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EduAI Recommender landing page and system overview.
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Semester V curriculum syllabus and unit breakdown.
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Interactive diagnostic test interface with question palette.
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AI-powered learning recommendations and study preference setting
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Student progress tracking and score improvement analytics.
Inspiration
About the Project
Inspiration
Every student has different learning abilities and difficulties. We wanted to create a system that identifies weak areas and supports students with personalized learning.
What We Built
We developed an AI-based platform that analyzes student performance and classifies learning levels as Weak, Average, or Strong. It helps students identify areas that need improvement.
How We Built It
We used Python, FastAPI, SQLite, and SQLAlchemy for development. We also explored machine learning and Nebius AI integration for personalized recommendations.
What We Learned
We learned backend development, database management, API integration, machine learning concepts, and teamwork through practical project development.
Challenges We Faced
Our main challenges were managing student data, analyzing performance, connecting the database, and integrating AI recommendations. We solved them through testing and debugging.
Our Vision
Our vision is to make education smarter and more personalized by helping every student identify and improve their learning difficulties.
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for AI-Based Personalized Student Learning Recommendation System
Built With
- analysis
- analytics
- data
- decision
- educational
- forest
- generative
- html
- javascript
- learning
- machine
- nebius
- performance
- personalized
- python
- random
- recommendation
- sqlalchemy
- sqlite
- student
- technologies:
- technology
- tree
