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.

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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.

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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.

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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.

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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.

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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)

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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.

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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.

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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

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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

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10. Appendix

• References:
  • Gradio Documentation
  • Azure OpenAI Documentation
  • Model Context Protocol
• Contact:
  • Hackathon team email/slack/discord

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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!