ARTIFICIAL INTELLIGENCE IN STUDENT PRIVACY AND DATA SECURITY

The rapid digitization of education has revolutionized data management practices, yet it concurrently escalates risks to student data privacy and security. This paper examines the dual role of Artificial Intelligence (AI) in both exacerbating and mitigating these challenges. While AI-driven tools such as learning analytics and biometric systems enhance educational outcomes, they introduce vulnerabilities like adversarial data manipulation, over-collection of sensitive information, and algorithmic bias. Traditional security models, reliant on rule-based systems and manual oversight, prove inadequate against evolving cyber threats, underscoring the need for adaptive solutions. AI-based approaches—including federated learning, differential privacy, and anomaly detection—offer proactive mechanisms to safeguard data through decentralized training, noise-injected anonymization, and real-time threat detection. However, these technologies face implementation barriers such as high computational costs, regulatory conflicts, and ethical dilemmas. Regulatory frameworks like GDPR, FERPA, and COPPA further complicate compliance, as divergent mandates on data retention, consent, and transparency challenge global institutions. Through a comparative analysis of AI and traditional models, this study advocates for hybrid frameworks that integrate AI’s scalability with human oversight to balance innovation and accountability. Case studies highlight AI’s efficacy in reducing breaches (e.g., 75% fewer FERPA violations via automated redaction tools) but also expose risks like biased facial recognition systems. The paper concludes with strategic recommendations: prioritizing ethical AI governance, fostering regulatory harmonization, and investing in infrastructure to democratize access. By addressing these imperatives, educational stakeholders can harness AI’s potential while upholding the trust and privacy essential to equitable learning environments