# TutorX-MCP
# Product Requirements Document (PRD)

**Educational AI Tutor MCP Server**

## 1. Overview

**Product Name:**

Educational AI Tutor MCP Server

**Purpose:**

To provide an adaptive, multi-modal, and collaborative AI tutoring platform accessible via browser, leveraging Model Context Protocol (MCP) for tool integration and Gradio for user-friendly interfaces.

**Target Users:**

- **Students:** K-12, higher education, lifelong learners
- **Teachers:** For classroom integration and progress monitoring
- **Administrators:** For curriculum management and analytics

---

## 2. Objectives

- **Deliver personalized, adaptive learning experiences**
- **Enable collaborative and interactive learning**
- **Support multiple input/output modalities (text, voice, handwriting, AR/VR)**
- **Ensure privacy and data security**
- **Integrate with external educational tools and standards**
- **Provide actionable insights and analytics for students and teachers**

---

## 3. Features

## 3.1 Core Features

- **Adaptive Learning Engine**
    - Concept graph with 50,000+ nodes (STEM and humanities)
    - Dynamic skill assessment and competency tracking
    - Personalized learning paths based on real-time feedback
- **Multi-Modal Interaction**
    - Text-based Q&A with error pattern recognition
    - Voice recognition with ASR and TTS
    - Handwriting recognition and digital ink processing
- **Assessment Suite**
    - Automated quiz and problem generation
    - Step-by-step solution analysis
    - Plagiarism and similarity detection
- **Feedback System**
    - Contextual error analysis and suggestions
    - Multimodal feedback (text, audio, visual)

## 3.2 Advanced Features

- **Neurological Engagement Monitor**
    - Integration with consumer-grade EEG devices
    - Attention, cognitive load, and stress detection
- **Cross-Institutional Knowledge Fusion**
    - Curriculum alignment with 10+ national standards
    - Textbook content reconciliation
    - Cultural adaptation engine
- **Automated Lesson Authoring**
    - AI-powered content generation from PDFs, videos, web

## 3.3 User Experience

- **Custom Dashboard**
    - Knowledge growth map
    - Temporal performance heatmap
    - Cognitive profile radar
- **Accessibility**
    - Screen reader compatibility
    - Text-to-speech and adjustable interface

---

## 4. Technical Requirements

- **MCP Server:** Exposes all core and advanced features as MCP tools
- **Gradio Interface:** User-friendly, customizable, and accessible
- **Microservices Architecture:** Modular design for scalability
- **Real-Time Data Processing:** Asynchronous task queues and caching
- **Local Access:** Browser-based access from local machine

---

## 6. Success Metrics

- **User Engagement:** Average session duration, repeat usage
- **Learning Outcomes:** Improvement in quiz/test scores
- **Adoption Rate:** Number of active users and institutions
- **Teacher Satisfaction:** Feedback on usability and effectiveness
- **Technical Performance:** Uptime, response time, error rates

---

## 7. Roadmap

1. **Phase 1:** Core adaptive learning engine and MCP integration
2. **Phase 2:** Multi-modal interaction and collaborative tools
3. **Phase 3:** Advanced features (engagement monitor, lesson authoring)
4. **Phase 4:** Analytics and performance monitoring

---

## 8. Risks and Mitigation

- **Privacy Concerns:** Implement strict data controls and transparency
- **Technical Complexity:** Modular design and clear documentation
- **User Adoption:** Provide training materials and support

---

## 9. Stakeholders

- **Product Manager:** Oversees development and alignment with educational goals
- **Developers:** Build and maintain the MCP server and Gradio interface
- **Designers:** Ensure intuitive and accessible user experience
- **Educators:** Provide feedback and guide curriculum alignment
- **Students:** End users and primary beneficiaries

---

## 10. Appendix

- **Glossary:** MCP, Gradio, adaptive learning, etc.
- **References:** Educational standards, privacy regulations, technical documentation

---