AI-Enabled Proctoring

AI-Enabled Proctoring

Looking for information about AI-Enabled Proctoring? Read on to learn more about the AI-Enabled Proctoring and how to use it in your hiring process. 

What is AI-Enabled Proctoring?

AI-enabled proctoring refers to using artificial intelligence technologies to monitor and supervise online tests or assessments remotely.

It involves employing software tools that use computer vision, facial recognition, pattern recognition, speech recognition, eye-movement detection, plane detection, and other machine-learning algorithms to track and analyze test-takers' behavior.

For example, when employees take an online assessment, the AI proctor can detect if they are cheating by tracking browser activity, if someone else is in the room, etc.

In addition, it can flag suspicious behavior such as cheating attempts, unauthorized access to test materials, or attempts to copy or share test content. This technology can also help verify test-takers' identity and ensure they are the same person who registered for the test.

To summarize it simply:

  • It is an AI tool used for online tests or assessments. It could be used at workplaces or educational institutions.
  • It ensures fairness and academic integrity in all kinds of assessments and test-taking.
  • It uses multiple tools like facial recognition, machine-learning algorithms, etc.

AI-enabled proctoring is also called "remote proctoring," "smart proctoring," or "online proctoring."

Types of AI-Enabled Proctoring

Different types of AI proctoring are being widely used. Some of them are:

1. Live Proctoring: AI assists a human proctor by flagging cheating behaviors in live proctoring. The human monitors the test-takers through a camera.

2. Recorded Proctoring: This allows the test-takers to take the test at their convenience and records their behavior. The AI detects any unfair practices and flags them to the reviewers.

3. Automated Proctoring: AI is used to monitor the behavior of the test-takers in real-time and can instantly flag behaviors such as copying, cheating, accessing resources that are not authorized, etc.

4. Hybrid Proctoring: This can be a combination of different types of proctoring. For example, the test-takers can be live-proctored but may also be recorded for future review.

Further, each kind of the above-listed proctoring can be either one of, a combination of a few, or all of the following:

The Benefits of AI-Enabled Proctoring

AI-enabled proctoring comes with a host of benefits.

  • It can improve accuracy by decreasing the consequences of human error. It is like a second, more efficient set of eyes and ears. In addition, AI gathers multiple data points through various software, making it almost foolproof.
  • It can reduce test-takers' anxiety and also take the pressure off of test administrators.
  • It is cost-effective and decreases overall costs, eliminating the need to hire many people for proctoring tasks.
  • It can help with scalability as the same algorithm can be applied simultaneously for multiple assessments.
  • It increases convenience and accessibility by decreasing location dependability.
  • It has higher security than traditional proctoring as it eliminates impersonation and can assist with identity verification.
  • It is easy to install and can be implemented immediately.  

Ready to level up recruitment w/ AI-based proctoring? See how Capgemini, Ericsson & Vanguard benefitted from iMocha's solution.

Terms Related to AI-Enabled Proctoring

  • Behavioral Biometrics: An essential part of AI-enabled proctoring, it is the analysis of behavior patterns, such as typing speed, facial movements, mouse movements, keystroke dynamics, etc.
  • Proctoring Rules Engine: A proctoring rules engine is a set of guidelines and rules for AI-powered software that defines what to look out for during a test and how to respond if it detects any suspicious behavior. These rules help ensure the software is consistent and fair in its approach to monitoring assessments.
  • Machine Learning: Machine learning enables AI-powered software to identify suspicious behavior during tests over time better. It works by analyzing past data and user feedback to improve its accuracy and ability to catch cheating attempts.

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