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Privacy Concerns of AI-Based Surveillance Systems Under FERPA and Educational Privacy Laws
Conference proceeding

Privacy Concerns of AI-Based Surveillance Systems Under FERPA and Educational Privacy Laws

Sabrina Hackman, Michelle Johnson, W. Chris Taylor, Anisha Yellamraju and Maryam Taeb
Proceedings of the 2026 ACM Southeast Conference, pp.183-192
ACM Other Conferences
ACMSE 2026: 2026 ACM Southeast Conference (Troy University, Troy, Alabama, USA, 04/23/2026–04/25/2026)
04/23/2026
DOI: https://doi.org/10.1145/3746467.3801527

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Abstract

K-12 schools are increasingly implementing Artificial Intelligenc (AI)-based surveillance tools, such as facial recognition and behavior-monitoring systems, to enhance campus safety. However, these tools collect sensitive biometric and behavioral data, raising concerns about privacy, consent, and potential discrimination. Prior studies show that AI surveillance systems often rely on opaque algorithms that shape disciplinary or safety decisions without transparency. Privacy laws, particularly the Family Educational Rights and Privacy Act (FERPA), were not designed for algorithmic monitoring or inferential data practices. Studies indicate that gaps in FERPA's definitions of educational records and personally identifiable information allow schools and third party vendors to process biometric and behavioral data with limited oversight. Our review finds significant regulatory gaps and underscores the need for updated equitable policies that ensure transparency, accountability, and the protection of student rights as AI monitoring expands across K-12 education. This review distinguishes between raw educational records and AI-generated inferences, highlighting how this distinction complicates Family Educational Rights and Privacy Act compliance and enforcement.

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