Inspiration
As a student, I understand how difficult it can be to organize study time, decide what to learn next, and practice consistently. This inspired me to build AI Study Assistant, a simple tool that helps students plan their learning and practice questions.
How I Built It
I built the project using Python and Flask for the web application, with HTML, CSS, and JavaScript for the user interface. I also implemented an MCP server with tools for creating study plans and generating practice questions, demonstrating how these features can be exposed to an agent-based experience.
Challenges I Faced
As a beginner developer, I faced challenges setting up the Python environment, installing compatible dependencies, organizing project files, and getting the MCP server running correctly. I solved these problems step by step by testing the application and debugging errors.
What I Learned
This project helped me gain practical experience with web development, Python libraries, API communication, MCP tools, debugging, and GitHub. Most importantly, I learned that building a project involves experimenting, solving problems, and improving through practice.
What's Next
I would like to enhance the assistant with AI-powered personalized recommendations, progress tracking, and integration with a real AI model to make learning more adaptive and engaging.
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