Study Buddy AI

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\title{Study Buddy AI} \author{AWS Cloud Club} \date{}

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\section*{Inspiration}

Students increasingly rely on AI tools to finish homework quickly, often receiving answers without fully understanding the material. At the same time, many students struggle with managing multiple assignments, deadlines, and exams during busy weeks. We wanted to build an AI system that helps students organize their workload and decide what to work on first rather than simply giving answers.

\section*{What it does}

Study Buddy AI is an AI-powered task prioritization tool for students. Users can upload assignments such as PDFs or slides, and the system analyzes the content to estimate difficulty, required time, and urgency. Based on these factors, the system generates an ordered task list that helps students decide which assignments to start first and how to manage their workload.

\section*{How we built it}

We built Study Buddy AI as a web application. Uploaded documents are stored in Amazon S3 and processed using AI models to extract information about assignments. The backend uses Node.js and MongoDB to store user data and compute task priority scores based on due date, difficulty, estimated time, and user confidence. The frontend dashboard, built in React, displays prioritized tasks and assignment deadlines.

\section*{Challenges we ran into}

One challenge was reliably extracting useful information from different assignment formats such as PDFs, slides, and images. Another challenge was designing a prioritization algorithm that balances multiple factors like deadlines, estimated difficulty, and time requirements. We also had to carefully scope the project to focus on a strong core feature rather than implementing too many incomplete features.

\section*{Accomplishments that we're proud of}

We are proud of building a working prototype that can process assignment uploads and generate a prioritized task list for students. The system demonstrates how AI can support better time management rather than simply providing answers. We also designed a scalable architecture that can support additional features in the future.

\section*{What we learned}

Through interviews and feedback, we learned that many students underestimate how long assignments will take and often procrastinate until deadlines are close. We also saw that AI can play a useful role in guiding how students approach their work rather than replacing the learning process. Technically, we learned how to combine document processing, AI models, and cloud infrastructure into a functional application.

\section*{What's next for Study Buddy AI}

Next, we plan to integrate Study Buddy AI with platforms like Canvas and Google Calendar so assignments and deadlines can be imported automatically. We also want to improve the accuracy of difficulty and time estimates using better AI models. Long term, we hope to expand the platform into a full academic productivity assistant that helps students manage their workload more effectively.

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Built With

• javascript
• mongodb
• node.js
• novalite2
• react