A hybrid proctoring approach blends AI with the efforts of human proctors and instructors to effectively detect and prevent cheating.

3 reasons a hybrid proctoring approach makes sense

AI needs to be combined with the efforts of human proctors and instructors to effectively detect and prevent cheating

As online learning increasingly becomes part of the higher-ed experience, it is imperative that the methods and technologies used to deliver and monitor online learning maintain high levels of integrity, fairness and usability. When it comes to online assessments and proctoring, there are multiple models—AI-only monitoring (fully automated), human-only monitoring or a combination of both: hybrid proctoring.

Each model offers a slightly different approach, and each has benefits. AI and machine learning tools utilize technology to improve monitoring efficiency, provide better exam insights, and strengthen approaches to online teaching and learning. Human oversight allows for decisions to be made using an individual’s knowledge, logic, and reasoning, making students feel more confident that they are not being assessed solely by a machine.

But the most effective online proctoring model blends these benefits by combining AI, machine learning, and human intervention to detect unexpected behaviors, ensure fairness, and uphold integrity. The convenience that AI and machine learning provide, combined with a human’s ability to make decisions based on complex and unforeseeable situations, improves the entire testing experience for both students and instructors.

Here are three ways that a hybrid proctoring model—where AI and human oversight are used in tandem—creates the ideal testing experience for both instructors and students.

1. A Less Invasive Experience for Students

With fewer distractions during exams, students are able to focus on showing what they have learned. However, proctoring AI tools that are overly sensitive can trigger flags and create unnecessary interruptions.

Most proctoring AI can detect sound, but some platforms have AI with smart voice detection. While these terms are often used interchangeably, how they impact the test experience is very different.

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