Design of a Remote Proctoring System for Online Certifications in Nigerian Professional Bodies

The credibility of online certification examinations conducted by Nigerian professional bodies is increasingly challenged by the absence of robust remote proctoring infrastructure. This study designs a remote proctoring system for online professional certification examinations, with a case study from the Institute of Chartered Accountants of Nigeria (ICAN) South West district. The system is built using WebRTC for live video streaming, Python OpenCV for automated facial recognition monitoring, and a Node.js backend. The spiral development model is used to manage the technical risks inherent in real-time video processing and AI-based anomaly detection. Key features include identity verification at login, continuous webcam monitoring, AI flagging of suspicious behaviour, human proctor review queues, and post-exam audit reports. System testing involved 80 volunteer candidates and 5 professional proctors over three simulated examination sessions. Evaluation criteria include false positive flag rate, candidate experience satisfaction, and bandwidth efficiency. Results show an 87 percent precision rate in anomaly detection and strong candidate satisfaction scores despite initial concerns about surveillance. The study recommends graduated AI sensitivity settings to balance integrity enforcement with candidate dignity. This research supports the integrity of Nigeria's professional certification ecosystem as online credentialing becomes mainstream. Keywords: remote proctoring, online certification, ICAN, facial recognition, academic integrity