation.
- Reliability: System should handle API failures gracefully and provide user-friendly error messages.
5. Optional & Stretch Features
- Multi-modal Lessons: Incorporate images, audio, or video if supported by Azure OpenAI
- Custom Content Upload: Allow educators to add their own lesson modules
- Daily Reminders: Send notifications or emails to encourage regular learning
- Leaderboard: Display top learners (opt-in)
- Advanced Analytics: Detailed learning pattern analysis and predictive insights
- Social Learning: Collaborative features and peer-to-peer learning opportunities
✅ Recently Implemented Features
- ✅ Voice Narration: AI-powered audio synthesis with Azure Speech Services (COMPLETED)
- ✅ Gamification System: Achievements, points, levels, and progress rewards (COMPLETED)
- ✅ Enhanced Progress Tracking: Multi-dimensional analytics and visual feedback (COMPLETED)
6. Technical Stack
6.1 Core Technologies
- Backend: Azure OpenAI (GPT-4.1)
- Frontend: Gradio (Python)
- MCP Integration: Gradio MCP server functionality
- Data Storage: In-memory or lightweight database (for hackathon demo)
- Deployment: Hugging Face Spaces or Azure App Service
6.2 Azure OpenAI Rationale
Strategic Choice: Bridging Enterprise and Open Source
SkillSprout leverages Azure OpenAI to deliver the best of both enterprise-grade reliability and open source innovation:
🛡️ Enterprise-Grade Foundation
- Content Safety: Built-in content filtering ensures educational content is appropriate and safe for all learners
- Security & Compliance: Enterprise-level data protection with SOC 2, GDPR, and HIPAA compliance for educational institutions
- Observability: Comprehensive monitoring, logging, and analytics for production workloads and learning analytics
- Performance: Guaranteed SLAs, low latency, and scalable infrastructure for consistent user experience
- Global Availability: Multi-region deployment options ensuring worldwide accessibility for diverse learners
🚀 Open Source Innovation
- Model Context Protocol: Embraces open standards for seamless agent interoperability
- Open Architecture: Modular design compatible with any MCP-compatible client or educational platform
- Community Integration: Works with open source frameworks like Gradio for rapid prototyping and deployment
- Extensible Design: Easy to adapt, modify, and extend for different educational use cases
- Developer-Friendly: Modern APIs with robust documentation and active community support
💡 Educational Focus Benefits
- Production-Ready: Enterprise controls meet innovative open source capabilities for real-world deployment
- Content Appropriateness: AI safety features ensure suitable learning materials for all age groups
- Scalable Learning: Access to latest AI models while maintaining stability and educational governance
- Future-Proof: Continuous model updates and improvements without breaking existing integrations
This combination enables educational institutions, enterprises, and individual developers to confidently deploy AI-powered learning solutions at scale while maintaining the flexibility and innovation of open source development.
7. Success Metrics
- User Engagement: Number of lessons/quizzes completed per user.
- Learning Outcomes: Improvement in quiz scores over sessions.
- MCP Usage: Number of successful external calls to MCP endpoints.
- User Satisfaction: Positive feedback from hackathon judges and users.
8. Risks & Mitigations
| Risk | Mitigation |
|---|---|
| Slow response from Azure OpenAI | Cache common lessons/quizzes, optimize prompts |
| User data loss (demo) | Regular backups, clear communication |
| MCP integration complexity | Use official Gradio MCP templates and docs |
| Overly generic lessons/quizzes | Refine prompts, add manual review if possible |
9. Milestones & Timeline
| Milestone | Target Date |
|---|---|
| Project setup & Azure OpenAI config | Day 1 |
| Core agent logic implemented | Day 2 |
| Gradio UI complete | Day 3 |
| MCP endpoints exposed & tested | Day 4 |
| Polish, optional features, testing | Day 5 |
| Submission & documentation | Day 6 |
10. Appendix
- References:
- Contact:
- Hackathon team email/slack/discord
This PRD is designed for clarity, feasibility, and alignment with hackathon goals. Let me know if you need a version tailored for a specific audience (e.g., business, technical, or educational) or want to add/remove features!