Product Requirements Document (PRD)

# 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](https://www.gradio.app/)
    - [Azure OpenAI Documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/)
    - [Model Context Protocol](https://modelcontextprotocol.io/)
- **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!**