Abstract
The increasing demand for online education has opened up new avenues for academic institutions and students alike. However, the issue of maintaining academic integrity during online exams has emerged as a significant challenge. To address this concern, artificial intelligence (AI) has been harnessed to create sophisticated online proctoring systems that can monitor students and detect any potential cheating behaviors. This chapter presents a comprehensive review of AI-assisted students online proctoring systems. It explores the current state of the art, highlighting the different techniques and technologies employed in these systems to ensure fair and secure online assessments. The objective is to provide a nuanced understanding of the advancements made so far and identify areas that require further research and development. In summary, this chapter presents a comprehensive review of the advancements, challenges, and future directions of AI-assisted online proctoring systems.
Top1. Introduction
Online education has gained significant popularity in recent years, especially with the advent of technological advancements and the ongoing COVID-19 pandemic (Chiu, 2021). While online learning provides flexibility and convenience to both educators and students, it also presents unique challenges, particularly in ensuring academic integrity during assessments and evaluations. To overcome this challenge (Barrot, 2021), AI-assisted online proctoring systems have emerged as a viable solution.
This chapter aims to provide an overview and review of AI-assisted online proctoring systems, focusing on their effectiveness, implementation, and associated concerns. By understanding the capabilities and limitations of these systems, educators, and institutions can make informed decisions about implementing such technology to maintain academic integrity in online assessments.
1.1 AI-assisted online proctoring systems
AI-Assisted Online Proctoring Systems (Nigam, 2021), often referred to as remote proctoring or e-proctoring systems, utilize artificial intelligence (AI) algorithms and computer vision techniques to monitor and analyze students' behavior during online assessments. The primary purpose of these systems is to prevent cheating and ensure examination integrity in online educational environments.
AI-assisted online proctoring systems typically consist of three key components: a secure online testing platform, remote monitoring tools, and AI algorithms. The secure online testing platform serves as a portal for delivering assessments, while remote monitoring tools enable the collection of students' audio, video, and screen activities during the exam. The collected data is then analyzed using AI algorithms, which flag suspicious behaviors for further investigation.
The mechanisms employed by these systems for monitoring and analysis vary but commonly include facial recognition, gaze tracking, keystroke analysis, and plagiarism detection. These mechanisms ensure that students' identities are verified, their attention is monitored, and any attempts at cheating or plagiarism are promptly identified.
AI-assisted online proctoring systems (Chen, 2020) (Figure 1) offer numerous benefits to educators, institutions, and students. Firstly, these systems provide a scalable solution that can monitor a large number of students simultaneously, reducing the burden on faculty members. Secondly, they offer real-time monitoring, allowing for immediate intervention in case of suspicious behavior. Thirdly, they enable a more inclusive assessment process by accommodating students with disabilities or special learning needs.
However, concerns have been raised regarding the privacy implications and potential biases associated with these systems. The constant monitoring of students during exams raises concerns about the invasion of privacy and the collection of sensitive data. Additionally, the effectiveness of AI algorithms in accurately detecting and flagging suspicious behavior remains a subject of debate. False positives or false negatives can undermine the credibility of the system and impact the trust between students and institutions.
Figure 1. AI-assisted online proctoring systems
Key Terms in this Chapter
Online Proctoring: A method of monitoring online assessments to ensure academic integrity.
Machine Learning: A subset of AI that enables machines to learn from data, recognize patterns, and make informed decisions.
Ethnocentric: A belief that one's own culture is superior to other cultures.
Formative Assessment: An assessment method used to evaluate student learning progress and provide feedback for improvement.
Natural Language Processing (NLP): A technique that enables machines to understand and analyze written or spoken language.
Algorithmic Bias: Biases exhibited by AI algorithms based on race, gender, or socioeconomic factors.
Artificial Intelligence (AI): The simulation of human intelligence in machines to perform tasks and solve problems autonomously.
Computer Vision: A technology that enables machines to interpret and analyze visual data, such as images and videos.
Equity Concerns: The disproportionate impact of proctoring systems on students with limited access to stable internet connections, reliable technology, or suitable testing environments.
False Positives/Negatives: Incorrectly flagging innocent behaviors as cheating or failing to detect sophisticated cheating methods.