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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:** | |
| - [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!** | |