Spaced Repetition Learning Bot

Inspiration

The inspiration behind the Spaced Repetition Learning Bot came from the desire to address the common challenge of forgetting important information over time. We were motivated to find a solution that leverages AI and spaced repetition techniques to enhance learning and memory retention.

What it does

The Spaced Repetition Learning Bot is an AI-powered tool that generates personalized questions and answers based on user-provided content. It employs a spaced repetition system to strategically schedule review sessions for optimal knowledge retention. Users can engage with the generated questions and track their progress over time, ensuring long-term memory reinforcement.

How we built it

We built the Spaced Repetition Learning Bot using a combination of cutting-edge technologies. We utilized the LangChain framework to access OpenAI's GPT model which we used to generate the questions and answers from the inputted notes. We created a template which allows users to simply input notes and the questions and answers will be generated. Using Streamlit we created the UI which allows users to easily enter their notes and navigate to the "Quizzes" page where they can begin studying. In this page users can see the generated questions which they then answer and rate based upon how easily they were able to come across the answers, after which the true answer to the question is revealed. Using this data we created an algorithm, drawing inspiration from other learning models such as the SM2 algorithm, to create a priority queue which then presents the questions to the user based upon how easy or difficult it was for them to answer. Using this spaced repetition model, users are more effectively and efficiently able to learn and retain the information in their notes.

Challenges we ran into

The primary challenge that we came across when creating this project was utilizing the Streamlit framework, particularly the creation of buttons. Because we were new to this framework and its syntax much of the time we spent on this project was dedicated towards familiarizing ourselves with this tool. For this reason, we faced difficulties implementing code whose logic we had already established, the most notable of which was with the buttons.

Accomplishments that we're proud of

We are proud of how we were able to use a framework for the first time and are going to be able to use this knowledge to build even better apps in the future

What we learned

How to use LangChain, and how to use APIs and AI models. We think that the most important thing we learned was how to not give up and to continue to strive. Because we found out about this Hackathon relatively late, we found that the time crunch that we were in was hard because we are new to this technology. Additionally we became more familiarized with Python syntax (as we usually work in Java and C++), which is something that will definitely be useful for future projects.