Introduction

In recent years, the use of artificial intelligence (AI) in proctoring examinations has grown in popularity in the assessment industry. AI-driven proctoring offers a number of potential advantages over traditional methods, including cost savings, improved accuracy and efficiency, and increased security. AI-driven proctoring technologies are able to detect cheaters and monitor student behavior in a variety of ways, these include; facial recognition, eye tracking, and voice analysis. As AI-driven proctoring becomes increasingly popular, it is important to consider the ethical implications of these technologies, including privacy concerns, algorithmic bias, and the potential for AI-driven proctoring to be used as a tool for surveillance.

Current Landscape of AI-driven Proctoring Technologies

There are a variety of AI-driven proctoring technologies available for use in the assessment industry. Many of these technologies are based on facial recognition and use computer vision algorithms to track the student’s face and eye movements. For example, ExamSoft provides a facial recognition system which compares the student’s face to a pre-captured image to verify identity. In addition, some AI-driven proctoring technologies use voice analysis to detect cheating by analyzing the student’s speech patterns and pauses. Similarly, ProctorU uses voice biometrics to detect changes in the student’s voice that may indicate cheating. Moreover, AI-driven proctoring can also be used to detect cheating by analyzing the student’s movements and interactions with the computer, such as mouse clicks and typing patterns.

Ethical Implications of AI-driven Proctoring

As AI-driven proctoring becomes increasingly popular, it is important to consider the ethical implications of these technologies. One of the primary concerns is privacy. AI-driven proctoring technologies can collect a large amount of data about the student’s behavior, including facial images, voice recordings, and mouse clicks. This raises questions about how this data is used and stored, and who has access to it. In addition, AI-driven proctoring technologies are vulnerable to algorithmic bias, which can lead to inaccurate assessments of student behavior. For example, facial recognition algorithms have been found to be less accurate for people of certain races or genders. Finally, AI-driven proctoring can be used as a tool for surveillance, particularly if the data collected is used for purposes other than proctoring.

Conclusion

AI-driven proctoring presents a range of potential advantages for the assessment industry, including cost savings, improved accuracy and efficiency, and increased security. However, it is important to consider the ethical implications of these proctoring technologies, including privacy concerns, algorithmic bias, and the potential for AI-driven proctoring to be used as a tool for surveillance. Further research is needed to assess the effectiveness of AI-driven proctoring, as well as to develop clear guidelines and policies to ensure that AI-driven proctoring is used responsibly.