Integrate IoT and biometric technologies to augment the security, scalability, and operational efficiency of online proctoring systems¶

Strategy · All strategies
Evidence · 1 claim (1 against) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The review recommends combining Internet of Things sensing and biometric verification with AI-based proctoring to strengthen security, scalability, and operational efficiency. It reports that "IoT Monitoring Enables real-time, multimodal cheating detec -tion using sensors" while noting privacy concerns and environment-dependent accuracy, and that advanced security techniques with IoT enhance authenticity and data integrity using biometric/fingerprint methods but involve costly hardware and user resistance due to invasiveness.

Design Implications¶

Context¶

Requirements¶

• Hardware such as biometric/fingerprint sensors; the review notes costly hardware as a limitation

Constraints¶

• Privacy concerns and environment-dependent accuracy for IoT monitoring; user resistance due to invasiveness

Target Learners¶

• higher education students taking online or remote examinations

Target Learning Goals¶

• secure identity verification and cheating detection in digital assessment

Claims¶

• Model accuracy of vision-based proctoring systems can vary across demographics, lighting conditions, and hardware setups, potentially disadvantaging some students [-W]

Related Strategies¶

• Mitigation strategies pairing each AI-DSS challenge with a targeted countermeasure

Examples¶

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Key Sources¶

• Malhotra, M., & Chhabra, I. (2026). Ensuring academic integrity through automated online exam proctoring a decade long systematic review. Discover Education. https://doi.org/10.1007/s44217-026-01224-3