# 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:** - [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 --- **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!**