Enabling Scalable AI-Based Evaluation for a Global Debate and Learning Platform
A global education technology platform offering debate, reasoning, and academic skill-building programs across 30+ countries needed to automate and scale its evaluation processes. The goal: ensure secure, consistent, and real-time assessment of thousands of students participating in live and asynchronous competitions.
Challenge
Manual scoring and proctoring workflows were limiting scalability and impacting evaluation consistency.
Inability to validate session authenticity in real time & Inconsistent scoring
High support overhead during peak test windows
Solution
Innovation Strategy
Entrans built an AI-native evaluation engine that combined facial recognition-based proctoring, behavioral tracking, and NLP-based scoring of spoken and written responses—all within a scalable cloud architecture.
Collaborative Approach
Our engineers and data scientists worked with the client’s product and operations teams to align scoring logic, reduce false flags, and optimize for usability.
Key Initiatives
- Built real-time proctoring with facial recognition and behavior analysis
- Developed NLP-based automated scoring aligned with rubrics
- Integrated evaluation APIs into the client’s core learning platform
The Outcome
Quantitative Results:
- 95% automation in evaluation workflows
- Real-time monitoring for over 10,000 concurrent sessions
- 40% drop in support queries related to scoring or proctoring
Business Transformation
The platform is now capable of delivering consistent, real-time assessments at scale—freeing up human resources, reducing evaluation bias, and improving learner experience across global markets.
Future-Ready
With AI at the core of its evaluation framework, the client is now positioned to expand into new learning domains while maintaining quality and scale.
Client Quote
"Partnering with Entrans allowed us to completely rethink scale and quality in evaluation. We now have the speed, accuracy, and confidence to grow without worrying about manual bottlenecks."
— Head of Product, Global Debate & Learning Platform
Outcomes
The platform was successfully developed and delivered within a remarkably short timeframe of four months.
The platform effectively monitors and reports foul language during real-time debates.
A highly scalable social platform capable of handling increasing user numbers and future feature expansions.
