StudyPilot AI — Intelligent Study Companion

An intelligent study companion that helps students organize learning materials, plan study sessions, practice quizzes, and explore MCP-powered study assistance in one simple workspace.

Comment

💡 Inspiration

Students often use different applications to manage their notes, study materials, daily schedules, and practice questions. Switching between these tools can make studying less organized and time-consuming.

This inspired us to build StudyPilot AI — Intelligent Study Companion, a student-focused workspace that brings essential study activities together and explores how the Model Context Protocol (MCP) can expose useful study tools to compatible AI clients.

Our goal is to make study organization simpler, more accessible, and easier to extend with future AI capabilities.

🎯 What It Does

StudyPilot AI combines study organization and learning assistance in one web application.

Key Features

  • Student Dashboard: A central workspace for accessing study activities.
  • User Authentication: Registration and login for individual users.
  • PDF Learning Materials: Upload PDF documents and extract selectable text for revision.
  • Study Planner: Create study tasks, organize subjects, and track completion.
  • Quiz Practice: Answer practice questions and review quiz results.
  • Study Assistant: Receive basic study guidance through rule-based responses.
  • Simulated Alexa-Style Experience: Explore a conversational interface with optional browser speech input where supported.
  • MCP Study Tools: A separate MCP server exposes tools for study advice, quiz prompts, and study tips using Streamable HTTP.

The current prototype works locally without requiring a paid generative AI API.

🛠️ How We Built It

We built StudyPilot AI using Python and Flask for the backend, HTML and CSS for the user interface, JavaScript for browser interactions, and SQLite for local data storage.

PyMuPDF is used to extract selectable text from uploaded PDFs. Flask handles authentication, application routes, study tasks, quiz workflows, and assistant interactions.

The MCP component is implemented as a separate Python service. It exposes three study-related tools:

  • get_study_advice
  • create_quick_quiz
  • get_study_tip

The MCP server uses Streamable HTTP so that a compatible MCP client can discover and invoke these tools. The web application and MCP server run as separate processes during local development.

We chose a lightweight architecture so the project could be developed and demonstrated without depending on paid AI services. The current assistant uses rule-based logic and basic text extraction; integrating a generative AI model and retrieval-augmented generation is planned future work.

🏗️ Architecture

The project consists of four main layers:

  1. Presentation Layer: Browser interface, HTML templates, CSS, and JavaScript.
  2. Application Layer: Flask routes, authentication, study workflows, and assistant logic.
  3. Data Layer: SQLite database and local PDF storage.
  4. MCP Integration Layer: A separate Streamable HTTP server exposing study tools to compatible MCP clients.

The web application provides the student-facing experience, while the MCP service demonstrates how selected study functions can be made available through a standardized tool interface.

🚧 Challenges We Faced

One of the main challenges was bringing several study workflows into a single application while keeping the architecture lightweight and easy to run locally.

Another challenge was configuring the MCP environment and understanding the difference between a server starting successfully and a compatible client actually discovering and invoking its tools. MCP transport and client compatibility require careful validation.

We also wanted the prototype to remain accessible without requiring paid AI API credits. For this version, we used rule-based assistance and extractive PDF processing instead of a live large language model.

These constraints helped us identify the next steps needed to make the application more reliable, capable, and ready for broader use.

📚 What We Learned

Through this project, we explored:

  • Building web applications with Python and Flask.
  • Organizing application data with SQLite.
  • Extracting text from PDF documents.
  • Designing a student-oriented interface and study workflow.
  • Separating a web application from an MCP server.
  • Understanding MCP tools and Streamable HTTP transport.
  • Managing Python dependencies and local development environments.
  • Documenting software architecture, limitations, and future improvements.

We also learned the importance of testing integrations with a compatible client rather than assuming that a running server is sufficient proof of successful integration.

🚀 What's Next?

Our future plans include:

  • Integrating a generative AI model for contextual study assistance.
  • Adding retrieval-augmented generation (RAG) with document-grounded answers and source references.
  • Supporting OCR for scanned PDFs.
  • Generating quizzes with explanations and configurable difficulty levels.
  • Creating personalized study schedules based on deadlines and available time.
  • Adding calendar integration and reminders.
  • Expanding MCP tools to support study tasks and document workflows.
  • Improving automated testing, deployment, authentication security, and data protection.

⚠️ Current Status

StudyPilot AI is a working prototype intended for demonstration and further development.

The Alexa-style interface is simulated and is not a live Alexa+ skill or official Amazon integration. The assistant currently uses rule-based responses rather than a live generative AI model. The MCP server is a separate service, and successful tool discovery and invocation must be verified with a compatible MCP client before claiming full integration.

🌱 Our Vision

We envision StudyPilot AI becoming a more personalized learning companion that helps students organize their work, revise more effectively, and access study tools through intuitive interfaces and interoperable AI integrations.

Built With

Share this project:

Updates

Submission history