Abstract

This study investigates the role of Artificial Intelligence (AI) in enhancing cybersecurity for U.S. public schools, with the primary objective of evaluating AI's effectiveness in reducing cyber threats and safeguarding student privacy. Specifically, the study assesses AI-driven security systems such as threat detection and anomaly detection algorithms, which help schools monitor network traffic and identify potential breaches in real-time. Using logistic regression on data from the K-12 Cybersecurity Resource Center, findings reveal that schools implementing AI solutions are 75% less likely to experience cyber breaches (p < 0.001), highlighting AI's protective impact. Furthermore, a comparative analysis of FERPA and COPPA compliance reports highlights a substantial reduction in privacy violations among AI-using schools, with an average of 0.57 violations per school, compared to 1.50 in schools without AI. A K-means cluster analysis identified budget constraints (65.75%) and IT staff shortages (55.25%) as primary barriers to AI adoption. To address these obstacles, the study recommends phased technology upgrades and increased funding for workforce training as critical strategies to facilitate AI integration and enhance cybersecurity across educational institutions. These strategic interventions are essential for optimizing the effectiveness of AI-driven security systems, making it feasible for resource-constrained schools to adopt and maintain advanced cybersecurity measures. The study’s findings contribute to the growing body of knowledge on educational cybersecurity and provide actionable insights for policymakers and administrators seeking to strengthen data protection and privacy in school environments.

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