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Main page
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upload your note
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select a future date
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click generate quiz
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your quiz
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weak topics
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day by day routine
Inspiration
Preparing for an exam is not just about reading notes. Students also need to test their understanding, identify weak topics, and decide how to use their remaining study time effectively. Managing all these tasks can be difficult, especially when an exam is approaching.
I built StudySprint AI to bring these steps together in one simple application. My goal was to help students turn their own study notes into focused practice and a structured revision plan using generative AI.
What it does
StudySprint AI transforms study notes into an exam-preparation workflow.
- AI-generated quizzes: Generates five multiple-choice questions, each with four answer options, based on the student's notes.
- Instant assessment: Shows the student's score, correct answers, and explanations after submission.
- Weak-topic identification: Highlights topics that need additional attention based on quiz performance.
- Personalized revision schedule: Organizes day-by-day revision tasks around the exam date, available daily study hours, and identified weak topics.
- Simple, accessible experience: Students can start a study session without creating an account or maintaining a database.
How we built it
I built StudySprint AI using Python and Streamlit, with Google's Gemini API powering the question-generation process.
The application accepts study notes, an exam date, and the student's available daily study time. It sends the notes to Gemini to generate structured multiple-choice questions. After the student answers them, the application evaluates the responses, identifies weak topics, and creates a revision schedule based on the available preparation time.
I also used automated tests with pytest to verify important application behavior and help maintain reliability. I documented the project scope, product requirements, and technical specifications to keep development focused on a practical minimum viable product.
The application is deployed on Streamlit Community Cloud, and its source code is publicly available on GitHub.
Challenges we ran into
One of the main challenges was handling temporary Gemini API availability issues. During testing, the API returned a 503 UNAVAILABLE error because the selected model was experiencing high demand.
I investigated the error and switched to gemini-3.5-flash, which successfully generated quizzes in my tests. This experience taught me the importance of testing deployed applications, understanding API errors, and choosing a practical solution rather than assuming every failure comes from application code.
Another challenge was keeping the application focused. Instead of adding unnecessary features, I prioritized a complete workflow from study notes to quiz results and a revision schedule.
Accomplishments that we're proud of
I'm proud to have built and deployed a working AI-powered study assistant that combines quiz generation, performance feedback, weak-topic identification, and revision planning in one application.
I also created project documentation, made the source code publicly available, and tested the main workflow. StudySprint AI demonstrates how a generative AI model can be integrated into a practical educational tool rather than used only for open-ended chat.
What we learned
Building StudySprint AI strengthened my understanding of integrating generative AI into a web application, working with structured model outputs, testing application behavior, and deploying a project to a cloud platform.
I also learned that a useful AI application requires more than a model API call. It needs a clear problem definition, a focused user experience, error handling, testing, and a workflow that turns AI output into something actionable.
What's next for StudySprint AI
Future improvements could include support for uploading study materials, more flexible quiz difficulty, multiple quiz lengths, progress tracking across study sessions, and richer revision recommendations.
I would also like to explore additional reliability improvements, including configurable model fallbacks and clearer recovery guidance when an AI service is temporarily unavailable.
My long-term goal is to make StudySprint AI a more capable study companion that helps students prepare with greater focus, confidence, and structure.
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