GenQ

A website that allows you to choose any subject and topic and generates a customized study guide or interactive practice quiz.

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Inspiration

GenQ began with the aim of improving existing digital learning tools, and was inspired by quiz + study guide hosting platforms such as Quizlet and CourseHero. These platforms are part of an extremely innovative market that has changed how both students and teachers access and interact with educational content both in and out of the classroom. Unfortunately, platforms such as these have recently shifted towards a pay-to-use model, limiting access to the full range of their resources for users who can't afford a subscription. This transition creates barriers to education, especially for students and teachers in low-income communities or even developing countries. Recognizing this inspired me to create GenQ not as a new, revolutionary software, but as a major improvement on existing technology that aims to provide free access to quality educational tools for everyone.

What it does

GenQ is a Flask-based web application that allows users to generate customized practice quizzes and study guides easily. By simply inputting their desired subject, topic, and subtopic, users can choose between creating a study guide for comprehensive learning or a practice quiz for self-assessment. GenQ uses generative AI to create and present relevant and informative material, catering to a wide array of learning objectives and styles.

How I built it

I used VSCode and built the website using the Flask framework. The backend is written in Python, which handles all interaction with Google's generative AI model, Gemini Pro. The backend also utilizes py-mongo to interact with MongoDB, which holds all information about subjects, topics, and subtopics.

Challenges I ran into

Figuring out how to phrase the prompts that are fed into the generative AI model to be specific enough that I could parse the data proved to be a challenge, as its responses can vary. Another challenge I ran into was implementing free-response questions, which requires natural language processing (NLP) of some sort, to be able to analyze the user's answers. I unfortunately had to scrap this concept due to time constraints.

Accomplishments that I'm proud of

Mainly, I'm proud of the fact that I was able to create a working, presentable version of GenQ within the time limit of this hackathon.

What I learned

I learned a lot about how to interact with a generative AI model, as well as how to interact with a database with Python (specifically, MongoDB).

What's next for GenQ

In the future, I'd love to expand on the site, adding more subjects + topics, implementing free-response questions, and adding login functionality so users can save quizzes and study guides to their profile and even share them with others.

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