A Parameterized and Deterministic Framework for Robust AI-Driven Assessment and Integrity Validation
Recruitix is an AI-driven online assessment and proctoring platform designed to ensure fairness, reproducibility, and integrity in remote evaluations.
Unlike traditional AI-based testing systems that rely on random logic or opaque algorithms, Recruitix introduces a deterministic, parameterized, and auditable framework for technical, HR, and live interview assessments.
The system combines semantic grading, behavioral simulation, and live integrity monitoring to create a transparent, explainable, and ethical AI evaluation environment.
Recruitix can be used by academic institutions, corporate recruiters, and certification agencies for secure, large-scale, and unbiased assessments.
- ✅ Deterministic Evaluation Engine – Produces identical results for identical inputs, ensuring full reproducibility.
- ✅ Semantic Similarity Scoring – Uses Jaccard similarity and keyword weighting for accurate conceptual grading.
- ✅ HR Simulation Engine – Implements deterministic behavioral models for candidate profiling.
- ✅ Parameterized Proctoring System – Monitors live video and event data to detect integrity violations.
- ✅ Secure Firebase Backend – Authentication, real-time database, and safe environment-based credential handling.
- ✅ Responsive Web Interface – Developed using React + TypeScript with Framer Motion and Shadcn UI.
- ✅ Explainable and Auditable AI – Every score, deduction, and event is logged for transparency.
| Category | Technologies |
|---|---|
| Frontend | React.js, TypeScript, Shadcn UI, Framer Motion |
| Backend | Firebase (Auth, Firestore, Hosting) |
| Algorithms | Jaccard Similarity, Event-Driven Scoring |
| Security | Environment-based variables, OWASP compliance |
| Tools | Vite, Node.js, GitHub, VS Code |
User Interface (React + TypeScript)
↓
Deterministic Core Algorithms
├── Semantic Similarity Engine
├── HR Simulation Engine
└── Parameterized Proctoring
↓
Firebase Backend (Auth | Firestore | Event Logs)
↓
Dashboard & Integrity Report Visualization
semanticSimilarity.ts→ Computes conceptual and keyword-based gradinghrSimulationEngine.ts→ Generates deterministic behavioral test datamonitoringProfiles.ts→ Defines event severity for proctoring validationLiveInterview.tsx→ Live session and score tracking UI
git clone [https://github.com/debojyoti10cc/Recruitix.git](https://github.com/debojyoti10cc/Recruitix.git)
cd Recruitixnpm installCreate a .env file in the project root and add your Firebase configuration:
VITE_FIREBASE_API_KEY=your_api_key
VITE_FIREBASE_AUTH_DOMAIN=your_auth_domain
VITE_FIREBASE_PROJECT_ID=your_project_idnpm run devNow open http://localhost:5173 to view Recruitix in your browser.
| Metric | Description | Result |
|---|---|---|
| Reproducibility | Output consistency across runs | 99.9% |
| Semantic Fairness | Correlation with expert grading | 92% |
| Integrity Latency | Detection delay for violations | 340 ms |
| Security Validation | Firebase key exposure incidents | 0 |
Recruitix achieved stable, reproducible outcomes across all tests, validating its deterministic design and fair evaluation framework.
The global market for AI-based assessment and proctoring tools is projected to reach USD 12.8 Billion by 2030, growing at a CAGR of 16.5%.
Recruitix targets this space with three main differentiators:
- Transparent, explainable AI evaluation
- Deterministic and reproducible assessment logic
- Lightweight and secure cloud-based architecture
| Category | Description | Value (USD) |
|---|---|---|
| TAM | Total global AI assessment market | 12.8 Billion |
| SAM | Academic and HR-focused assessment systems | 3.84 Billion |
| SOM | Early achievable Recruitix share | 115 Million |
Recruitix’s scalable and ethical design allows it to penetrate both academic and corporate segments, making it suitable for long-term adoption and commercialization.
Recruitix/
├── src/
│ ├── components/
│ │ ├── LiveInterview.tsx
│ │ ├── monitoringProfiles.ts
│ │ └── semanticSimilarity.ts
│ ├── utils/
│ │ └── hrSimulationEngine.ts
│ └── App.tsx
├── public/
│ └── assets/
├── .env.example
├── package.json
├── vite.config.ts
└── README.md
- 🔒 No hardcoded credentials in source code.
- 🔒 Environment-based Firebase configuration.
- 🔒 Authentication with access tokens.
- 🔒 Compliant with OWASP Secure Coding Practices.
| Resource | Link |
|---|---|
| 🎥 Demo Video | [suspicious link removed] |
| 📄 Research Paper | [suspicious link removed] |
| 💻 GitHub Repository | https://github.com/debojyoti10cc/Recruitix |
| Name | Role | Institution |
|---|---|---|
| Debojyoti De Majumder | Lead Developer & Researcher | IEM Kolkata |
| Rupsa Dhar | Co-Developer & Tester | IEM Kolkata |
Special thanks to Prof. Dr. Moutushi Singh, Head, Department of Computer Science and Engineering (AI), Institute of Engineering and Management, Kolkata, for guidance, mentorship, and continuous academic support.
This project is licensed under the MIT License. You are free to use, modify, and distribute for research and educational purposes.
Recruitix demonstrates how deterministic algorithms, semantic logic, and ethical AI can transform modern remote assessments into transparent, fair, and secure processes. Its reproducible design ensures that every score is explainable, auditable, and trustworthy—setting a new benchmark for AI-integrated evaluation systems.
"Reproducibility builds trust — Recruitix builds reproducibility."