AI proctoring, also known as automated proctoring, is transforming the way online exams are monitored and secured. It makes online exams more secure and efficient by preventing cheating, ensuring fairness, and saving time through automated supervision. AI-proctored exams help maintain fairness and trust helps maintain fairness and trust in digital testing environments across education, recruitment, and certification fields.
AI proctoring is a system that uses artificial intelligence to monitor candidates’ webcams, microphones, screens, and various user activities such as mouse movements, click rates, copy/pasting, and typing speed during an exam to detect suspicious behavior. Instead of relying only on human proctors, AI-based proctoring creates a scalable and consistent supervision system that works through video and audio input.
AI proctoring works by using the candidate’s webcam and microphone to automatically monitor and analyze their behavior and surroundings during the exam. It analyzes video and audio feeds from the candidate’s webcam and microphone throughout the test session to detect suspicious behaviors like looking away often or hearing other voices. Advanced algorithms analyze these patterns in real time, flagging suspicious activity for review by exam administrators.
AI-based proctoring enhances online exam security by ensuring that only the registered candidate takes the test and by continuously monitoring for suspicious behavior. It helps create a controlled and trustworthy testing environment that minimizes cheating and upholds the integrity of online assessments.
AI-powered proctoring relies on advanced visual and audio analysis to monitor the test environment in real time. By detecting faces, movements, sounds, and objects within the camera and microphone range, it identifies potential irregularities.
More than one person detected = Red flag (possible external assistance).
Missing or obstructed face = Alert (camera tampering or absence).
Consistent eye contact with the screen = Normal behavior.
Frequent or prolonged glances away = Orange alert (possible distraction or cheating attempt).
Visible electronic device = Red flag (unauthorized aid).
Before an online exam begins, AI proctoring systems perform identity verification to confirm that the registered candidate is the one actually taking the test. This process typically involves facial recognition, ID document matching, or live photo comparison. Throughout the exam, the system also uses continuous facial monitoring to verify that the same person remains in front of the camera, ensuring that no one else replaces or assists the candidate during the session.
TestInvite’s AI proctoring tool classifies detected activities into different alert levels, distinguishing between clear rule violations and potentially suspicious behaviors.
Another person appearing in the camera frame.
A detected phone or other electronic device.
Screen mirroring or unauthorized applications.
Excessive head or body movement.
Frequent gaze changes away from the screen.
Environmental noise or unclear audio cues.
While the AI flags these behaviors automatically, it does not make disciplinary decisions. Each alert must be reviewed manually by an exam administrator before action is taken. In this sense, the AI functions as an assistant, helping human proctors save time and focus on high-risk cases, not as a final authority.
AI proctoring becomes more effective when supported by additional security measures. Combining it with tools like a lockdown browser, time limits per question or section, and navigation restrictions helps minimize opportunities for cheating. These measures create a controlled testing environment where candidates focus solely on their exams, reducing distractions and preventing unauthorized actions.
While some systems position AI proctoring as fully automated, TestInvite follows a more reliable AI-assisted approach, where artificial intelligence supports but never replaces human judgment. In this setup, the AI continuously monitors the exam, flags potential issues, and organizes them for review.
However, the final decision always rests with a human proctor, who evaluates each alert in context to ensure fairness. This model combines efficiency with oversight. AI saves time, enables large-scale monitoring, and maintains consistency, while human reviewers provide the nuance and empathy that automation alone cannot achieve.
For AI proctoring to function effectively, clear environmental and procedural rules must be established before the exam. Without such guidelines, for instance, if candidates are not required to take the exam in a quiet setting, the system may constantly detect background noise or irrelevant movements. This makes it harder to accurately identify genuine irregularities.
The accuracy and reliability of AI proctoring depend heavily on the rules and technical conditions set before the exam begins. Clear environmental and equipment requirements help the AI interpret visual and audio data correctly.
For example, camera position plays a major role, a front-facing view allows accurate face and gaze tracking, while a side-facing camera may cause the system to misread normal movements as suspicious. Proper lighting, clear visibility, and a distraction-free background also ensure that the AI can distinguish genuine behavior from potential irregularities.
Establishing clear participation rules is essential to balance accessibility and exam integrity. Candidates may have different testing environments or needs, some may prefer quiet spaces with headphones, while others might take the exam from a café. Automated online exam proctoring systems can adapt to these variations, but only if institutions define what is acceptable in advance.
Allowing too much flexibility can reduce control and increase the chance of false alerts, while overly strict rules may exclude test-takers who lack ideal conditions. The key is setting consistent, reasonable standards that maintain fairness for all participants.
The number of cameras used in AI proctoring has a major impact on how effectively the system can monitor an exam. In single-camera setups, visibility is limited to one narrow angle, typically showing only the test-taker’s face and part of their surroundings. This restricted view makes it difficult to detect what’s happening outside the frame, such as activity behind the screen or on the desk.
By contrast, multi-camera systems, for example, when a secondary device like a smartphone is positioned beside the candidate, provide a much broader perspective of the testing environment. With multiple viewing angles, both the AI and human proctors can observe the candidate’s workspace more clearly, minimizing blind spots and improving overall exam security.
The key difference between live and post-exam AI proctoring lies in timing and control.
In live AI proctoring, the system analyzes activity in real time, allowing proctors to intervene immediately, for instance, pausing the exam, verifying the candidate’s surroundings, or addressing suspicious behavior as it happens.
In contrast, post AI proctoring takes place after the exam has ended. The AI reviews the recording, flags potential issues, and provides these insights for later evaluation. While this method is efficient for large-scale exams, it lacks real-time interaction. Once the test is over, the proctor cannot communicate with the candidate or investigate the situation further, which often leaves uncertainty or doubt about what truly happened.
AI proctoring also raises important questions about ethics and personal privacy. Many candidates take exams from home, where their surroundings, such as their room, bed, or even family members, may appear on camera. Not everyone has access to a quiet or private space, which creates concerns about fairness and inclusion. Should these individuals be prevented from taking the exam simply because their environment isn’t ideal?
On the institutional side, organizations must also comply with privacy regulations such as the GDPR, ensuring that biometric data, video recordings, facial information, and audio files are processed securely and used only for legitimate exam-related purposes. Responsible implementation of AI proctoring requires transparency, data protection, and respect for each candidate’s personal circumstances.
AI proctoring delivers the most value in situations where scalability and efficiency are essential. In large-scale exams with 50 to 100 or more participants, it becomes nearly impossible for a single human proctor to monitor every test-taker effectively. AI assists by analyzing all exam feeds simultaneously, automatically flagging unusual behavior, and prioritizing which sessions need human attention.
It is equally powerful during live exam sessions, where AI helps one proctor oversee dozens of candidates in real time by highlighting potential issues as they occur. In post-proctoring, AI streamlines the evaluation process by organizing video recordings, marking key moments of concern, and significantly reducing the time required for manual review.
| Aspect | Human proctoring | AI proctoring |
| Supervision method | Live human invigilators monitor test-takers in real time through webcam feeds. | AI algorithms monitor video and audio activity automatically. |
| Scalability | Limited, requires a proportional number of proctors for candidates. | Highly scalable, thousands of exams can be monitored simultaneously |
| Consistency | May vary based on proctor experience, attention, or fatigue. | Provides consistent, objective, and standardized monitoring across all sessions. |
| Detection capabilities | Relies on human observation and intuition to identify suspicious behavior. | Detects patterns and anomalies (e.g., gaze shifts, multiple faces, background noise) using computer vision and machine learning. |
| Cost efficiency | Higher cost due to staffing and scheduling requirements. | More cost-effective by reducing human involvement and manual supervision. |
| Availability | Limited to specific time zones and human working hours. | Available 24/7, enabling flexible scheduling and global exam delivery. |
| Response to alerts | Human proctor can intervene immediately during the exam. | AI flags suspicious activities automatically; human review may occur live or post-exam. |
| Fairness & bias | May be affected by human subjectivity or inconsistency. | Ensures unbiased monitoring and consistent evaluation across all candidates. |
| Best use case | Small-scale, high-stakes exams requiring human interaction. | Large-scale or remote exams needing secure, automated monitoring. |
[1] Aruğaslan, E. (2025). Artificial Intelligence-Based Proctored Online Exams: A Study on the Experiences of Distance Education Students. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi(65), 2728-2748. https://doi.org/10.53444/deubefd.1543471
[2] Nigam, Aditya & Pasricha, Rhitvik & Singh, Tarishi & Churi, Prathamesh. (2021). A Systematic Review on AI-based Proctoring Systems: Past, Present and Future. Education and Information Technologies. 26. 6421-6445. 10.1007/s10639-021-10597-x.
Yes, most AI-proctored exam systems can track and analyze eye movement using facial recognition and gaze detection technologies. These systems monitor where the candidate is looking during the exam to identify unusual patterns such as frequently glancing away from the screen, looking down at a potential device, or focusing outside the testing area.
Yes, AI-proctored exams can often detect the presence or use of a phone during a test. Advanced systems can recognize when a mobile device appears in the camera frame, such as when it’s placed on the desk or picked up by the test-taker. In addition to visual detection, AI also analyzes behavioral cues like repeatedly looking down, shifting focus away from the screen, or unusual hand movements, which may suggest that a candidate is checking a phone.
Yes, AI proctoring is reliable when supported by the right infrastructure, clear exam rules, and human review. Advanced AI systems provide consistent, unbiased monitoring throughout the entire exam, ensuring every candidate is treated equally. However, reliability also depends on camera and microphone quality, proper lighting, and well-defined testing guidelines. When AI technology is combined with transparent policies and human verification, it delivers a trustworthy and fair exam experience for all participants.
According to a study published in the Journal of Buca Faculty of Education, Dokuz Eylül University (2025), 85.3% of students found AI-proctored online exams to be reliable and trustworthy. The study highlights that with the right infrastructure, software, and institutional support, AI proctoring can create a secure and controlled testing environment comparable to traditional face-to-face assessments. [1]
