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Journal articles on the topic 'Automated Proctoring'
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Atoum, Yousef, Liping Chen, Alex X. Liu, Stephen D. H. Hsu, and Xiaoming Liu. "Automated Online Exam Proctoring." IEEE Transactions on Multimedia 19, no. 7 (2017): 1609–24. http://dx.doi.org/10.1109/tmm.2017.2656064.
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Kadyrov, Bakhitzhan, Shirali Kadyrov, and Alfira Makhmutova. "Automated Reading Detection in an Online Exam." International Journal of Emerging Technologies in Learning (iJET) 17, no. 22 (2022): 4–19. http://dx.doi.org/10.3991/ijet.v17i22.33277.
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In this article, we study the deep learning-based reading detection problem in an online exam proctoring. Pandemia-related restrictions and lockdowns led many educational institutions to go to online learning environments. It brought the exam integrity challenge to an online test-taking process. While various commercial exam proctoring solutions were developed, the online proctoring challenge is far from being fully addressed. This article is devoted to making a contribution to the exam proctoring system by proposing an automated test-taker reading detection method. To this end, we obtain our own dataset of short video clips that resemble a real online examination environment and different video augmentation methods utilized to increase the training dataset. Two different deep learning techniques are adapted for training. The experiments show quite satisfactory results with model accuracy varying from 98.46% to 100%. The findings of the article can help educational institutions to improve their online exam proctoring solutions, especially in language speaking tests.
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Arnò, Simone, Alessandra Galassi, Marco Tommasi, Aristide Saggino, and Pierpaolo Vittorini. "State-of-the-Art of Commercial Proctoring Systems and Their Use in Academic Online Exams." International Journal of Distance Education Technologies 19, no. 2 (2021): 55–76. http://dx.doi.org/10.4018/ijdet.20210401.oa3.
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Online proctoring generally refers to the practice of proctors monitoring an exam over the internet, usually through a webcam. This technology has gained relevance during the current COVID-19 pandemic, given that the social distance owing to health reasons has consequently led to the switching of all learning and assessment activities to online platforms. This paper summarises the available state-of-the-art of commercial proctoring systems by identifying the main features, describing them, and analysing the way in which different proctoring programs are grouped on the basis of the services they offer. Furthermore, the paper reports on two case studies concerning online exams taken with both automated and human proctoring approaches. The outcomes from state-of-the-art approaches and the experience gained by the two case studies are then summarised in the conclusion, where the need for an organisational effort in loading photographs that can be used to easily recognise student faces, and using an automated online proctoring program to support manual proctoring have been suggested.
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Manoorkar, Sakshi. "AI-Powered Online Exam Proctoring System for Secure and Scalable Assessment." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50876.
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The rise of remote education has introduced new challenges in maintaining academic integrity during online examinations. Traditional methods of in-person or manual remote proctoring are either impractical or resource-intensive, leading to increased incidents of academic dishonesty. This project proposes an AI-powered online proctoring system capable of monitoring student behaviour in real time to ensure fair and secure assessment environments. The system integrates facial recognition, eye gaze tracking, voice activity detection, and browser tab monitoring to detect behaviours such as looking away, multiple face presence, or window switching. Built using MediaPipe FaceMesh, OpenCV, and TensorFlow, it provides real-time anomaly detection and alerting. A Python-based backend coordinates proctoring logic, while Streamlit and React.js power the examiner and student interfaces. Detected events are logged to Cloud Firestore and accompanied by automated screenshots, enabling scalable, post-exam review. This framework offers academic institutions a robust, scalable, and automated approach to uphold integrity in digital assessments. Key Words: Academic Integrity, AI-based Monitoring, Eye Gaze Tracking Online Proctoring.
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Breskina, A. A. "Development of an automated online proctoring system." Informatics and mathematical methods in simulation 13, no. 1-2 (2023): 180–89. http://dx.doi.org/10.15276/imms.v13.no1-2.180.
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Breskina, Anastasiia A. "Development of an automated online proctoring system." Herald of Advanced Information Technology 6, no. 2 (2023): 163–73. http://dx.doi.org/10.15276/hait.06.2023.11.
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The rapid development of machine learning technologies, the increasing availability of devices and widespread access to the Internet have significantly contributed to the growth of distance learning. Alongside distance learning systems, proctoring systems have emerged to assess student performance by simulating the work of a teacher. However, despite the development of image processing and machine learning technologies, modern proctoring systems still have limited functionality: some systems have not implemented computer vision methods and algorithms satisfactorily enough (false positives when working with students of different ancestry, racial background and nationalities) and classification of student actions (very strict requirements for student behaviour), so that some software products have even refused to use modules that use elements of artificial intelligence. It is also a problem that current systems are mainly focused on tracking students' faces and gaze and do not track their postures, actions, andemotional state. However, it is the assessment of actions and emotional state that is crucial not only for the learning process itself, but also for the well-being of students, as they spend long periods of time at computers or other devices during distance learning, which has a great impact on both their physical health and stress levels. Currently, control over these indicators lies solely with teachers oreven students themselves, who have to work through test materials and independent work on their own. An additional problem is the quality of processing and storage of students' personal data, as most systems require students to be identified using their identitydocuments and store full, unanonymised video of students' work on their servers. Based on the analysis of all these problems that impede the learning process and potentially affectstudents' health in the long run, this article presents additional functional requirements for modern automated online proctoring systems, including the need to analyse human actions to assess physical activity and monitor hygiene practices when using computers in the learning process, as well as requirements for maximum protection of students' personal data. A prototype of the main components of an automated online proctoring system that meets the proposed requirements has been developed
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Dr. N, Suguna, Sowkarthika Prof P., and Saranhariharajeyan L. "Automated Placement Coordinator System with Automated Proctoring Assessment using Python Django." IARJSET 8, no. 5 (2021): 455–60. http://dx.doi.org/10.17148/iarjset.2021.8580.
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Cele, Siyanda, and Mncedisi Christian Maphalala. "Examining Social Justice Implications of Proctoring Technologies in Online Assessments within Open and Distance e-Learning (ODeL) Environments: Privacy, Equity, and Access." International Journal of Educational Innovation and Research 4, no. 1 (2025): 125–43. https://doi.org/10.31949/ijeir.v4i1.12773.
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The study explores the social justice implications of proctoring technologies in online assessments within Open and Distance e-learning (ODeL) environments, focusing on privacy, equity, and access. Through a systematic literature review (SLR) adhering to PRISMA protocols, the study analyses peer-reviewed empirical research published between 2014 and 2024. Key thematic areas identified include the ethical concerns surrounding privacy and surveillance, the impact of proctoring technologies on equitable access to assessments, and the potential biases embedded within automated monitoring systems. The study highlights the need for fair and transparent strategies for online proctoring in Open and Distance e-Learning (ODeL) environments. It is essential to balance maintaining academic integrity and respecting students' privacy and ethical concerns. Proctoring technologies should align with Social Justice Theory to safeguard students' rights to privacy, equal access, and active participation. Future studies should focus on developing policies and technologies that enhance fairness and trust while protecting students' rights during online assessments.
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BHVSP, Subrahmanyam, and Chandrasekhar Balabhadrapatruni. "AI based Online Proctoring Remote Monitoring Intruder, Emotion Detection and Distance Estimation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 08 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem36926.
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AI based online learning has undeniably surged in popularity over recent years. The COVID-19 pandemic has further accelerated the transition to online education, heightening the need for secure methods to authenticate and proctor online students. Today, a range of technologies offers varying levels of automation. In this paper, we present a comprehensive analysis of a specific solution that integrates multiple automated authentication technologies with an automatic proctoring system. The parameters that we used to achieve our goal are face detection, eye gaze tracking, multiple person detection, emotion detection, distance estimation and background noise detection. All these components help to maintain the exam integrity and to mitigate the existing limitation of e-exam proctoring software’s. Keywords— AI based online learning, Exam remote Monitoring integrity, Gaze tracking, Intruder, distance estimation.
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Ishrath Unnisa, Sadiya Baseer, and Dr. Mohammed Abdul Bari. "Online Examination And Proctoring System." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 67–76. https://doi.org/10.62647/ijitce2025v13i2spp67-76.
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The increasing demand for remote education and online assessments has necessitated the development of secure and reliable exam monitoring systems. This project presents an Online Examination And Proctoring System that ensures academic integrity by monitoring candidates in real-time through webcam and screen activity. Utilizing advanced technologies such as facial recognition, object detection, and behavior analysis, the system identifies and flags suspicious activities including presence of multiple faces, unauthorized device usage, and leaving the test environment. It offers a scalable and automated solution that reduces the need for human invigilators, thus enhancing accessibility and efficiency in digital examinations. The system is developed using Python, OpenCV, and machine learning models, and integrates seamlessly with popular learning management platforms.
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Golovanov, A. L. "Development of an automated proctoring system for Google forms." Mathematical structures and modeling, no. 4 (2022): 100–111. http://dx.doi.org/10.24147/2222-8772.2022.4.100-111.
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The paper presents a description of software modules developed by the author to create a system of automated proctoring for online testing. The analysis of the data set created with the help of the developed programs and the description of the methods used to automatically determine the dishonesty of testing is given.
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Abzalov, A. R., A. V. Zhiganov, and R. R. Samigullina. "User identification through keystroke dynamics as part of automated proctoring systems." National Interests: Priorities and Security 16, no. 3 (2020): 582–96. http://dx.doi.org/10.24891/ni.16.3.582.
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Subject. Popular online courses and testing programs integrate into correspondence education systems, which are more often than not based on automated proctoring. What makes the latter vulnerable is user identification. Objectives. We examine user identification methods through keystroke dynamics and devise a more accurate and effective technique for user identification through keystroke dynamics. Methods. The article sets out a three-tiered model for identifying users more accurately not only in automated proctoring environments, but also in critically sensitive locations. Results. We had an experiment, which showed a 97.5 percent accuracy of user identification. We significantly reduced illegitimate users at the statistical level of the three-tiered model. Conclusions and Relevance. Following the study, it is possible to develop a logic comparison method for higher accuracy. It will serve for creating a more refined model, which would accommodate for distinctions of each user and some deviations of users’ emotions. This would contribute to continuous user identification systems to monitor their emotional condition at critical locations.
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Ankita, Vijay Dhamal, Rohidas Parkhande Priyanka, Shankar Dighe Vinayak, Ramdas Gaikwad Rushikesh, and Ms.K.S Khamkar Prof. "Online Assessment & Proctoring for Candidate Recruitment at Workplace." Advanced Innovations in Computer Programming Languages 4, no. 1 (2022): 1–7. https://doi.org/10.5281/zenodo.6635246.
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In the present world, human proctoring is the most important approach to evaluation, by either providing the supervisor to visit an examination center or supervising them observably and acoustically while exam using a webcam however such methods are manual and costly. So, we aim to develop an automated proctoring system to detect an extensive variety of cheating behavior during an online assessment exam. The system requirement includes a working webcam and mic. This system includes four features. User verification, eye-tracking, head-pose- estimation, and person and object detection. Person and object detection using the yolo algorithm and eye-tracking using blob algorithm and cascade classification. To develop our purpose system, we collect required data from 10 subjects who also performed Various types of cheating while examination/assessment. Vast experimental outcomes indicate our proctoring system's accuracy, validity, and proficiency. By using this system for recruitment, we can increase the chances of getting deserving candidates to the organization candidates.
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Akshay Vinod Patil, Payal Atul Chavan, Shruti Rangnath Jadhav, Atharva Umesh Phodkar, Mayuresh Bhagwat Gulame, and Aarti Paresh Pimpalkar. "Online exam proctoring application using-AI." International Journal of Science and Research Archive 15, no. 2 (2025): 1228–34. https://doi.org/10.30574/ijsra.2025.15.2.1440.
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In the evolving landscape of online education, ensuring the integrity of remote examinations has become a critical challenge. This paper presents the development of an “AI-powered Online Exam Proctoring Application” designed to detect and prevent cheating during online assessments. The proposed system leverages computer vision techniques using OpenCV and Media Pipe to monitor students' behavior in real-time by tracking both eye movements and hand gestures. If a student looks away from the screen or their hands move out of the camera frame, both potential indicators of malpractice, the system automatically captures a screenshot as evidence. These captured incidents are logged and made available to the invigilators for review, thereby supporting teachers in identifying and addressing instances of academic dishonesty. By combining automated monitoring with intelligent violation detection, the system offers a scalable and effective solution to maintain fairness and credibility in online examinations
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Akber,, Nawal, Neha N,, Rithinraj PK, Shinas S, and Swathy CS. "Online Exam Proctoring System Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43097.
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A web-based exam monitoring system enhances the integrity and fairness of online examinations by simulating the role of an invigilator through advanced AI technologies, ensuring secure, fair, and regulated conditions. In a virtual setting where direct supervision is not possible, this system serves as an automated proctor, detecting and flagging suspicious behavior that may indicate potential cheating. One of its key features is Face Detection and Recognition, which verifies the student’s identity to eliminate the risk of impersonation. Additionally, People Counting enhances security by scanning the environment for additional faces, preventing unauthorized assistance. The system also employs Head and Eye Tracking, which monitors the student’s gaze and head movements to ensure their attention remains on the exam screen. Any prolonged distraction is logged and flagged for review. To counter fraudulent attempts, Face Spoofing Detection uses liveness detection techniques to differentiate between a real person and fake representations like photographs, videos, or 3D models, ensuring only authentic users take the exam. Furthermore, Object Recognition identifies and flags unauthorized items such as mobile phones, calculators, and notes, which are typically restricted in exam settings. By integrating object detection algorithms, the system can automatically recognize such items, alert proctors, and even pause the exam if necessary. Together, these features create a comprehensive, AI-driven monitoring solution that closely mimics in-person invigilation, making online exams more secure and fair. This application not only upholds academic integrity but also ensures students are evaluated purely on their knowledge and efforts, providing a virtual invigilation process that is reliable, effective, and as close as possible to traditional exam supervision. Keywords: Web-based exam monitoring, virtual invigilator, AI-powered proctoring, Face Detection, Face Recognition, People Counting, Head Tracking, Eye Tracking, Face Spoofing Detection, Object Recognition, online exam security, automated proctoring, academic integrity, liveness detection, fraud prevention, cheating detection, biometric verification, digital invigilation.
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Nugroho, Eko Cahyo. "A Horizontally Scalable WebSocket Architecture for Cost-Effective Online Examination Proctoring System on AWS Cloud Infrastructure." Engineering, MAthematics and Computer Science Journal (EMACS) 7, no. 1 (2025): 115–26. https://doi.org/10.21512/emacsjournal.v7i1.12770.
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In this research work we present the cost-effective prototype of a WebSocket server with a horizontal scaling feature on AWS Cloud Service. AWS API Gateway for establishing WebSocket connections also works but is exceedingly expensive for schools. The solution presented in this study proposes an on-premise WebSocket server deployed at AWS EC2 instances. The server utilizes Node. js's cluster module to make the most out of the CPU's cores and has also implemented a Redis pub/sub mechanism to easily horizontal scale it to many EC2 instances. The system architecture utilizes DynamoDB to store students' proctoring status recorded on the first attempt at the quiz. Then, the real status update is delivered by WebSocket message. The implementation shows effective real-time monitoring capabilities for online examinations, including student activity tracking, automated disconnection detection, and proctor-student interaction features. The results show improved cost efficiency compared to API Gateway as the WebSocket server. This solution provides schools with a cost-effective and reliable proctoring feature in LMS for implementing online examination proctoring systems at scale.
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Shkodzinsky, Oleh, and Mykhailo Lutskiv. "Automated ai-based proctoring for online testing in e-learning system." Scientific journal of the Ternopil national technical university 107, no. 3 (2022): 76–85. http://dx.doi.org/10.33108/visnyk_tntu2022.03.076.
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Based on the analysis of existing on the market algorithmic solutions for identity verification during knowledge control in electronic learning systems, the requirements for the target system were formed. The main algorithms and approaches to the detection and recognition of faces were considered, as a result of which an effective combination of algorithms was chosen. The system of photo fixation and identity verification during knowledge control in LMS ATutor was designed and implemented. Its effectiveness was verified on the basis of a sample of test passes during its work in the real conditions of the educational process. Conclusions were made regarding the feasibility of implementation.
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Gloriya, Antony, Zameen Aflah, K. Jesus Jeswin, Venugopal Navya, and Mathew Alpha. "Recruit Mate: An automated Interview Bot." Research and Reviews: Advancement in Robotics 6, no. 3 (2024): 1–3. https://doi.org/10.5281/zenodo.13120964.
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<em>Resume screening and interview bots have become essential in modern hiring processes, where a large volume of candidates apply for job posts. Manual sorting of resumes to match job requirements is a tedious task. The motivation behind our project is to automate the recruitment process, alleviating this challenge. Our objective is to develop an interview bot capable of automating hiring for specific job postings. Through prompt questions, the bot assists in profile creation and resume generation. Additionally, it recommends jobs based on candidate skills, enabling participation in multiple-choice questions and robotic interviews. Computer vision technology is employed for proctoring during interviews, ensuring exam integrity by detecting candidate faces. The interview bot assesses candidate performance and generates final scores. By addressing challenges like manual resume sorting, our project aims to streamline and enhance recruitment efficiency.</em>
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Breskina, А., and S. Antoshchuk. "EMOSTUDENT: A DATASET FOR COMPLEX STUDENT BEHAVIOUR EVALUATION." Odes’kyi Politechnichnyi Universytet Pratsi 1, no. 67 (2023): 54–59. http://dx.doi.org/10.15276/opu.1.67.2023.07.
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This article discusses artificial intelligence based automated online proctoring systems. The practical implementation of the task of analysing students’ behaviour in the process of working with these systems and the datasets used to solve this task were considered. A general model for processing data on human activity in the process of online learning has been created, which is aimed at analysing and describing the activity and emotional state of the student. For this purpose, various features that affect the assessment of student behaviour during independent work on test tasks or exams were identified. Based on the analysis of existing datasets and the problems of modern implementations of automated online proctoring systems, a classification of features used specifically for analysing video sequences in the context of solving the problem of analysing student behaviour was made. In accordance with the developed requirements, a dataset was proposed. The data source for the developed dataset was the YouTube platform: videos with a Creative Commons licence were used. Amazon SageMaker platform was utilized to organise the process of data labelling and dataset formation. The generated dataset was added to the Kaggle and Hugging Face platforms. This allows us to utilized the work among other scientists and software developers and to test the developed dataset in practice in training of various implemented models of artificial neural networks.
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Bhargavi, J., R. Naveena, G. Gowtham Raju, and D. Ganesh. "AI Virtual Classroom Assistance Using Zoom." International Scientific Journal of Engineering and Management 04, no. 01 (2025): 1–6. https://doi.org/10.55041/isjem02232.
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This work investigates developing an AI-powered virtual classroom assistant integrated with Zoom. The system enables users to input a topic for a class and select from four AI voice lecturers: Indian male, Indian female, UK male, and UK female. Users can schedule the class once the voice is selected, which generates a link directing them to the official Zoom website. During the 15-minute session, the AI lecturer presents the topic. Post-class, the system automatically generates a PowerPoint presentation on the chosen topic and conducts a quiz to evaluate students. Students scoring low are provided with simpler quizzes. It will generate the results also. The quiz is conducted under proctoring to ensure integrity. A chatbot feature allows students to ask questions and resolve doubts. The implementation leverages Python with Flask, Selenium, and libraries like PyAutoGUI, Pyperclip, and threading for seamless functionality and interaction. . Keywords: AI virtual classroom, Zoom integration, voice assistants, automated PowerPoint generation, AI lecturer, proctored quiz, chatbot, Python Flask, Selenium, interactive learning.
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Romdani, Romdani, and Adiyono Adiyono. "How is Artificial Intelligence (AI) Changing the Future of Computer-Based Testing (CBT)?" Universal Education Journal of Teaching and Learning 2, no. 2 (2025): 76–85. https://doi.org/10.63081/uejtl.v2i2.48.
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This study examines the transformative impact of Artificial Intelligence (AI) on Computer-Based Testing (CBT) through a systematic literature review (SLR) following the PRISMA 2020 protocol. The research identifies key opportunities, including automated grading (reducing instructor workload by 70%) and adaptive testing (enhancing personalized assessments), alongside critical challenges such as algorithmic bias (particularly in speech recognition systems) and privacy concerns in AI-based proctoring. Analysis of 95 peer-reviewed studies (2015-2024) reveals a significant post-2020 surge in research, driven by digital education demands during the pandemic, with current trends focusing on Generative AI integration (25% of studies) and bias mitigation (35%). The findings highlight the need for ethical and equitable development of AI-enhanced CBT systems that prioritize both technological innovation and ethical considerations, particularly regarding fairness, transparency, and data protection. The study concludes with recommendations for future research directions, including the development of Explainable AI (XAI) frameworks and inclusive assessment models. These insights provide valuable guidance for educators, policymakers, and technology developers working to optimize AI applications in educational assessment.
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Saba, Tanzila, Amjad Rehman, Nor Shahida Mohd Jamail, Souad Larabi Marie-Sainte, Mudassar Raza, and Muhammad Sharif. "Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach." IEEE Access 9 (2021): 47639–56. http://dx.doi.org/10.1109/access.2021.3068223.
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Tjahyaningtijas, Hapsari Peni Agustin, Nanang Husin, Hasanuddin Al Habib, et al. "Machine learning on academic education: Bibliometric studies." E3S Web of Conferences 450 (2023): 02010. http://dx.doi.org/10.1051/e3sconf/202345002010.
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The use of Machine Learning exhibits significant promise in facilitating advancements in the field of education. It is vital to conduct a comprehensive review of existing research to ascertain the significance of utilizing Machine Learning as a viable approach to enhance educational advancements. This bibliometric analysis provides a comprehensive overview of the advancements in the application of machine learning techniques within the field of education. This study utilizes publication and citation data from many academic literature sources to elucidate prominent patterns, areas of research emphasis, and scholarly collaborations within this field. The findings of the bibliometric analysis reveal a significant increase in scholarly attention toward the application of machine learning in the field of education during the past several years. The scope of these investigations encompasses a diverse array of subjects, such as personalized learning, predictive analytics, automated evaluation, learning recommendations, and online exam proctoring. The findings of this study also demonstrate a notable rise in the level of collaboration among scholars from many fields, highlighting the significance of interdisciplinary approaches in tackling the intricate challenges associated with the integration of machine learning in education.
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Patil, Rhutuja. "Smart Interview Coach: Voice-Based HR and Technical Chatbot." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04055.
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Abstract—The growth of internet-based learning platforms has revolutionized the education system, making E-learning more accessible and widespread. However, with this shift toward digital education, maintaining academic integrity—especially during online examinations—has become a major concern. Traditional approaches to preventing cheating often fall short in virtual environments where physical invigilation is absent. Students appearing for online exams from remote locations are typically not monitored in real time, increasing the likelihood of unfair practices. To counter this, many educational institutions continue to conduct in-person exams, which goes against the core principle of flexible, remote learning. This project aims to address these challenges by developing an AI-driven system that ensures secure and fair online assessments. By incorporating multiple face recognition, object detection, emotion analysis, and voice-driven interview evaluations, the system identifies malpractice and provides a realistic simulation of technical and HR interviews. This automated approach not only strengthens exam monitoring but also enhances the overall credibility of E-learning platforms. Index Terms—E-learning, Online Examination, Academic Integrity, Malpractice Detection, Face Recognition, Object Detection, Emotion Analysis, AI Interview Chatbot, Remote Proctoring, Real-time Monitoring.
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Harriet Akudo, Agbarakwe,, and Chibueze, Ozioma Ogbonna. "Leveraging Artificial Intelligence for Enhanced Assessment and Feedback Mechanisms in Nigeria Higher Education System." International Journal of Research and Innovation in Social Science VIII, no. IX (2024): 142–51. http://dx.doi.org/10.47772/ijriss.2024.809012.
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This paper explores the inherent potentials of Artificial Intelligence (AI) in enhancing assessment and feedback mechanisms within Nigeria’s higher education system. The traditional assessment methods in Nigerian institutions often suffer from issues like inconsistent grading, delayed feedback, and significant administrative burdens on educators, which impede timely student interventions. AI technologies, with their capabilities in automation, data processing, and pattern recognition, offer solutions by enabling efficient, equitable, and personalized assessment systems. Automated grading, intelligent tutoring systems, and adaptive learning platforms are among the AI tools discussed, highlighting their role in streamlining grading processes, providing tailored learning experiences, and ensuring academic integrity through AI-powered proctoring. Key research and reports, such as those by Eli-Chukwu et al., Baker & Smith, Popenici & Kerr, Holmes et al., and Seldon & Abidoye, were cited to provide a thorough knowledge of AI’s influence in today’s educational system. Despite these benefits, the paper also addresses significant challenges, including infrastructure deficits, high implementation costs, digital literacy gaps, and ethical concerns such as data privacy and algorithmic bias. The study advocates for strategic investments in educational technology, professional development, and the establishment of ethical and regulatory frameworks to mitigate these challenges. By prioritizing AI integration, Nigeria can enhance educational quality, promote inclusivity, and align with global trends in higher education innovation.
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Malinich, I. P., and Y. V. Ivanchuk. "Features of microservices placement of learning management systems in hybrid clouds." System technologies 3, no. 158 (2025): 157–70. https://doi.org/10.34185/1562-9945-3-158-2025-16.
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The article focuses on various approaches and methods for deploying microservices in the cloud, as well as the specifics of their application for hosting microservices in a hybrid cloud environment for learning management systems. The relevance of the study consists in the problem that migrating high-load learning management systems (LMS) with numerous integration links to other services to the cloud is a complex task that is difficult to be solved using existing methods. LMS’es such as Moodle, Canvas LMS, Forma LMS, and others are often used to organize the educational process in Ukrainian educational institutions. One of the most popular LMS in Ukraine is Moodle, which is used by many higher education institu-tions and other educational establishments. Many LMS have extensive integration with other resources of the educational institution, such as library resources, software development tools, or even proctoring systems. The overall server information system of educational insti-tutions can be distributed across different servers or clouds and proper solutions for this pro-cess should be found. The aim of study is developing a solution for organizing data flows in the process of deploying microservices using various methods including the features of educa-tional information systems. Using the structural analysis method, a data flow solution for the automated deployment of microservices in learning management systems within a hybrid cloud environment created. Also various methods for deploying microservices in the cloud were examined, both practical and scientific, comparing their positive and negative effects. Key metrics for assessing network availability for microservice deployment have been identi-fied. The proposed data flow solution for the automated deployment of microservices in a hy-brid cloud provides ways to improve microservice distribution methods giving users sufficient control over the process including features of learning management systems.
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PYLYPENKO, D., and O. KOVALENKO. "Modern Methods and Tools for Testing Web-Oriented Systems." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 1, no. 40 (2025): 111–18. https://doi.org/10.31474/1996-1588-2025-1-40-111-118.
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"Based on the analysis of methods and tools for testing web-oriented systems, recommendations for their use are formed. The specifics of such systems and the relationship between test cases and functional and non-functional requirements for web systems in various subject areas are considered. The performed analysis confirms the relevance of developing analytical methodologies for the justified selection of the implementation of testing processes. The authors propose the development of special software support for the formation of a strategic testing plan and its implementation for web-oriented systems using modern methods and tools. Traditional QA methods were formed using a defined structure and systematic processes. The very first was, and continues to develop, manual testing. This method requires a carefully developed scenario and documentation requirements, is laborintensive, and depends on the human factor. However, it meets the requirements of exploratory testing and various situational scenarios with an emotional component. Automated testing is effectively used for repetitive tasks, when checking functionality, security, and performance. The most appropriate use of this type of testing is in large-scale projects. This is due to the need for maintenance of test scenarios and the necessity of investing in tools and infrastructure software tools for automatic execution of test cases. This method involves writing scripts that check various aspects of software, including functionality, performance and security. Automated testing is particularly useful for repetitive tasks and large-scale applications. The balance of using manual and automated methods is the best solution for forming a strategic testing plan and its practical implementation. The active development of technologies and platforms for managing testing processes allows us to assert that existing methods and tools for hybrid testing are the most in demand. The research goal is to analyze modern methods and tools for testing weboriented systems to form a strategy for developing a balanced testing system. A balanced testing system is defined indicators, processes, forms, and testing methods that best meet the defined criteria of the software product. According to the results of the research, a strategic testing plan for the ""Electronic University"" system will be formed. The optimal approach to testing web systems is hybrid. Analyzing the project, defining its features, requirements, and risks allows for the creation of a list of manual and automated tests, forming a testing plan and its implementation. Testing educational web systems has its own specific features that distinguish it from testing ordinary web applications. These features are related to their target audience, functionality, data importance, and security requirements. Testing educational web systems requires careful planning, a deep understanding of the specifics of the educational process, and attention to details such as gamification, knowledge assessment, proctoring, and communication, to ensure a high-quality, secure, and effective learning platform for all users. Recommendations for the ""Electronic University of VNTU"" system serve as a basis for a project to determine the maturity, sociotechnicality, and quality indicators of the system, which will allow for the systematic formation of indicators and their visualization using special tools."
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Researcher. "DEEP LEARNING FOR AUTOMATICALLY DETECTING CHEATING IN ONLINE EXAMS." International Journal of Information Technology Research and Development (IJITRD) 4, no. 2 (2023): 17–25. https://doi.org/10.5281/zenodo.15267916.
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<em>Since the quick move towards online education, strict concerns over academic integrity have been heightened to such an extent, especially in terms of cheating during remote exams. The purpose of this research is to establish a robust and a deep learning based approach for detecting cheating behaviors during online exams, which can ensure fairness and reliability of remote assessments. Using the latest developments in convolutional neural networks and long short term memory networks, our method seamlessly fuse the data analysis of multiple modalities, web cam feed of facial expressions, eye movements, and head pose to pinpoint the suspicious behaviors of cheating.</em> <em>We trained and evaluated our proposed deep learning model using over 500 exam session videos including our own dataset with more than 500, both normal and cheating scenarios. It is found out that our hybrid CNN-LSTM model attains a high (92.5%) accuracy and performs better in comparison to traditional machine learning techniques. Integration of temporal dynamics through LSTMs effectively improves results compared to other biRNN variants, implying that behavior over time provides a more meaningful facet of behavior for the detection of slight cheating actions.</em> <em>Emerging technologies that support monitoring online assessments are multimodal deep learning and real-time behavior analytics, which are stressed to be essential. In addition, this work furthers the field of automated proctoring solutions by creating scalable methods of identifying incidents in an unbiased and less human error prone and subjective way. The proposed model has significant potential for being used by the educational institutions to solve practical problems related to academic standards and academic integrity in virtual environments.</em>
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Kosorukov, Artem Andreevich. "Artificial Intelligence Platforms in Education." Социодинамика, no. 3 (March 2025): 40–60. https://doi.org/10.25136/2409-7144.2025.3.73766.
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Modern artificial intelligence (AI) platforms have a significant impact on education, they are becoming a full-fledged professional activity tool capable of optimizing learning processes and educational administration. The introduction of AI in the field of education is aimed at improving efficiency, personalizing approaches and automating routine tasks. The subject of this study is the use of AI platforms in education, their impact on the quality of services provided and the effectiveness of educational processes in the context of platform integration. In the educational field, AI platforms are being considered, including adaptive learning platforms Knewton, DreamBox Learning, Civitas Learning, IBM Watson Education, proctoring platforms ProctorU, ExamSoft, Turnitin writing quality control platforms, Grammarly, Edsight and Automated Essay Scoring creative work assessment platforms. As part of the research, data from an online survey of Russian experts representing universities from 8 federal districts and having experience working with these AI platforms is being processed. A comparative analysis method is used that identifies common and distinctive features of AI platforms based on special criteria, the integral assessment of which underlies the ranking of platforms. The scientific novelty of this study lies in a comprehensive analysis of the use of AI platforms in such a socially significant field as education. Unlike the systemic approaches of S.M. Kashchuk or B. Omodan, the study covers special issues of automated decision-making and evaluation of its effectiveness in real conditions. An important contribution of this study is the analysis of the mechanisms of AI adaptation to the individual needs of users, which is a key factor in the successful platform integration of these technologies. An expert survey based on the analysis of such special criteria as adaptability, interactivity, functionality, efficiency, accessibility, integration and innovation on a scale of "low -moderate – medium – high" allows for an integrated multi-criteria assessment of platforms based on the totality of all criteria, to build a platform rating, to identify the most promising AI platforms (in terms of interactivity and innovation – DreamBox Learning, in terms of adaptability and functionality – Knewton), as well as identify ways to overcome their limitations.
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Ferdosi, B. J., M. Rahman, A. M. Sakib, and T. Helaly. "Modeling and Classification of the Behavioral Patterns of Students Participating in Online Examination." Human Behavior and Emerging Technologies 2023 (December 30, 2023): 1–19. http://dx.doi.org/10.1155/2023/2613802.
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Online education has become an essential part of the modern education system, but keeping the integrity of the online examination remains a challenge. A significant increase in cheating in online examinations (from 29.9% before COVID-19 to 54.7% during COVID-19, as per a recent survey) points out the necessity of online exam proctoring systems. Traditionally, educational institutes utilize different questions in onsite exams: multiple-choice questions (MCQs), analytical questions, descriptive questions, etc. For online exams, form-based exams using MCQs are popular though in disciplines like math, engineering, architecture, art, or other courses, paper and pen tests are typical for proper assessment. In form-based exams, students’ attention is toward display devices, and cheating behavior is identified as the deviation of head and eye gaze direction from the display device. In paper- and pen-based exams, students’ main attention is on the answer script not on the device. Identifying cheating behavior in such exams is not a trivial task since complex body movements need to be observed to identify cheating. Previous research works focused on the deviation of the head and eyes from the screen which is more suited for form-based exams. Most of them are very resource-intensive; along with a webcam, they require additional hardware such as sensors, microphones, and security cameras. In this work, we propose an automated proctoring solution for paper- and pen-based online exams considering specific requirements of pen-and-paper exams. Our approach tracks head and eye orientations and lip movements in each frame and defines the movement as the change of orientation. We relate cheating with frequent coordinated movements of the head, eyes, and lips. We calculate a cheating score indicative of the frequency of movements. A case is marked as a cheating case if the cheating score is higher than the proctor-defined threshold (which may vary depending on the specific requirement of the discipline). The proposed system has five major parts: (1) identification and coordinate extraction of selected facial landmarks using MediaPipe; (2) orientation classification of the head, eye, and lips with K-NN classifier, based on the landmarks; (3) identification of abnormal movements; (4) calculation of a cheating score based on abnormal movement patterns; and (5) a visual representation of students’ behavior to support the proctor for early intervention. Our system is robust since it observes the pattern of movement over a sequence of frames and considers the coordinated movement pattern of the head, eye, and lips rather than considering a single deviation as a cheating behavior which will minimize the false positive cases. Visualization of the student behavior is another strength of our system that enables the human proctor to take preventive measures rather than punishing the student for the final cheating score. We collected video data with the help of 16 student volunteers from the authors’ university who participated in the two well-instructed mock exams: one with cheating and another without cheating. We achieved 100% accuracy in detecting noncheating cases and 87.5% accuracy for cheating cases when the threshold was set to 40.
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Tweissi, Adiy, Wael Al Etaiwi, and Dalia Al Eisawi. "The Accuracy of AI-Based Automatic Proctoring in Online Exams." Electronic Journal of e-Learning 20, no. 4 (2022): 419–35. http://dx.doi.org/10.34190/ejel.20.4.2600.
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This study technically analyses one of the online exam supervision technologies, namely the Artificial Intelligence-based Auto Proctoring (AiAP). This technology has been heavily presented to the academic sectors around the globe. Proctoring technologies are developed to provide oversight and analysis of students’ behavior in online exams using AI, and sometimes with the supervision of human proctors to maintain academic integrity in a blended format. Manual Testing methodology was used to do a software testing on AiAP for verification of any possible incorrect red flags or detections. The study took place in a Middle Eastern university by conducting online exams for 14 different courses, with a total of 244 students. Afterward, five human proctors were assigned to verify the data obtained by the AiAP software. The results were then compared in terms of monitoring measurements: screen violation, sound of speech, different faces, multiple faces, and eyes movement detection. The proctoring decision was computed by averaging all monitoring measurements and then compared between the human proctors’ and the AiAP decisions, to ultimately set the AiAP against a benchmark (human proctoring) and hence to be viable for use. The decision represented the number of violations to the exam conditions, and the result showed a significant difference between Human Decision (average 25.95%) and AiAP Decision (average 35.61%), and the total number of incorrect decisions made by AiAP was 74 out of 244 exam attempts, concluding that AiAP needed some improvements and updates to meet the human level. The researchers provided some technical limitations, privacy concerns, and recommendations to carefully review before deploying and governing such proctoring technologies at institutional level. This paper contributes to the field of educational technology by providing an evidence-based accuracy test on an automatic proctoring software, and the results demand institutional provision to better establish an appropriate online exam experience for higher educational institutions.
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Shaik Abdulla Mumtaz, Mohammed Aamir Khan, Razi Hassan, and Dr. Ijteba Sultana. "HireProMax: An AI-Powered Recruitment and Assessment Platform." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 152–61. https://doi.org/10.62647/ijitce2025v13i2spp152-161.
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The contemporary landscape of human resource management faces significant challenges in talent acquisition, characterized by time-consuming manual processes, inherent biases, and a lack of standardized assessment methodologies. Traditional recruitment often struggles with crafting precise job descriptions, developing relevant and effective assessment questions, and generating comprehensive candidate evaluations, leading to inefficiencies and suboptimal hiring decisions. To address these critical limitations, this project introduces HireProMax, an innovative AI-powered recruitment and assessment platform meticulously designed to revolutionize and streamline the entire hiring ecosystem for organizations, concurrently fostering a highly engaging and transparent experience for job candidates. HireProMax leverages advanced artificial intelligence and machine learning algorithms to automate and enhance several pivotal stages of the recruitment funnel. A cornerstone of the platform is its intelligent job description generation module, which utilizes natural language processing (NLP) to dynamically create professional, compelling, and highly precise job advertisements tailored to specific roles and organizational needs. This capability ensures clarity and accuracy, attracting a more relevant pool of applicants from the outset. Furthermore, the platform integrates an AI-driven assessment engine capable of formulating bespoke and challenging assessment questions across various domains, designed to rigorously evaluate candidates' skills, knowledge, and aptitudes pertinent to the targeted position. This adaptive questioning mechanism ensures that assessments are fair, relevant, and effectively identify top-tier talent. Central to HireProMax functionality its robust and secure online testing environment. This feature facilitates proctored, virtually supervised examinations, ensuring the integrity and authenticity of candidate responses, thereby mitigating concerns of cheating and maintaining the credibility of the assessment results. Following the completion of these assessments, the platform's analytical capabilities come to the forefront, generating detailed and insightful candidate reports. These comprehensive reports provide recruiters with objective data on candidate performance, highlighting strengths, identifying areas for development, and offering comparative analyses, thus empowering informed decision-making. The reports are designed to be intuitive, presenting complex data in an easily digestible format, significantly reducing the manual effort involved in candidate evaluation. By automating repetitive tasks, standardizing assessment procedures, and providing data-driven insights, HireProMax promises to dramatically reduce the time-to-hire, lower recruitment costs, and enhance the overall quality of talent acquisition. For candidates, the platform offers a seamless, transparent, and fair assessment journey, promoting a positive impression of the hiring organization. Ultimately, HireProMax represents a significant leap forward in recruitment technology, poised to transform how organizations identify, evaluate, and secure the best human capital in an increasingly competitive global market. Algorithms: Job Description Generator Assessment Question Engine Secure Online Testing (AI Proctoring) Automated Evaluation Candidate Reporting
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Куцак, Лариса Вікторівна. "ШТУЧНИЙ ІНТЕЛЕКТ У СУЧАСНІЙ ОСВІТІ: ПЕРСПЕКТИВИ ЗАСТОСУВАННЯ ТА ВИКЛИКИ". Modern Information Technologies and Innovation Methodologies of Education in Professional Training Methodology Theory Experience Problems, № 74 (11 лютого 2025): 27–37. https://doi.org/10.31652/2412-1142-2024-74-27-37.
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The article examines the prospects for the application of artificial intelligence (AI) technologies in modern education, identifies the main directions of their implementation, key advantages and challenges. It is noted that the implementation of AI is an important component of the digital transformation of education, which involves the personalization of the educational process, automation of routine tasks of teachers, and expanding access to high-quality educational services. Particular attention is paid to the problems of digital inequality, insufficient training of teachers, as well as the lack of a clear regulatory framework for regulating the use of AI in the educational environment. The paper analyzes the experience of integrating AI into the educational process in Ukraine and abroad, in particular successful examples of the use of adaptive learning systems, automated assessment, intelligent assistants and proctoring systems. It is established that personalized learning contributes to taking into account the individual needs of students, but requires an ethical approach to the protection of personal data. Automation of assessment allows for transparency and efficiency of feedback, but has limitations in creative tasks. Particular attention is paid to the ethical risks associated with the use of AI, in particular in the area of data confidentiality, as well as the importance of developing teacher training programs for the effective use of technology. The creation of a national strategy for integrating AI into education, which includes the development of infrastructure, ensuring digital literacy and the formation of standards for the use of technology, is recommended. The article emphasizes that the effective implementation of AI can significantly improve the quality of education, create a more inclusive and adaptive educational environment, and promote the development of academic integrity. At the same time, the success of the implementation depends on an integrated approach that takes into account technical, ethical and organizational aspects. Innovative technologies, in particular AI, have the potential to become an important tool for transforming the educational process, contributing to increasing its efficiency, adaptability and flexibility. The use of such technologies will make education more accessible, aimed at meeting the needs of both society and each individual student.
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Roli, Jain, Gupta Puja, Bansal Sahil, and Singh Vichitra. "An AI-Based Online Exam Proctoring Framework." i-manager's Journal on Computer Science 10, no. 3 (2022): 7. http://dx.doi.org/10.26634/jcom.10.3.19161.
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In recent years, there has been an increase in online education. However, an adequate technique was not developed for online examinations. Some educational facilities have compiled assignments that students may copy and paste from the web, while others use remote proctoring, where a human proctor monitors what the students are doing online. Cheating in online tests remains common despite all the growth that has taken place in this field. This paper proposes an Artificial Intelligence (AI)-based system that can assist in the automatic detection of fraud in online tests. This method is both efficient and trustworthy. This system outperformed previous systems in experiments.
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Hussein, Fairouz, Ayat Al-Ahmad, Subhieh El-Salhi, Esra’a Alshdaifat, and Mo’taz Al-Hami. "Advances in Contextual Action Recognition: Automatic Cheating Detection Using Machine Learning Techniques." Data 7, no. 9 (2022): 122. http://dx.doi.org/10.3390/data7090122.
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Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.
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Marat, A., and S. Rakhmetulayeva. "ANALYSIS OF VIDEO QUALITY AND ITS IMPACT ON FACE RECOGNITION FOR AN ONLINE PROCTORING SYSTEM." BULLETIN of D. Serikbayev EKTU 2 (June 30, 2023): 149–57. http://dx.doi.org/10.51885/1561-4212_2023_2_149.
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Online proctoring is a monitoring tool that instantly recognizes any unlawful behavior both before and during the exam. Three different sources are used for surveillance: audio, video streams, and desktop screen record-ings. In general, a proctor, a specifically trained professional who checks to see if the system has made a mistake and looks for violations from students taking the exam, always keeps an eye on the proctoring process. Yet, it is impossi-ble for one individual to monitor more than two people at once, and it is also possible to let fraudulent students sit for the test. Determining how an online proctoring system will be very successful in preventing unfair testing by employ-ing face recognition on video samples is the focus of this paper. The development of a trainable artificial intelligence network makes it possible to employ multiple variables simultaneously. We developed a deep learning technique for automatic face identification as part of this work, employing a convolutional neural network to calculate the differ-ence between a sample and a face in a video. It was typical to employ video samples of various qualities, taken at various distances, from various angles, with various lighting conditions, and with the inclusion of accessories to get more accurate findings. The simulation results showed that, with the exception of the experiment when accessories such as a medical face mask were present, the model we trained accurately predicted the outcome for each sampling criterion. Further examples show the effectiveness of face recognition, also in the quick playback format, the percentage of face recognition reached the minimum threshold (slightly more than 75 %).
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Shmelev, Alexander G. "ACCURACY OF EXPERT FRAUD DETECTION TECHNOLOGY IN REMOTE TEST EXAMS (PROCTORING)." Moscow University Psychology Bulletin, no. 4 (2020): 44–66. http://dx.doi.org/10.11621/vsp.2020.04.03.
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The purpose (objective) of the empirical study is the measurement of the accuracy of expert-proctors in detecting cheating in online testing. Sample of the study. 35 test takers passed an online test of general knowledge on the basis of 30 multiple choice questions. Half of the subjects (18 persons) were “artificial cheaters” — they used cheat sheets with correct answers. Methods. The video recording of the testing process included a “screen capture” so that expert-proctors could observe all cursor movements, see a recording of the subject’s facial expressions and a visual focus of attention in a separate window (recording from the front camera), and could listen to the subject pronouncing the task conditions and answers (“oral decision”). 14 experts took part in rating of video recordings, of which 8 experts showed satisfactory results in terms of the level of accuracy in detecting cheating (their accuracy that was measured using the Kappa coefficient was higher than 0.5). Conclusions. A high asymmetric validity of expert assessments is revealed. More accurate experts allowed a negligible (about 5 percent) number of errors of the “false alarm” type, but a relatively large number of errors of the “skip” type. Recommendations are made for the practical use of the expert assessment method in combination with automatic chronometric analysis of the degree of atypical protocols and subsequent control of face-to-face offline testing of all suspected subjects (examinees).
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Wira Harjanti, Trinugi, Hari Setiyani, Muhamad Nizar Alfi, and Ugi Ispoyo Widodo. "Penerapan Aplikasi Online Exam (ONEX) pada Yayasan Mandiri Smart Education." Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) 8, no. 3 (2025): 218–26. https://doi.org/10.47970/siskom-kb.v8i3.807.
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The modernization of education demands efficiency, flexibility, and integrity in examinations, especially in non-formal institutions conducting competency tests for students. As digital transformation progresses, the need for fast, secure, and reliable assessments increases. ONEX addresses this by providing an online examination system with camera- and microphone-based proctoring, enabling real-time monitoring to maintain exam integrity. This system offers flexibility for participants to take exams remotely while ensuring strict supervision. For organizers, ONEX automates registration, question preparation, and evaluation, reducing time, resources, and personnel costs. Additionally, it enhances accessibility for test-takers, minimizing transportation costs and familiarizing them with digital assessments. By integrating modern evaluation methods, ONEX ensures an efficient, transparent, and fair examination process, benefiting both administrators and participants.
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S., Sakena Benazer, Kandaneri Ramamoorthy Saravanan, and S.M.Kamali. "An AI-Based Intelligent Exam Proctoring System for Secure and Fair Online Assessments." Journal of Advanced Research in Artificial Intelligence & It's Applications 2, no. 1 (2024): 1–7. https://doi.org/10.5281/zenodo.13744992.
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<em>With the growing demand for remote learning and online education, ensuring the security and fairness of online assessments has become a critical challenge. This paper presents an AI-Based Intelligent Exam Proctoring System (AI-EPS) designed to monitor and secure online assessments in real time. The system leverages advanced facial recognition, eye-tracking, posture analysis, and voice recognition technologies to detect suspicious behaviours, such as cheating attempts or unauthorized personnel in the testing environment. By integrating machine learning algorithms, AI-EPS provides real-time monitoring, automatic anomaly detection, and post-exam auditing, ensuring a secure and fair assessment process. The proposed system has been evaluated across different online exam platforms, demonstrating significant improvements in detecting potential cheating attempts and providing instructors with detailed reports. Results indicate a 90% accuracy in detecting irregular behavior, contributing to more reliable and credible online examination practices.</em>
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Abzalov, A. R., I. I. Kashapov, A. Yu Orlov, and I. R. Mamleev. "User authentication based on the three-stage keyboarding model." Herald of Dagestan State Technical University. Technical Sciences 47, no. 3 (2020): 39–48. http://dx.doi.org/10.21822/2073-6185-2020-47-3-39-48.
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Objective. The article offers a three-stage model that allows increasing the effectiveness of authentication in the implementation of distance learning systems, not only in automatic proctoring systems but also in complex information systems of critical objects. Methods. Increasing the effectiveness of user authentication is achieved by increasing the accuracy of authentication using keyboarding. Results. The proposed model will help to implement the process of access differentiation for fraudsters and legal users, by adapting to the slightest changes in the keyboarding parameters, which allows increasing the authentication accuracy. During testing, one of the tested users was authenticated using a deviation comparison, while the other three users were authenticated using the χ2 criterion. The remaining users were not able to complete the authentication procedure at all stages of the system. Conclusion. The results of an experimental study showed the high ability of the proposed model of access control for legitimate users and attackers with some minor changes in the parameters of the keystrokes dynamics, improving the accuracy of user authentication. The user authentication reliability in practice was 97.5%.
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Chikondi, Phiri, and G. Glorindal. "Ensuring integrity in online exams with AI anti-cheat system." i-manager’s Journal on Image Processing 10, no. 3 (2023): 1. http://dx.doi.org/10.26634/jip.10.3.20109.
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The COVID-19 pandemic has compelled radical and innovative reforms and Education and academia have been identified as sectors most adversely affected by the pandemic. Disrupting the age-old classroom setup, the pandemic has forced educational institutions such as schools and universities to implement 'online classes.' However, the evaluation aspect of education remains to be desired. Many automatic online exam proctoring systems have been proposed for online examinations during this COVID-19 pandemic, but they have certain limitations, including fewer and inaccurate functionalities. In this paper, a smart exam monitoring system is presented that addresses many of the problems with past systems, aiming to help institutions prevent malpractices during exams. This smart exam monitoring system leverages advanced AI algorithms to monitor online exams in a more precise and comprehensive manner. It can detect various forms of cheating, such as screen sharing and unauthorized resource access, while also ensuring a fair evaluation process. By integrating cutting-edge technology into the education sector, the aim is to uphold the integrity of online examinations and adapt to the challenges posed by the ongoing COVID-19 pandemic.
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А.Г., Леонов, Мащенко К.А., Орловский А.Е. та Шляхов А.В. "Механизм интерактивного взаимодействия преподавателя со студентами в цифровой образовательной платформе Мирера". Труды НИИСИ РАН 11, № 4 (2022): 94–99. http://dx.doi.org/10.25682/niisi.2021.4.0012.
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Создание цифровых образовательных платформ и сред предоставляет педагогам возможность интенсифицировать образовательный процесс, упрощая и расширяя способы внеаудиторной коммуникации со студентами. С другой стороны, студенту предоставляется возможность осваивать изучаемые компетенции, отправлять задания для автоматической проверки, в удобной форме в любое время, при наличии связи с цифровой образовательной платформой. Разработчики платформы в свою очередь должны предоставить удобный и надежный способ поддержки такого взаимодействия. В статье излагается вариант решения этой проблемы на примере авторской цифровой образовательной платформы Мирера. Отмечается возможность использования подобного механизма для реализации альтернативного подхода к прокторингу. The creation of digital educational platforms and environments gives educators the opportunity to intensify the learning process by simplifying and expanding the ways of extracurricular communication with students. On the other hand, the student is given the opportunity to master the competencies being studied, send assignments for automatic verification, in a convenient form at any time, if there is a connection with a digital educational platform. Platform developers, in turn, must provide a convenient and reliable way to support this interaction. The article describes a solution to this problem using the example of the author's digital educational platform Mirera. The possibility of using such a mechanism to implement an alternative approach to proctoring is noted.
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N, Mr Aravindhan. "Remote Exam Integrity through Advanced AI and Camera Vision Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1226–29. https://doi.org/10.22214/ijraset.2025.72230.
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Education stands as the foundation for individual growth and the advancement of society. In this regard, e-assessment has become an essential and adaptive approach for evaluating students in a remote setting. However, this mode of evaluation necessitates advanced solutions to uphold the authenticity and fairness of the testing process. Key obstacles include preventing unauthorized support, maintaining student engagement, and ensuring that assessments accurately measure genuine understanding and skill.To address these concerns, this project introduces an intelligent, AI-enhanced remote proctoring system that incorporates computer vision capabilities to establish a reliable virtual examination environment. The backbone of this system is built on deep learning techniques, especially Temporal Convolutional Networks (TCNs), which analyze sequences of video frames to interpret user behavior and recognize signs of disengagement or malpractice.The platform integrates various components such as live webcam monitoring, background audio analysis, and activity tracking of keyboard and mouse inputs. These interconnected features collectively validate the student's identity and detect any suspicious actions during the test.By leveraging artificial intelligence for automatic supervision, the system reduces the need for human invigilators while enhancing the transparency and reliability of digital exams. Its modular, scalable, and efficient design seamlessly aligns with the expanding ecosystem of online education tools, ensuring continuous learning.
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Yuniar, Rendhi Fatrisna. "The Implementation of Quipper Campus in English Language Assessment: A Qualitative Case Study." Proceeding of International Conference on Islamic Education (ICIED) 9, no. 1 (2024): 313. https://doi.org/10.18860/icied.v9i1.3159.
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Technological advancements in education are encouraging the next generation to enhance skills in computer-based learning. Educational media, such as Quipper Campus, have increasingly adopted online platforms to make learning more effective and efficient. This study explores teachers and students perceptions of using Quipper Campus in teaching and learning process, especially for English class. This research also focuses on the implementation of Quipper Campus on assessments aspect and investigates how the platform can facilitate effective and efficient assessment processes. The research was conducted at university level in Indonesia, involving ten English lecturers and thirty students which are selected through purposive sampling. A qualitative case study approach was employed, using interviews, observations, and documentation as data collection instruments. Data analysis followed Miles and Huberman’s approach, consisting of data reduction, data presentation, and conclusion drawing. The findings indicate that Quipper Campus supports seven of the eleven types of assessments defined in Permendikbud No. 66 of 2013 on educational standards, including self-assessment, authentic assessment, portfolio-based assessment, and daily tests. Moreover, Quipper Campus’s adaptability is highlighted as it supports multiple curriculum standards, allowing teachers to provide relevant and tailored content. However, mid-semester and end-semester tests are not implemented on the platform due to Quipper lacks comprehensive security features to prevent cheating during exams, such as automatic proctoring, plagiarism detection, or browser locking.
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Мерецков, О. В. "Применение систем искусственного интеллекта для снижения трудоемкости сопровождения электронного обучения преподавателем". Человеческий капитал, № 5(197) (18 травня 2025): 113–26. https://doi.org/10.25629/hc.2025.05.11.
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В данной работе представлен системный обзор современных возможностей использования систем искусственного интеллекта (ИИ) для оптимизации труда педагогов в электронной образовательной среде. Особое внимание уделяется анализу сервисов, предназначенных для автоматизации различных этапов педагогической деятельности: подготовки и проведения занятий, проверки самостоятельных работ обучающихся, промежуточной и итоговой аттестации. Рассмотрены инструменты для поиска и структурирования научной информации, автоматизации создания учебного контента (конспектов, презентаций, иллюстраций), а также сервисы для синтеза и распознавания речи. Работа содержит практические рекомендации по применению таких систем, как генеративные нейросети (ChatGPT, YandexGPT, Гигачат), библиотеки научных публикаций (eLibrary), специализированные платформы для тестирования и проверки оригинальности текстов («Антиплагиат», Quizgecko), а также системы автоматического прокторинга. Анализируются возможности интеграции ИИ в образовательный процесс с учетом российского законодательства и требований национальных стандартов. Поднимаются этические вопросы и обсуждаются риски, связанные с использованием ИИ студентами для недобросовестного выполнения заданий. Работа выделяется структурированностью, актуальностью рассматриваемых тем и ориентирована на широкий круг специалистов: преподавателей, методистов, администраторов образовательных платформ. Отдельно выделяется потенциал дальнейшего развития технологий ИИ в образовании и необходимость их ответственного применения. This paper presents a systematic review of modern possibilities of using artificial intelligence (AI) systems to optimize the work of teachers in the electronic educational environment. Particular attention is paid to the analysis of services designed to automate various stages of pedagogical activity: preparation and conduct of classes, verification of students' independent work, interim and final certification. Tools for searching and structuring scientific information, automating the creation of educational content (notes, presentations, illustrations), as well as services for speech synthesis and recognition are considered. The paper contains practical recommendations on the use of such systems as generative neural networks (ChatGPT, YandexGPT, Gigachat), libraries of scientific publications (eLibrary), specialized platforms for testing and checking the originality of texts (“Antiplagiat”, Quizgecko), as well as automatic proctoring systems. The possibilities of AI integration into the educational process are analyzed, taking into account Russian legislation and requirements of national standards. Ethical issues are raised and risks are discussed, related to the use of AI by students for unfair fulfillment of assignments. The work is distinguished by its structured nature, relevance of the topics under consideration, and is oriented to a wide range of specialists: students, teachers, and other professionals. The potential for further development of AI technologies in education and the need for their responsible use. responsible application.
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Mohammed, Hussein M., and Qutaiba I. Ali. "Cheating Detection in E-exams System Using EEG Signals." International Conference on Scientific and Innovative Studies 1, no. 1 (2023): 200–209. http://dx.doi.org/10.59287/icsis.601.
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Cheating in e-exams is a real problem that threatens academic integrity and underminesconfidence in the feasibility of remote assessments. Many previous research papers and studies discussedthe issue of cheating in e-exams to prevent or reduce it through the use of the available technologies suchas the use of a web camera to monitor the examinee, some researchers proposed using specific software torestrict the examinee from accessing resources that are not permitted during the exam. This work aims todesign a Semi-automatic, AI-based e-proctoring system that mitigates cheating in e-exams. This researchproposed an innovative method to detect the possibility of cheating in the e-exams. This method relies onthe use of IoT and the Muse2 devices to detect the examinee's physiological state and determine whether itis “Normal” or “Abnormal” through the examinee`s EEG signal, where the abnormal state indicates apossibility of cheating. Convolutional Neural Network (CNN) was used to distinguish the examinee's state.The collected data from 15 students at the fourth stage of the Computer Engineering Department/ Universityof Mosul ranging between 23 and 26 years old showed that there is an obvious difference between the“calm” or “Normal” state and “stress” or “Abnormal” state in the EEG signal of the volunteer. The accuracyof the system was obtained for many testing datasets. The dataset was divided into two main datasets; the30 and 60 seconds duration time. The best accuracy obtained for the 30sec duration time was 97.37%, and97.14% for the 60sec duration time. The researchers concluded that the EEG signal contains a lot ofimportant information that can be utilized to detect the physiological state of the examinee and that theMuse2 device can be reliable to record the EEG signal.
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Abhijith, B. "Litertature Survey for Digital Transformation of Academic Activities Using Web Development." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 68–71. http://dx.doi.org/10.22214/ijraset.2022.40189.
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Abstract: This project presents a web interface for organising training and placement information. The goal of this project is to create a system that can be used by a college's placement cell and To automate the training and placement process . It's the most important programme. Students utilise this to help them prepare for their placements. It contains company information for numerous companies, and users can search for recruitment processes, profiles. This facility is available to students for future enhancement. The documents supplied can be downloaded and used for offline preparation for the placement exam. In addition, there are job interview tips included and also questions for preparing aptitude and technical interview. This project uses Java server page on the front end, and Servelet on the back end, and MY SQL for the database. The goal is to create a system that includes functionality for doing placement-related actions. It is built using a fully modular architecture. The modularity of the system. Education and training Any educational institute's placement department is critical. Which the majority of the work has been done manually up to this point. The project will involve the least amount of manual labour and the most amount of technology. Optimisation, abstraction, and security are all factors to consider. This is a web page application that will benefit both students and teacher administration authority to undertake out all activities in the this division. The system is a web-based programme that may be accessed by anyone in the DSCE(Dayananda Sagar College of Engineering) organisation with a valid login. This system can be used as a starting point. TPO (Training and Placement Officers) application in order for the college to manage the student information in relation to placement. Students who are logging in should be able to fill in the blanks. Form for registering This project's most distinguishing aspect is that it is a collaborative effort. Registration is only required once. The application gives you the option of keeping track of the kids' information It also comes with a list of prospects was asked to recruit pupils based on specified search term When administrator logs in, he or she can search for anything. The students' information is displayed. Colleges will benefit from this endeavour.to put full-fledged IT implementation into effect This will also be beneficial. The Proctorial System is designed to guide students through their academic and professional journeys. A proctor, who is a faculty member from the concerned/relevant department, is assigned to each undergraduate student. The proctor monitors the student’s performance and behaviour, offering guidance and enforcing discipline when needed. The student and proctor meet regularly to assess the student’s academic progress with regard to performance, registration for credits, and attendance. The same is also reported to the student’s parents/guardians.
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Arnò, Simone, Alessandra Galassi, Marco Tommasi, Aristide Saggino, and Pierpaolo Vittorini. "State-of-the-Art of Commercial Proctoring Systems and Their Use in Academic Online Exams." January 19, 2021. https://doi.org/10.4018/ijdet.20210401.oa3.
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Online proctoring generally refers to the practice of proctors monitoring an exam over the internet, usually through a webcam. This technology has gained relevance during the current COVID-19 pandemic, given that the social distance owing to health reasons has consequently led to the switching of all learning and assessment activities to online platforms. This paper summarises the available state-of-the-art of commercial proctoring systems by identifying the main features, describing them, and analysing the way in which different proctoring programs are grouped on the basis of the services they offer. Furthermore, the paper reports on two case studies concerning online exams taken with both automated and human proctoring approaches. The outcomes from state-of-the-art approaches and the experience gained by the two case studies are then summarised in the conclusion, where the need for an organisational effort in loading photographs that can be used to easily recognise student faces, and using an automated online proctoring program to support manual proctoring have been suggested.
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Wang, Pu, Yifeng Lin, and Tiesong Zhao. "Smart proctoring with automated anomaly detection." Education and Information Technologies, December 2, 2024. https://doi.org/10.1007/s10639-024-13189-7.
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Yoder-Himes, Deborah R., Alina Asif, Kaelin Kinney, et al. "Racial, skin tone, and sex disparities in automated proctoring software." Frontiers in Education 7 (September 20, 2022). http://dx.doi.org/10.3389/feduc.2022.881449.
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Students of color, particularly women of color, face substantial barriers in STEM disciplines in higher education due to social isolation and interpersonal, technological, and institutional biases. For example, online exam proctoring software often uses facial detection technology to identify potential cheating behaviors. Undetected faces often result in flagging and notifying instructors of these as “suspicious” instances needing manual review. However, facial detection algorithms employed by exam proctoring software may be biased against students with certain skin tones or genders depending on the images employed by each company as training sets. This phenomenon has not yet been quantified nor is it readily accessible from the companies that make this type of software. To determine if the automated proctoring software adopted at our institution and which is used by at least 1,500 universities nationally, suffered from a racial, skin tone, or gender bias, the instructor outputs from ∼357 students from four courses were examined. Student data from one exam in each course was collected, a high-resolution photograph was used to manually categorize skin tone, and the self-reported race and sex for each student was obtained. The likelihood that any groups of students were flagged more frequently for potential cheating was examined. The results of this study showed a significant increase in likelihood that students with darker skin tones and Black students would be marked as more in need of instructor review due to potential cheating. Interestingly, there were no significant differences between male and female students when considered in aggregate but, when examined for intersectional differences, women with the darkest skin tones were far more likely than darker skin males or lighter skin males and females to be flagged for review. Together, these results suggest that a major automated proctoring software may employ biased AI algorithms that unfairly disadvantage students. This study is novel as it is the first to quantitatively examine biases in facial detection software at the intersection of race and sex and it has potential impacts in many areas of education, social justice, education equity and diversity, and psychology.