ExamShield AI – AI-Resistant Examination & Coding Assessment Platform

Online examinations and coding assessments are facing a major challenge due to the rapid adoption of AI tools. Candidates can use AI-powered browser extensions, external devices, code generation assistants, and other resources to obtain solutions during assessments. Traditional systems mainly focus on fullscreen restrictions, browser locking, and basic proctoring, which are no longer sufficient in the AI era. The challenge is not only detecting cheating but also verifying whether the candidate genuinely understands and produced the submitted work. Institutions require a smarter assessment syst Process I started by observing real-world coding assessments and identifying how candidates could bypass existing restrictions. I analyzed common cheating methods such as AI browser extensions, tab switching, copy-pasting generated code, using mobile phones, and external assistance. After researching existing proctoring platforms, I noticed that most solutions focused on restrictions rather than understanding candidate behavior. I then designed ExamShield AI as a multi-layered assessment integrity platform. The solution combines a secure Electron-based examination environment, AI-powered webcam monitoring, behavioral risk scoring, coding timeline analysis, and AI-generated viva verification. Instead of relying on a single detection mechanism, the platform evaluates multiple behavior Results The proposed solution provides a comprehensive framework for AI-resistant examinations. It moves beyond simple browser restrictions and focuses on behavioral intelligence, coding authenticity, and knowledge verification. Key innovations include Coding Timeline Replay, Behavioral Risk Scoring, and AI Knowledge Verification. The platform is designed to improve assessment integrity, reduce AI-assisted malpractice, and provide faculty with actionable evidence rather than relying solely on automated flags. Reflection If given additional time, I would develop a working prototype with real-time AI viva evaluation, advanced anomaly detection models, and deeper integration with coding assessment platforms. I would also conduct pilot testing with students and faculty to measure accuracy, reduce false positives, and refine the risk scoring system. Future versions would include adaptive assessments, personalized question generation, and enhanced analytics dashboards to further strengthen examination integrity.