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

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