The onset of the COVID-19 pandemic changed the landscape of education and led to increased usage of remote proctoring tools that are designed to monitor students when they take assessments outside the classroom. While prior work has explored students' privacy and security concerns regarding online proctoring tools, the perspective of educators is under explored. Notably, educators are the decision makers in the classrooms and choose which remote proctoring services and the level of observations they deem appropriate. To explore how educators balance the security and privacy of their students with the requirements of remote exams, we sent survey requests to over 3,400 instructors at a large private university that taught online classes during the 2020/21 academic year. We had n=125 responses: 21% of the educators surveyed used online exam proctoring services during the remote learning period, and of those, 35% plan to continue using the tools even when there is a full return to in-person learning. Educators who use exam proctoring services are often comfortable with their monitoring capabilities. However, educators are concerned about students sharing certain types of information wi
The burgeoning of online education has created an urgent need for robust and scalable systems to ensure academic integrity during remote examinations. Traditional human proctoring is often not feasible at scale, while existing automated solutions can be intrusive or fail to detect a wide range of cheating behaviors. This paper introduces AutoOEP (Automated Online Exam Proctoring), a comprehensive, multi-modal framework that leverages computer vision and machine learning to provide effective, automated proctoring. The system utilizes a dual-camera setup to capture both a frontal view of the examinee and a side view of the workspace, minimizing blind spots. Our approach integrates several parallel analyses: the Face Module performs continuous identity verification using ArcFace, along with head pose estimation, gaze tracking, and mouth movement analysis to detect suspicious cues. Concurrently, the Hand Module employs a fine-tuned YOLOv11 model for detecting prohibited items (e.g., mobile phones, notes) and tracks hand proximity to these objects. Features from these modules are aggregated and fed into a Long Short-Term Memory (LSTM) network that analyzes temporal patterns to calculate
For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system.
Remote proctoring technology, a cheating-preventive measure, often raises privacy and fairness concerns that may affect test-takers' experiences and the validity of test results. Our study explores how selectively obfuscating information in video recordings can protect test-takers' privacy while ensuring effective and fair cheating detection. Interviews with experts (N=9) identified four key video regions indicative of potential cheating behaviors: the test-taker's face, body, background and the presence of individuals in the background. Experts recommended specific obfuscation methods for each region based on privacy significance and cheating behavior frequency, ranging from conventional blurring to advanced methods like replacement with deepfake, 3D avatars and silhouetting. We then conducted a vignette experiment with potential test-takers (N=259, non-experts) to evaluate their perceptions of cheating detection, visual privacy and fairness, using descriptions and examples of still images for each expert-recommended combination of video regions and obfuscation methods. Our results indicate that the effectiveness of obfuscation methods varies by region. Tailoring remote proctoring
Educators are rapidly switching to remote proctoring and examination software for their testing needs, both due to the COVID-19 pandemic and the expanding virtualization of the education sector. State boards are increasingly utilizing these software for high stakes legal and medical licensing exams. Three key concerns arise with the use of these complex software: exam integrity, exam procedural fairness, and exam-taker security and privacy. We conduct the first technical analysis of each of these concerns through a case study of four primary proctoring suites used in U.S. law school and state attorney licensing exams. We reverse engineer these proctoring suites and find that despite promises of high-security, all their anti-cheating measures can be trivially bypassed and can pose significant user security risks. We evaluate current facial recognition classifiers alongside the classifier used by Examplify, the legal exam proctoring suite with the largest market share, to ascertain their accuracy and determine whether faces with certain skin tones are more readily flagged for cheating. Finally, we offer recommendations to improve the integrity and fairness of the remotely proctored e
Privacy is one of the key challenges to the adoption and implementation of online proctoring systems in higher education. To better understand this challenge, we adopt privacy as contextual integrity theory to conduct a scoping review of 17 papers. The results show different types of students' personal and sensitive information are collected and disseminated; this raises considerable privacy concerns. As well as the governing principles including transparency and fairness, consent and choice, information minimization, accountability, and information security and accuracy have been identified to address privacy problems. This study notifies a need to clarify how these principles should be implemented and sustained, and what privacy concerns and actors they relate to. Further, it calls for the need to clarify the responsibility of key actors in enacting and sustaining responsible adoption and use of OPS in higher education.
Online exams conducted via video conferencing platforms such as Zoom have become widespread, yet ensuring exam integrity remains challenging due to the difficulty of monitoring multiple video feeds in real time. We present iExam, an online exam proctoring and analysis system that combines lightweight real-time face detection with deep face recognition for postexam analysis. iExam assists invigilators by monitoring student presence during exams and identifies abnormal behaviors, such as face disappearance, face rotation, and identity substitution, from recorded videos. The system addresses three key challenges: (i)efficient real-time video capture and analysis, (ii) automated student identity labeling using enhanced OCR on dynamic Zoom name tags, and (iii) resource-efficient training and inference on standard teacher devices. Extensive experiments show that iExam achieves 90.4% accuracy in real-time face detection and 98.4% accuracy in post-exam recognition with low overhead, demonstrating its practicality and effectiveness for online exam proctoring.
In response to the Covid-19 pandemic, educational institutions quickly transitioned to remote learning. The problem of how to perform student assessment in an online environment has become increasingly relevant, leading many institutions and educators to turn to online proctoring services to administer remote exams. These services employ various student monitoring methods to curb cheating, including restricted ("lockdown") browser modes, video/screen monitoring, local network traffic analysis, and eye tracking. In this paper, we explore the security and privacy perceptions of the student test-takers being proctored. We analyze user reviews of proctoring services' browser extensions and subsequently perform an online survey (n=102). Our findings indicate that participants are concerned about both the amount and the personal nature of the information shared with the exam proctoring companies. However, many participants also recognize a trade-off between pandemic safety concerns and the arguably invasive means by which proctoring services ensure exam integrity. Our findings also suggest that institutional power dynamics and students' trust in their institutions may dissuade students'
The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular di
Online proctoring has become a necessity in online teaching. Video-based crowd-sourced online proctoring solutions are being used, where an exam-taking student's video is monitored by third parties, leading to privacy concerns. In this paper, we propose a privacy-preserving online proctoring system. The proposed image-hashing-based system can detect the student's excessive face and body movement (i.e., anomalies) that is resulted when the student tries to cheat in the exam. The detection can be done even if the student's face is blurred or masked in video frames. Experiment with an in-house dataset shows the usability of the proposed system.
Online exams have become widely used to evaluate students' performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.
Multiple choice questions are at the heart of many standardized tests and examinations at academic institutions allover the world. In this paper, we argue that recent advancements in sensing and human-computer interaction expose these types of questions to highly effective attacks that today's proctor's are simply not equipped to detect. We design one such attack based on a protocol of carefully orchestrated wrist movements combined with haptic and visual feedback mechanisms designed for stealthiness. The attack is done through collaboration between a knowledgeable student (i.e., a mercenary) and a weak student (i.e., the beneficiary) who depends on the mercenary for solutions. Through a combination of experiments and theoretical modeling, we show the attack to be highly effective. The paper makes the case for an outright ban on all tech gadgets inside examination rooms, irrespective of whether their usage appears benign to the plain eye.
Most of today's educators are in no shortage of digital and online learning technologies available at their fingertips, ranging from Learning Management Systems such as Canvas, Blackboard, or Moodle, online meeting tools, online homework, and tutoring systems, exam proctoring platforms, computer simulations, and even virtual reality/augmented reality technologies. Furthermore, with the rapid development and wide availability of generative artificial intelligence (GenAI) services such as ChatGPT, we are just at the beginning of harnessing their potential to transform higher education. Yet, facing the large number of available options provided by cutting-edge technology, an imminent question on the mind of most educators is the following: how should I choose the technologies and integrate them into my teaching process so that they would best support student learning? We contemplate over these types of important and timely questions and share our reflections on evidence-based approaches to harnessing digital learning tools using a Self-regulated Engaged Learning Framework we have employed in our research in physics education that can be valuable for educators in other disciplines.
In the near future a governmental body will be asked to allow companies to use AI for age verification. If they allow it the resulting system will both be easily circumvented and disproportionately misclassify minorities and low socioeconomic status users. This is predictable by showing that other very similar systems (facial recognition and remote proctoring software) have similar issues despite years of efforts to mitigate their biases. These biases are due to technical limitations both of the AI models themselves and the physical hardware they are running on that will be difficult to overcome below the cost of government ID-based age verification. Thus in, the near future, deploying an AI system for age verification is folly.
Unconstrained gaze estimation is the process of determining where a subject is directing their visual attention in uncontrolled environments. Gaze estimation systems are important for a myriad of tasks such as driver distraction monitoring, exam proctoring, accessibility features in modern software, etc. However, these systems face challenges in real-world scenarios, partially due to the low resolution of in-the-wild images and partially due to insufficient modeling of head-eye interactions in current state-of-the-art (SOTA) methods. This paper introduces DHECA-SuperGaze, a deep learning-based method that advances gaze prediction through super-resolution (SR) and a dual head-eye cross-attention (DHECA) module. Our dual-branch convolutional backbone processes eye and multiscale SR head images, while the proposed DHECA module enables bidirectional feature refinement between the extracted visual features through cross-attention mechanisms. Furthermore, we identified critical annotation errors in one of the most diverse and widely used gaze estimation datasets, Gaze360, and rectified the mislabeled data. Performance evaluation on Gaze360 and GFIE datasets demonstrates superior within-d
Online proctoring systems (OPS) are technologies and services that are used to monitor students during an online exam to deter cheating. However, OPS often violates student privacy by implementing overly intrusive surveillance to which students cannot consent meaningfully. The technologies used in OPS have been shown to unfairly flag students with disabilities. Our reflexive thematic analysis of interviews with students who have first-hand experience with online invigilated exams and who have disability accommodations points to their anxiety about the interaction between surveillance and their disabilities, leading to fears about misrepresentation and increased cognitive load on the exam. Students describe the compromises they need to make with their privacy and accommodations to take remote tests and share their privacy values. We present the implications for the design of OPS to mitigate the issues faced by disabled students.
We resolve the explicit bijection problem between symmetric plane partitions (SPPs) and quasi transpose complementary plane partitions (QTCPPs), introduced by Schreier-Aigner, who proved their equinumerosity. First, we relate this problem to Proctor's parallel equinumerosities for SPPs, even SPPs, staircase plane partitions, and parity staircase plane partitions, by constructing several bijections. As a result, we reduce the task to constructing a compatible bijection between even SPPs and staircase plane partitions. We then provide non-intersecting lattice path configurations for these objects, apply the LGV lemma, and transform the resulting path configurations. This process leads us to new combinatorial objects, $I_m$ and $J_m$, and the task is further reduced to constructing a compatible sijection (signed bijection) between $I_m$ and $J_m$, which is carried out in the final part of this paper. Our construction also answers the 35-year-old open problem posed by Proctor: constructing an explicit bijection between even SPPs and staircase plane partitions.
This article introduces the concept of \textit{authoritarian recursion} to theorize how AI systems consolidate institutional control across education, warfare, and digital discourse. It identifies a shared recursive architecture in which algorithms mediate judgment, obscure accountability, and constrain moral and epistemic agency. Grounded in critical discourse analysis and sociotechnical ethics, the paper examines how AI systems normalize hierarchy through abstraction and feedback. Case studies -- automated proctoring, autonomous weapons, and content recommendation -- are analyzed alongside cultural imaginaries such as Orwell's \textit{Nineteen Eighty-Four}, Skynet, and \textit{Black Mirror}, used as heuristic tools to surface ethical blind spots. The analysis integrates Fairness, Accountability, and Transparency (FAccT), relational ethics, and data justice to explore how predictive infrastructures enable moral outsourcing and epistemic closure. By reframing AI as a communicative and institutional infrastructure, the article calls for governance approaches that center democratic refusal, epistemic plurality, and structural accountability.
The spread of the Coronavirus disease-2019 epidemic has caused many courses and exams to be conducted online. The cheating behavior detection model in examination invigilation systems plays a pivotal role in guaranteeing the equality of long-distance examinations. However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze angle, body posture, and background information in the task of cheating behavior detection. In this paper, we develop and present CHEESE, a CHEating detection framework via multiplE inStancE learning. The framework consists of a label generator that implements weak supervision and a feature encoder to learn discriminative features. In addition, the framework combines body posture and background features extracted by 3D convolution with eye gaze, head posture and facial features captured by OpenFace 2.0. These features are fed into the spatio-temporal graph module by stitching to analyze the spatio-temporal changes in video clips to detect the cheating behaviors. Our experiments on three datasets, UCF-Crime, ShanghaiTech and Online Exam Proctoring (OEP), prove the effectiveness of our method
Online exams are more attractive after the Covid-19 pandemic. Furthermore, during recruitment, online exams are used. However, there are more cheating possibilities for online exams. Assigning a proctor for each exam increases cost. At this point, automatic proctor systems detect possible cheating status. This article proposes an end-to-end system and submodules to get better results for online proctoring. Object detection, face recognition, human voice detection, and segmentation are used in our system. Furthermore, our proposed model works on the PCs of users, meaning a client-based system. So, server cost is eliminated. As far as we know, it is the first time the client-based online proctoring system has been used for recruitment. Online exams are more attractive after the Covid-19 pandemic. Furthermore, during recruitment, online exams are used. However, there are more cheating possibilities for online exams. Assigning a proctor for each exam increases cost. At this point, automatic proctor systems detect possible cheating status. This article proposes an end-to-end system and submodules to get better results for online proctoring. Object detection, face recognition, human voic