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Study with Purpose

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🚀 About the Project — Propel Study AI

💡 Inspiration As students, we often struggle with two interconnected problems: staying focused while studying and understanding which career paths actually suit our strengths. Most productivity apps focus only on time tracking, while most career platforms rely on static questionnaires that feel disconnected from real learning behavior. We wanted to build a system that connects how a student studies with where they can go in life. That idea became Propel Study AI — a productivity-driven, aptitude-aware career recommendation platform designed for students worldwide.

🧠 What It Does Propel Study AI helps students: 1)Stay productive using focused study sessions 2)Measure strengths through a multi-domain aptitude test 3)Receive data-driven career and exam recommendations 4)Understand skill gaps and strengths with actionable feedback 5)Instead of hardcoding a few jobs, the system uses a scalable career taxonomy and weighted aptitude matching, allowing it to represent global careers across domains such as technology, healthcare, government, business, defense, and creative fields.

🛠️ How We Built It 1)We built Propel Study AI as a web-based prototype using: i)React + TypeScript for a fast, modular frontend ii)Tailwind CSS for a clean, responsive UI 2)Component-based architecture for features like: i)Pomodoro focus timer ii)Aptitude testing iii)Career & exam prediction iv)The recommendation system uses rule-based AI logic with weighted aptitude scoring to ensure transparency and explain ability. To accelerate development, we used Lovable AI to scaffold the initial UI and project structure. This allowed us to focus on designing original system logic, including: 1)Aptitude-to-career mapping 2)Fit percentage calculation 3)Strength and gap analysis 4)Scalable configuration for adding new careers or exams

⚙️ Challenges We Faced 1)Designing a system that scales globally without hardcoding thousands of careers 2)Balancing simplicity vs. meaningful recommendations within a hackathon timeline 3)Keeping the application stable and demo-ready while integrating multiple features 4)Ensuring the logic remained transparent, not a black-box model These challenges pushed us to prioritize clean architecture and extensibility over unnecessary complexity.

📚 What We Learned 1)How to design scalable recommendation systems 2)Why clear, explainable logic often beats opaque AI models 3)How low-code tools can be used responsibly to accelerate—not replace—engineering 4)How productivity data and aptitude insights can combine to create real student impact

🌱 Future Scope 1)Replace rule-based logic with ML-based ranking models 2)Add country-specific career filters 3)Generate personalized learning roadmaps 4)Enable long-term productivity and habit analysis

🎯 Impact Propel Study AI helps students study with purpose — not just to be productive, but to move closer to careers that genuinely match their strengths.

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