Study AI

"Learn Smarter, Not Harder"

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Inspiration

  • The need for a smarter, more efficient way to study: many students spend a lot of time making notes or flashcards manually, which is tedious and error-prone.
  • The rise of AI tools and large language models makes it feasible to automate parts of studying—creating explanations, flashcards, structuring content—so learners can focus on understanding rather than formatting.
  • A desire to help people learn faster, retain more, and manage study content in an organized way.

What it does

  • It acts as a personal AI tutor. Students can turn topics into structured study material: flashcards, quizzes.
  • Key features include:
    • AI-generated flashcards
    • Smart explanations with definitions, key concepts, benefits & limitations
    • Adaptive learning (different difficulty levels: easy/medium/hard) depending on the learner’s stage
  • Privacy & security are emphasised: materials are stored with user accounts. The app declares “no data shared with third parties”, although data isn’t encrypted.

How we built it

  • Front-end / client app: Android app built using Kotlin Multiplatform.
  • Back-end / AI services: Ktor Server - Kotlin Framework (Langchain4j and Koog).
  • Adaptive difficulty logic: Logic to select or generate content at different difficulty levels (easy, medium, hard).
  • Data storage/user accounts: Users’ materials (flashcards, quizzes) are stored under the user account with a backend database.

Challenges we ran into

  • Correctness and reliability of AI outputs.
  • Handling diverse domains (science, humanities, etc.).
  • Adaptive learning logic and user progress tracking.
  • Scaling backend infrastructure.
  • Privacy & security compliance.
  • User experience design on small screens.
  • Latency / responsiveness of AI features.

Accomplishments that we're proud of

  • Successfully launched Study AI on the Google Play Store, making AI-powered learning tools accessible to students worldwide.
  • Designed an intuitive UI/UX that makes studying simple, fast, and enjoyable.
  • Overcame technical challenges such as AI reliability, latency, and content quality, ensuring a smooth user experience.
  • Created a foundation for cross-platform scalability so that the app can expand beyond Android in the future.

What we learned

  • Integrating AI to produce pedagogically useful content is non-trivial: it's not just summarisation, but organising information well, generating useful questions, and ensuring correctness.
  • UX design is just as important as AI features: making the process seamless encourages adoption.
  • Performance and latency are key challenges when relying on AI over a network.
  • Ensuring content quality and reducing hallucinations is essential for learner trust.

What's next for Study AI

  • A lot of new things are coming soon, like Topic Generation, Community Quizzes, Achievements and much more.

Built With

  • ai
  • android
  • kotlin
  • kotlinmultiplatform
  • ktor
  • server
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