## Inspiration

All our team members enjoy reading as a hobby across numerous genres. Unfortunately, reading comprehension and literacy overall have been declining, which poses a massive problem for future generations. There are countless factors at play here, and we can't solve all of them, but we can definitely work to kindle a spark that might just blossom into a love for reading. Many of these adjacent tools are made for classroom settings. On the contrary, we're built for students, by students, and by readers, for readers.

## What it does

Storyscape is an application aimed at developing elementary and middle schoolers' grasp over the English language. Turn on your mic and get ready to read some words! To be specific, we have identified three core skills to be problematic: diction, sentence structure variety, and expressiveness. You will be given an unlimited amount of examples for each of these and the opportunity to practice and replicate these examples. With the help of our cat assistant, you can learn how to read passages expressively and eloquently!

## How we built it

Storyscape is built on five major technologies:

1. Google Gemini Flash -- speedy, cost-efficient, great at storytelling
2. Hume AI -- personable, easy to integrate TTS, and great for sentiment analysis
3. React/Next.js -- a popular JS framework that simplifies many best practices built for vercel
4. Vercel -- easy deployment to the web
5. Firebase -- a handy tool for auth, functions, storage and analytics

## Challenges we ran into

One major problem we faced during the competition was the cold weather at night. We struggled to get some quality sleep due to the lack of comfortable sleeping arrangements :(

## Accomplishments that we're proud of

* We would actually use this app ourselves after some minor improvements!
* Created a polished and aesthetically pleasing web app demonstrating AI technologies
* Stayed up 24 hours in a row

## What we learned

This hackathon was a great opportunity to learn about all the different AI technologies that are out there and how we can implement them into our lives. We learned about sentiment analysis, some handy prompt engineering, and some techniques to interface with AI in a smart way.

## What's next for Storyscape

We're still working on adding themes into the text adventures. With better prompt engineering and utilisation of the best models to foster a closer connection to the text, Storyscape can become a true companion for those who want to augment their English skills.
## **Inspiration:**

Our inspiration stemmed from the realization that the pinnacle of innovation occurs at the intersection of deep curiosity and an expansive space to explore one's imagination. Recognizing the barriers faced by learners—particularly their inability to gain real-time, personalized, and contextualized support—we envisioned a solution that would empower anyone, anywhere to seamlessly pursue their inherent curiosity and desire to learn.

## **What it does:**

Our platform is a revolutionary step forward in the realm of AI-assisted learning. It integrates advanced AI technologies with intuitive human-computer interactions to enhance the context a generative AI model can work within. By analyzing screen content—be it text, graphics, or diagrams—and amalgamating it with the user's audio explanation, our platform grasps a nuanced understanding of the user's specific pain points. Imagine a learner pointing at a perplexing diagram while voicing out their doubts; our system swiftly responds by offering immediate clarifications, both verbally and with on-screen annotations.

## **How we built it**:

We architected a Flask-based backend, creating RESTful APIs to seamlessly interface with user input and machine learning models. Integration of Google's Speech-to-Text enabled the transcription of users' learning preferences, and the incorporation of the Mathpix API facilitated image content extraction. Harnessing the prowess of the GPT-4 model, we've been able to produce contextually rich textual and audio feedback based on captured screen content and stored user data. For frontend fluidity, audio responses were encoded into base64 format, ensuring efficient playback without unnecessary re-renders.

## **Challenges we ran into**:

Scaling the model to accommodate diverse learning scenarios, especially in the broad fields of maths and chemistry, was a notable challenge. Ensuring the accuracy of content extraction and effectively translating that into meaningful AI feedback required meticulous fine-tuning.

## **Accomplishments that we're proud of**:

Successfully building a digital platform that not only deciphers image and audio content but also produces high utility, real-time feedback stands out as a paramount achievement. This platform has the potential to revolutionize how learners interact with digital content, breaking down barriers of confusion in real-time. One of the aspects of our implementation that separates us from other approaches is that we allow the user to perform ICL (In Context Learning), a feature that not many large language models don't allow the user to do seamlessly.

## **What we learned**:

We learned the immense value of integrating multiple AI technologies for a holistic user experience. The project also reinforced the importance of continuous feedback loops in learning and the transformative potential of merging generative AI models with real-time user input.
## Inspiration

We believe that a brighter future lies in the hands of the bright minds of the children of today. As adults, we might be changing today but kids are the tomorrow of humanity. For a bright future ahead of us we need bright minds to walk the path that lies ahead of the world.
Sadly, the content that the newer generation has been exposed to recently has been extremely toxic and repetitive making a toxically addictive environment for the young ones while hindering their mental development.
As many of our own related cousins and siblings already suffer from the same fate as most children exposed to the internet today, we felt compelled to bring a change for the betterment of our future. We place our values in the importance of a curious, creative and educated mind and to ensure that our future generation has an environment to promote such development, we came up with **Storyteller AI**.

## What it does

Our AI aims to provide a very comfortable space for children of any age to learn something through an interactive story ranging over 20+ genres and over 15 types of emotions. The AI is very easy to use with little to no user input required and produces very creative and engaging outputs to make sure that the users don't lose any sense of interest. The AI takes text input from the user and responds in speech as well as text with a humane AI voice model.
It frequently asks you regarding the course of the story and your opinion on the plot so far and it uses that data to generate further parts of the story with your feedbacks and preferences in mind. It ensures friendly, comfortable, inspiring, and safe conditions for the user while being very creative and respectful at the same time.
Our Storyteller ensures a safe and easy to understand speech at all times while maintaining an engaging and instant-generative story. We are proud of its reliability and helpfulness.

## How we built it

We first laid out the plan for the project and decided what APIs to use for different parts of our project. After researching more about the sponsors, we decided the most suitable APIs for our workload.
We decided to use OpenAI API as the basic chatbot training model, Gradio for the front-end, Elevenlabs for text-to-speech and the RAFT method for prompt engineering. After distributing the workload amongst the team members, we made sure to communicate with each other to ensure the reliability of each and every step.

## Challenges we ran into

We are CS majors so we don't know how to communicate with each other
This was our first hackathon so we barely had any idea of what the 'hackathon' means
Some sponsor APIs were very very very hard to integrate into our project so we spent hours and all of our mental health trying to make something work which we didn't need.
Sleepy
Syntax because computer science

## Accomplishments that we're proud of

As a team, we are proud of making it through this hackathon, as our team is participating in one for the first time, considering it is such a big platform to meet new people and learn everything from the smartest people of our industry. We are also proud of creating our first AI project together considering this is our first time working together. Most of all we are really proud of how suitable our Storyteller is for children as no matter the amount of improper language or scenarios we try to push onto the AI (for research purposes) it ensures to teach the flaw in that train of thought with a friendly, interactive and very easy to understand examples.
And most of all, we are proud of all of our teammates for making this possible.

## What we learned

We learned a lot more than we were ready for and all of it was very exciting information that will change our future and career for the better. We learned about CoT for AI models, application of RAFT, how to integrate AWS, Groq, Hume ai, You.com and many more amazing AI company models, we learned a lot from our competitors as well as their experience was very evident in their polished and palatable projects. Most importantly we suppose, we learned to fall in love with the process as the journey follows along with it.

## What's next for Storyteller AI

Storyteller AI has limitless opportunities and we plan on making the best out of them. We are further going to implement Speech-to-Speech for even easier access to children.
We plan on a big update later on in the future where we integrate Hume AI into our project and analyze the emotions of the user on the basis of speech interactions and generate the story to elevate any negative feelings so that the user feels more comfortable, understood and connected.
Finally we plan on integrating image generation for potential reference to make the story more engaging for the user.