Pages displayed

Smart M Tutor – Project Description

Track D: Personalized Tutoring & Adaptive Learning

Inspiration

The inspiration behind Smart M Tutor came from the difficulties students face while studying from lengthy PDFs, textbooks, and previous-year question papers. Finding answers, understanding complex concepts, and identifying important exam topics can take a lot of time. We wanted to create a platform that makes learning easier, faster, and more personalised using Artificial Intelligence (AI).

How We Built It

We built Smart M Tutor as an AI-powered web application using Python and Streamlit. We integrated the Google Gemini API to provide AI-generated explanations and help students understand their study materials. Using PyMuPDF, the application extracts text from PDF documents, allowing students to upload course materials and ask questions about selected pages.

The platform includes six main sections: Course Material, AI Tutor, Quiz, Learning Dashboard, Frequently Asked Questions, and Previous-Year Question (PYQ) Analysis. These features are designed to bring study resources, practice, and learning progress tracking together in one place.

Challenges We Faced

One of our main challenges was handling large PDF files and extracting useful information from different types of study materials. We also faced difficulties integrating the AI API, managing dependencies, deploying the application online, and ensuring that answers were based on the selected course content. Improving the identification of repeated questions in previous-year papers was another challenge.

Accomplishments That We Are Proud Of

We are proud to have developed Smart M Tutor as a platform that combines multiple learning tools in a single application. Students can upload their study materials, ask questions, practise quizzes, monitor their learning progress, and analyse previous-year question papers. We are especially proud of turning our idea into a working web application and learning how to integrate AI into an educational project.

What We Learned

Through this project, we learned how to develop a web application using Python and Streamlit, integrate an AI API, process PDF documents, and work with external libraries. We also gained experience in debugging, managing software dependencies, deploying applications, and designing features around students' learning needs. Most importantly, we learned that building an AI application requires continuous testing and improvement to make its responses more useful and reliable.

What's Next for Smart M Tutor?

Our next goal is to improve Smart M Tutor by introducing more personalised learning recommendations based on students' performance and weak topics. We plan to enhance repeated-question detection, improve the accuracy of document-based answers, support more document formats, and provide smarter quiz generation. In the future, we aim to make Smart M Tutor a more adaptive learning platform that helps students prepare for examinations and learn at their own pace.