Secure AI-powered assessment solutions for educational institutions, testing bodies, and EdTech providers.
OpenSJLab is a leading AI-powered online live video exam and test monitoring platform creatively designed to ensure secure, transparent, as well as, scalable remote assessments. Their platform improves academic integrity by analysing candidates in real-time through video and audio streams. Besides, their platform also detects suspicious activities and generates detailed post-exam reports.
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Amazon Bedrock, Anthropic Claude, RAG (Retrieval-Augmented Generation), Amazon ECS, AWS Fargate, Application Load Balancer (ALB), Amazon CloudFront, Amazon VPC, Amazon S3, Amazon RDS (PostgreSQL), Amazon ElastiCache, AWS Certificate Manager, AWS Secrets Manager, AWS IAM, SOC 2 Framework, Terraform, GitHub Actions, Pritunl
OpenSJLab required a resilient, scalable, and exceptional infrastructure to support its swiftly growing AI-enabled exam monitoring solutions. The platform had to smoothly handle fluctuating workloads, particularly during peak examination windows. Also, concurrent test-takers should be able to expand to 10,000+ users, thereby assuring constant operation and availability alongside low-latency processing of live video and audio streams. Since achieving 99.9% uptime was a critical requirement, necessitating a highly available serverless architecture capable of automated adjustability and quick recovery during high-stakes assessments, was also utmost important.
Security and data integrity were paramount priorities, considering the critical nature of student data and the high stakes of academic certification. The infrastructure had to assure end-to-end encryption for all media streams, besides maintaining strict access controls for guaranteed privacy and also regulatory compliance. Furthermore, the platform required advanced Generative AI capabilities to move beyond simple motion detection, requiring a system that could provide contextual behavioural analysis and automated integrity reporting to reduce the manual burden on educational administrators.
To balance cost-efficiency with high performance, OpenSJLab needed a "pay-as-you-go" model that eliminated the overhead of idle servers during non-exam periods. The solution required intelligent resource allocation through AWS managed services and automated workflows to streamline the generation of post-exam audit reports. Additionally, the architecture demanded a safe and isolated data environment to ensure that critical exam artefacts were protected. Plus, they had to be completely accessible not only for institutional audits but also for global expansion across 15+ countries.
Infrastructure Scalability and High Concurrency: Managing a large-scale EdTech platform requires the ability to handle enormous, unpredictable surges in traffic during global examination windows. The infrastructure needed to support 10,000+ concurrent video and audio streams without performance degradation. Switching to a serverless, event-driven model was fundamental for the system to scale dynamically while maintaining minimal lag for real-time monitoring.
Contextual AI Accuracy and Fraud Detection: Traditional monitoring tools often struggle with false positives, failing to distinguish between harmless environmental noise and actual misconduct. The challenge was to integrate Generative AI and advanced computer vision to deliver human-like contextual understanding, accurately detecting face absence, camera obstruction, and suspicious verbal activity across diverse lighting conditions and multiple languages.
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Generative AI was integrated via Amazon Bedrock to provide contextual analysis of candidate behaviour, moving beyond simple motion detection to understand the intent behind flagged activities.
To handle spikes of 10,000+ concurrent test-takers, the platform was built on AWS Lambda and Amazon ECS. This event-driven, serverless approach allows for spontaneous scaling during peak exam periods while eliminating the costs of maintaining idle servers. Moreover, Amazon CloudFront was utilised to deliver low-latency media streams to students across 15+ countries.
Amazon Transcribe and Amazon Translate were leveraged to capture and process multilingual audio, detecting unauthorized verbal communication. Generative AI models then synthesize these transcripts and visual flags into automated, concise Integrity Reports, reducing the manual review time for instructors by 60%.
By utilising AWS Key Management Service (KMS) and AWS Secrets Manager, a robust security framework was implemented for encryption of critical student PII and exam artefacts. All data is securely stored in Amazon S3 with strict lifecycle policies and access controls, ensuring compliance with international education data privacy standards.
Amazon Cognito was integrated to provide secure, scalable user authentication and authorisation. This ensures that only authorised proctors and students can access specific exam sessions, helping maintain a secure environment for high-stakes assessments.
Amazon CloudWatch and AWS X-Ray provide extensive monitoring and tracing across the entire AI pipeline. This enables real-time detection of video processing congestion, ensuring consistent 99.9% uptime and an uninterrupted experience for users across global regions.
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