AI-Powered Mobile Proctoring Frameworks using Machine Learning Algorithms in Higher Education: Post-Covid Trends, Challenges, and Ethical Implications

Abstract

The rapid transition to online learning during and after the COVID-19 (Corona Virus Disease) pandemic has heightened the need for secure, scalable, and ethical online exam systems. AI-powered mobile proctoring frameworks have emerged as viable alternatives to traditional invigilation methods, enabling automated anomaly detection and behavior analysis through machine learning algorithms. This systematic review examines post-COVID trends, technological developments, challenges, and ethical implications of mobile AI proctoring in higher education. Following PRISMA 2020 guidelines, 180 studies were retrieved and screened, with 20 peer-reviewed articles meeting the inclusion criteria. Findings reveal that while AI-powered proctoring enhances scalability, integrity, and real-time monitoring, it raises significant concerns about privacy, algorithmic bias, accessibility, and technical reliability. The review identifies gaps in relation to technical and methodological issues, ethical and social concerns, and institutional and infrastructural readiness. This review illustrates a lapse in the existing literature, which focus on resource intensive proctoring frameworks without considering mobile compatibility and light-weight frameworks, discusses technical challenges, and recommends future research directions to balance technological effectiveness with ethical standards.

Keywords

Mobile AI proctoring; machine learning algorithms; higher education; post-COVID trends; ethical and privacy; mobile proctoring frameworks.

References

P. Bawa, “Retention in online courses: Exploring issues and solutions—A literature review,” SAGE Open, vol. 6, no. 1, pp. 1–11, 2016. https://doi.org/10.1177/2158244015621777

A. W. Bates, Teaching in a Digital Age: Guidelines for Designing Teaching and Learning. Tony Bates Associates Ltd, 2015, [Online]. Available: https://open.library.ubc.ca/soa/cIRcle/collections/ubccommunityandpartnerspublicati/52387/items/1.0224023, Accessed: Dec. 14, 2024.

I. E. Allen and J. Seaman, Digital Learning Compass: Distance Education Enrollment Report 2017. Babson Survey Research Group, 2017, [Online]. Available: https://files.eric.ed.gov/fulltext/ED580868.pdf, Accessed: Dec. 14, 2024.

C. Huang, X. Li, and Y. Chen, “Machine learning for online exam proctoring: Ethical, legal, and technical perspectives,” Computers & Education, vol. 185, p. 104532, 2022. https://doi.org/10.1016/j.compedu.2022.104532

A. Jain and S. Dandapat, “Real-time student monitoring using deep learning models,” Journal of Educational Technology Systems, vol. 50, no. 2, pp. 143–165, 2021. https://doi.org/10.1177/00472395211009263

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, real-time object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788. https://doi.org/10.1109/CVPR.2016.91

J. Yang, L. Zhang, and Q. Chen, “Mobile AI proctoring frameworks in post-COVID higher education,” Educational Technology & Society, vol. 24, no. 4, pp. 15–28, 2021.

Y. Huang, H. Lin, and J. Wang, “Ethical implications of AI-based remote proctoring in higher education,” International Journal of Educational Technology in Higher Education, vol. 19, no. 1, p. 37, 2022. https://doi.org/10.1186/s41239-022-00352-1

S. Jain and S. Dandapat, “Intelligent proctoring using deep learning for online exam,” International Journal of Artificial Intelligence in Education, vol. 31, no. 4, pp. 642–660, 2021. https://doi.org/10.1007/s40593-020-00238-3

Y. Huang, F. Zhang, and X. Li, “Evaluating AI-driven proctoring technologies for online learning: Challenges and opportunities,” Education and Information Technologies, vol. 27, no. 8, pp. 10933–10955, 2022. https://doi.org/10.1007/s10639-022-11061-8

H. Yang, T. Liu, and P. Zhou, “Artificial intelligence in online assessments: Trends, technologies, and ethical concerns,” British Journal of Educational Technology, vol. 52, no. 5, pp. 2031–2047, 2021. https://doi.org/10.1111/bjet.13169

D. O. Omondi, G. Onyango, and M. Ouma, “Mobile-based AI proctoring: A framework for equitable assessment in Sub-Saharan Africa,” African Journal of ICT in Education, vol. 14, no. 2, pp. 88–104, 2022.

C. Chin, J. Lee, and S. Park, “Artificial intelligence ethics in online education: Privacy, fairness, and accountability,” Computers & Education, vol. 193, p. 104683, 2023. https://doi.org/10.1016/j.compedu.2023.104683

H. Kaur and R. Singh, “Privacy implications of AI-based student surveillance systems,” Journal of Educational Computing Research, vol. 60, no. 6, pp. 1330–1347, 2022. https://doi.org/10.1177/07356331221076032

M. Garcia, R. Patel, and S. Khan, “Post-pandemic adoption of online proctoring systems in higher education,” International Journal of Educational Technology in Higher Education, vol. 19, no. 1, p. 45, 2022. https://doi.org/10.1186/s41239-022-00360-1

Q. Zhang, L. Chen, and X. Zhao, “Machine learning in remote proctoring: A review of post-COVID developments,” Computers in Human Behavior Reports, vol. 9, p. 100259, 2023. https://doi.org/10.1016/j.chbr.2022.100259

M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021. https://doi.org/10.1136/bmj.n71

R. Omondi, C. Otieno, and J. Nyangena, “AI mobile proctoring adoption in Kenyan universities: Opportunities and challenges,” African Journal of Educational Technology, vol. 8, no. 2, pp. 21–35, 2022.

L. Zhang, T. Wu, and X. Lin, “Edge computing in mobile AI proctoring systems,” IEEE Transactions on Learning Technologies, vol. 16, no. 4, pp. 655–668, 2023. https://doi.org/10.1109/TLT.2023.3245678

M. Garcia, A. Torres, and L. Salazar, “Student perceptions of online proctoring during the COVID-19 pandemic,” Computers & Education, vol. 183, p. 104497, 2022. https://doi.org/10.1016/j.compedu.2022.104497

X. Li and J. Chen, “Privacy frameworks for AI-based assessment tools: A regulatory perspective,” Computers & Security, vol. 132, p. 103356, 2023. https://doi.org/10.1016/j.cose.2022.103356

S. Yang, Y. Chen, and K. Davis, “Ethical challenges in facial recognition-based proctoring,” Computers in Human Behavior, vol. 120, p. 106729, 2021. https://doi.org/10.1016/j.chb.2021.106729

http://dx.doi.org/10.28989/avitec.v8i1.3600

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