Secure Exams with AI Precision
Advanced computer vision to prevent cheating while ensuring student privacy. Real-time edge inference for fair, secure, and reliable academic assessments.
Quantifying academic integrity standards
Our AI-powered solution addresses modern exam misconduct by providing real-time detection, privacy-first monitoring, and reduced false alarms.
Reported Misconduct
Students admitting to using unauthorized digital tools during remote assessments.
Detection Accuracy
AI-driven pattern recognition identifying anomalies with high precision.
false Alarm Rate
Significant reduction in false flags through adaptive biometric calibration.
Pilot Universities
Academic institutions currently testing our privacy-first integrity framework.
Ready to secure your assessment environment?
Explore our ethical AI prototype and privacy-first architecture.
Common Cheating Vectors
ProctorAI identifies and categorizes prevalent academic dishonesty techniques to build robust, AI-driven defense mechanisms.
Students utilize secondary devices, hidden browser tabs, and virtual machines to bypass standard proctoring software constraints.
- Secondary device screen mirroring
- Unauthorized browser tab switching
- Virtual machine environment masking
Advanced LLM integration allows students to generate real-time exam responses that mimic human writing patterns and logic.
- Real-time LLM response generation
- Human-like syntax pattern mimicry
- Automated prompt-based cheating
Hidden earpieces and bone-conduction devices provide students with external audio feeds during high-stakes assessments.
- Bone-conduction audio reception
- Hidden micro-earpiece streaming
- External voice-to-text assistance
Need to see our prototype solution? View our prototype
Real-Time Integrity Monitoring System
Our multimodal AI system monitors gaze, audio, and input dynamics in real time to ensure exam integrity while maintaining student privacy through edge-based processing.
Gaze & Screen Tracking
Detecting unauthorized peripheral focus
Our system monitors eye movement vectors and screen activity to identify when a student shifts focus away from the exam interface to external materials.
Privacy-First Design
All gaze data is processed locally on the edge device; no raw video is ever transmitted or stored.
Detection Capabilities
- 1Real-time gaze vector deviation analysis
- 2Multi-display detection and blocking
- 3Active window focus monitoring
Ambient Anomaly Detection
Identifying unauthorized vocal inputs
Advanced audio processing identifies speech patterns, whispers, or background noise that deviate from the expected quiet exam environment.
false Alarm Mitigation
The system is tuned to ignore common household sounds like fans or distant traffic to prevent false flags.
Detection Capabilities
- 1Real-time speech-to-text anomaly flagging
- 2Background noise suppression and analysis
- 3Automated whisper detection algorithms
Keystroke & Input Dynamics
Detecting non-human input patterns
We analyze typing cadence and input behavior to identify automated scripts, copy-paste activity, or unauthorized remote access tools.
Security Standard
Input dynamics are analyzed as metadata, ensuring no sensitive personal information is captured or logged.
Detection Capabilities
- 1Keystroke rhythm and latency profiling
- 2Detection of automated macro execution
- 3Clipboard and remote desktop monitoring
Transparent Flagging
Actionable insights for proctors
Suspicious events are compiled into a clear, timestamped report, allowing human proctors to review only the flagged moments.
Human-in-the-Loop
AI flags are recommendations; final disciplinary decisions are always made by human faculty members.
Detection Capabilities
- 1Automated incident timeline generation
- 2Confidence score for each flagged event
- 3Secure export for academic review boards
Deploy Secure Exam Environments
Protect academic integrity with our privacy-focused AI solution. Contact us to discuss integration with your university's existing learning management system.
Integrity without surveillance
Evaluate exam authenticity without compromising student dignity. Real-time inference runs purely on edge devices with cryptographic anonymization.
All visual and audio evaluation executes locally inside the student's browser sandbox. Raw video streams are processed in memory and never uploaded to remote servers.
Facial geometry and gaze angles convert instantly into non-invertible coordinate arrays. The system stores zero facial images, voice recordings, or biometric templates.
Algorithms only generate non-binding event timestamps for anomalous telemetry. Final academic determinations rest exclusively with accredited university faculty.
Context-aware models differentiate between organic physical gestures and deliberate infractions. Room echoes, rapid eye fatigue, and shifting posture remain penalty-free.
Compliant with FERPA, GDPR Article 9, and university ethical computing standards.
Experience the Solution Prototype
Test our AI-powered integrity tools designed to prevent cheating while protecting student privacy and ensuring fair assessment outcomes.
Detect exam anomalies with real-time AI monitoring
Identify common cheating patterns instantly using our edge-based inference engine, ensuring academic integrity without invasive surveillance.
Edge Inference
Local processing of exam behavior patterns.
Anomaly Alerts
Real-time flagging of suspicious activity.
Audit Logs
Transparent reporting for every flag.
Privacy-First Design
Edge-based processing ensures raw biometric data never leaves the student device.
Reduced false Alarms
Advanced algorithms distinguish between genuine cheating and normal test behavior.
Transparent Auditing
Clear logs and explainable AI metrics for every flagged exam anomaly.