A Comprehensive Survey of AI-Based Online Examination Monitoring and Behavioural Performance Analysis Systems

Article Sidebar

PDF
Published: 2026-08-26
DOI: https://doi.org/10.26483/ijarcs.v17i4.7539
Keywords:
Online Examination Systems , AI-Based Proctoring , Mouse Heatmap Analysis, , Facial Emotion Recognition , Head Pose Estimation , Object Detection , Multimodal Data Fusion , Behavioural Performance Analysis , Academic Integrity ,

Main Article Content

ALAN C B
APJ Abdul Kalam Kerala Technological Univer
Ansha Anilkumar E
Nandana P V
Pooja T K
Dr. ARUNA A S

Abstract

The rapid adoption of online examination systems in educational institutions has introduced significant challenges in ensuring academic integrity, reliable student monitoring, and meaningful performance evaluation. Traditional online proctoring approaches primarily rely on webcam surveillance and screen recording, which are often insufficient to capture subtle behavioural patterns such as student engagement, attention, and cognitive difficulty during examinations. To address these limitations, recent research has explored the use of artificial intelligence and computer vision techniques for automated exam monitoring and behaviour analysis. This survey presents a comprehensive review of existing studies on AI-based online examination systems, focusing on key components such as face recognition for identity verification, mouse behaviour and heatmap analysis for interaction assessment, facial emotion recognition for affective state detection, head pose estimation for attention monitoring, and object detection for exam integrity enforcement. Furthermore, the survey examines multimodal data fusion techniques used to integrate heterogeneous behavioural signals for holistic performance evaluation. By critically analysing the methodologies, algorithms, strengths, and limitations of prior research, this survey identifies existing research gaps and establishes the conceptual foundation for the development of an integrated smart examination platform capable of real-time monitoring and post-exam behavioural performance analysis.

Article Details

Issue

Vol. 17 No. 4 (2026): July-August 2026

Section

Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.

References

[1] Valavanidis, A. (2023). Artificial intelligence (ai) applications. Department of Chemistry, National and Kapodistrian University of Athens, University Campus Zografou, 15784.

[2] N. G. Niharika and S. N. Nayak, “Artificial Intelligence Based Online Examination Proctoring System,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 11, no. IX, pp. 569–575, Sep. 2023.

[3] Y. Atoum, L. Chen, A. Liu, S. D. Hsu, and X. Liu, “Automated Online Exam Proctoring,” IEEE Trans. Multimedia, vol. 19, no. 7, pp. 1609–1624, Jul. 2017.

[4] Pi, P., & Lima, D. (2021). Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis. International Journal of Cognitive Computing in Engineering, 2, 93-103.

[5] Purwono, P., Ma'arif, A., Rahmaniar, W., Fathurrahman,

H. I. K., Frisky, A. Z. K., & ul Haq, Q. M. (2023).

Understanding of convolutional neural network (cnn): A review. International Journal of Robotics and Control Systems, 2(4), 739-748.

[6] Aruna, A. S., Babu, K. R., & Deepthi, K. (2025). A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study. Molecular Diversity, 29(3), 2473-2487.

[7] Alsanad, H. R., Sadik, A. Z., Ucan, O. N., Ilyas, M., & Bayat, O. (2022). A-V3 based real-time drone detection algorithm. Multimedia tools and applications, 81(18), 26185-26198.

[8] Turner, J. R. (2020). Area under the curve (AUC). In Encyclopedia of Behavioral Medicine (pp. 146-146). Cham: Springer International Publishing.

[9] P. Sharma and U. Tripathi, “AI-Based Remote Examination Monitoring System,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 4, pp. 311–318, 2021.

[10] D. Awaghade, T. Bombe, T. Deshmukh, and K. Takawane, “Automated Online Examination System with AI-Based Monitoring,” International Journal of Computer Applications, vol. 176, no. 29, pp. 1–6, 2020.

[11] H. S. G. Hadian, Y. Bandung, and A. A. Putra, “Online Examination System Using Face Detection and Verification,” Proc. Int. Conf. Information Technology Systems and Innovation, pp. 1–6, 2019.

[12] A. W. Muzaffar, M. Tahir, M. W. Anwar, Q. Chaudry, S.

R. Mir, and Y. Rasheed, “A Systematic Review of Online Exams Solutions in E-Learning: Techniques, Tools, and Global Adoption,” IEEE Access, vol. 9, pp. 32689–32712, 2021, doi: 10.1109/ACCESS.2021.3060192.

[13] Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to artificial intelligence (pp. 87-99). Cham: Springer International Publishing.

[14] Lim, C. Y., Markus, C., & Loh, T. P. (2021). Precision verification: effect of experiment design on false acceptance and false rejection rates. American Journal of Clinical Pathology, 156(6), 1058-1067.

[15] R. Ryu, S. Yeom, D. Herbert, and J. Dermoudy, “A Comprehensive Survey of Context-Aware Continuous Implicit Authentication in Online Learning Environments,” IEEE Access, vol. 11, pp. 24561–24585, 2023, doi: 10.1109/ACCESS.2023.3253484.

[16] Zhang, L., Wang, X., Cooper, E., Evans, N., & Yamagishi,

J. (2023). Range-based equal error rate for spoof localization. arXiv preprint arXiv:2305.17739.

[17] A. Khaesawad, Y. Fukuchi, V. Yem, and N. Nishiuchi, “Machine Learning-Based Classification of Programming Logic Understanding Levels by Mouse-Tracking Heatmaps,” IEEE Access, vol. 13, pp. 89905–89918, 2025, doi: 10.1109/ACCESS.2025.3571050.

[18] Valkenborg, D., Rousseau, A. J., Geubbelmans, M., & Burzykowski, T. (2023). Support vector machines. American journal of orthodontics and dentofacial orthopedics, 164(5), 754-757.

[19] Acito, F. (2023). k nearest neighbors. In Predictive Analytics with KNIME: Analytics for Citizen Data Scientists (pp. 209-227). Cham: Springer Nature Switzerland.

[20] Priyanka, & Kumar, D. (2020). Decision tree classifier: a detailed survey. International Journal of Information and Decision Sciences, 12(3), 246-269.

[21] Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69-79.

[22] Kruse, R., Mostaghim, S., Borgelt, C., Braune, C., & Steinbrecher, M. (2022). Multi-layer perceptrons. In Computational intelligence: a methodological introduction (pp. 53-124). Cham: Springer International Publishing.

[23] Sreedharan, R., Prajapati, J., Engineer, P., & Prajapati, D. (2023). Leave-one-out cross-validation in machine learning. In Ethical issues in AI for bioinformatics and chemoinformatics (pp. 56-71). CRC Press.

[24] S. Satre, S. Patil, T. Mane, V. Molawade, T. Gawand, and

A. Mishra, “Online Exam Proctoring System Based on Artificial Intelligence,” Proc. IEEE Int. Conf., 2023.

[25] S. Prathish, “Audio-Visual Based Online Exam Proctoring System,” International Journal of Engineering Research & Technology, vol. 9, no. 6, pp. 120–125, 2020.

[26] Mishra, S., Verma, V., Akhtar, N., Chaturvedi, S., & Perwej, Y. (2022). An intelligent motion detection using OpenCV. International Journal of Scientific Research in Science, Engineering, and Technology, 9(2), 51-63.

[27] Lim, W. M. (2025). What is quantitative research? An overview and guidelines. Australasian Marketing Journal, 33(3), 325-348.

[28] Vieira, S., Pinaya, W. H. L., Garcia-Dias, R., & Mechelli,

A. (2020). Multimodal integration. In Machine Learning (pp. 283-305). Academic Press.

[29] Manthiramoorthy, C., & Khan, K. M. S. (2024). Comparing several encrypted cloud storage platforms. International Journal of Mathematics, Statistics, and Computer Science, 1, 44-62.

[30] Pal, S., Jhanjhi, N. Z., Abdulbaqi, A. S., Akila, D., Almazroi, A. A., & Alsubaei, F. S. (2023). A hybrid edge-cloud system for networking service components optimization using the internet of things. Electronics, 12(3), 649.

[31] Liang, Q., Shenoy, P., & Irwin, D. (2020, October). Ai on the edge: Characterizing ai-based iot applications using specialized edge architectures. In IEEE International Symposium on Workload Characterization (IISWC).