Inspiration
Students are increasingly targeted by scams disguised as internships, job offers, online courses, scholarships, and study-abroad opportunities. Many of these scams appear legitimate and often demand registration fees, processing charges, or personal information before disappearing.
As students ourselves, we noticed that there is no simple platform focused specifically on helping students verify these opportunities before they apply or make payments. This inspired us to build StudentShield AI, a platform designed to protect students from educational and career-related scams.
What it does
StudentShield AI helps students identify potentially fraudulent opportunities before they become victims.
The platform allows users to:
Analyze internship offers Verify job opportunities Check course advertisements Review scholarship offers Evaluate study-abroad opportunities Upload screenshots for analysis Report scams to help other students
The system generates a risk score, identifies warning signs, and provides recommendations to users.
How we built it
We built StudentShield AI using a modern full-stack architecture:
React.js for the frontend user interface Express.js for backend APIs Amazon DynamoDB for storing scans and community reports Google Gemini API for AI-powered scam analysis Tesseract OCR for extracting text from screenshots Vercel for frontend deployment
When a student submits text, a URL, or a screenshot, the platform extracts relevant information, analyzes it using AI, and stores results in DynamoDB for future reference and community intelligence.
Challenges we ran into
Some of the main challenges included:
- Distinguishing genuine opportunities from misleading advertisements
- Designing prompts that provide consistent AI analysis
- Handling screenshot text extraction accurately
- Structuring DynamoDB tables for scalable storage
- Creating a simple user experience for non-technical students
Accomplishments that we're proud of
- Built a student-focused scam detection platform
- Combined OCR, AI analysis, and community reporting
- Designed a scalable architecture using AWS DynamoDB
- Created a solution with real-world impact for students
What we learned
During development we learned:
- How to integrate AI-powered content analysis into web applications
- How DynamoDB can be used for scalable data storage
- How OCR can convert screenshots into machine-readable text
- Best practices for building and deploying full-stack applications
What's next for StudentShield AI
Future improvements include:
- Browser extension support
- Mobile application
- Real-time scam intelligence feeds
- Institution trust scoring
- Community moderation system
- Multi-language scam detection
- Integration with university career portals
Our goal is to make StudentShield AI a trusted platform that helps students make safer educational and career decisions.
Built With
- amazon
- amazon-web-services
- api
- axios
- css
- dynamodb
- express.js
- gemini
- github
- javascript
- node.js
- ocr
- react.js
- render
- tailwind
- tesseract.js
- vercel
- vite
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