Inspiration
-- Traditional online learning platforms often struggle to provide personalized learning experiences, leading to decreased engagement and suboptimal learning outcomes. Many platforms use a one-size-fits-all approach, failing to adapt to individual student strengths, weaknesses, and learning speeds. This results in frustration, disengagement, and gaps in knowledge retention for students.
What it does
-- Our project aims to develop an AI-powered adaptive learning platform that dynamically adjusts content, difficulty levels, and teaching methods based on each student’s learning patterns, preferences, and performance. This system will leverage machine learning models to analyze student interactions and provide real-time recommendations, ultimately enhancing the learning experience and improving educational outcomes.
How we built it
-- We used HTML, CSS and Plain JS for the front end and pass our user data to a Django backend where a softmax regression python algorithm that suggests a learning path based on user data.
Challenges we ran into
-- Integrating The backend and frontend, Making the data set uncontaminated so the algorithm doesn't discriminate / develop unconscious bias
Accomplishments that we're proud of
-- Taking a small step towards equality
What we learned
-- Learnt AI Ethics and teamwork
What's next for Group 25 | UofT AI Hackathon Project
-- Potentially turn this project into a startup.
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