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Application that creates playable RPGs based on user selected topic, which allow users to make decisions and learn the consequences in real life scenarios. Used LangChain to feed Wikipedia articles as context GPT-3.5 based on user topic

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styled-man/story-blitz

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📖 Story Blitz

Story Blitz is an innovative project developed during the Hackabull 2023 hackathon, where it garnered significant recognition by winning 1st place for the Monic.AI challenge and securing 2nd place overall amidst over 200 attendees.

Overview

StoryBlitz is designed to create educative story RPGs (Role-Playing Games) which users interact with to practice medical subjects. It employs a unique approach to learning where users navigate through stories and make decisions based on their medical knowledge. If a user answers incorrectly, the story adapts and explains the consequences of the decision, providing an engaging and informative learning experience.

Technologies Used

  • Next.JS: For building the user interface and handling frontend logic.
  • Node.JS: To manage the backend logic and server operations.
  • TypeScript: Employed to ensure type safety and enhance development efficiency.
  • GPT-3.5: Utilized to generate dynamic and engaging story content.

Features

  • Interactive Learning: Engage with educational RPG stories that adapt based on your medical knowledge and decisions.
  • Dynamic Storytelling: Utilizes GPT-3.5 to create compelling and diverse story scenarios.
  • Educative Feedback: Receive informative feedback and explanations upon making decisions within the story.
  • Medical Practice: A unique approach to practicing and learning about various medical subjects through interactive storytelling.

Awards

  • 🏆 1st Place in the Monic.AI Challenge at Hackabull 2023
  • 🥈 2nd Place Overall at Hackabull 2023

About

Application that creates playable RPGs based on user selected topic, which allow users to make decisions and learn the consequences in real life scenarios. Used LangChain to feed Wikipedia articles as context GPT-3.5 based on user topic

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