Abstract
Objective
This presentation provides a foundational overview of the privacy and security risks associated with the increasing use of artificial intelligence in educational environments. Although originally designed for K–12 settings, the framework is equally relevant to higher education, where AI tools intersect with complex data systems, academic research, and diverse learner populations.
Context
AI-enabled systems now power functions ranging from adaptive learning platforms and automated assessments to predictive analytics and content generation. As these tools expand, so does the volume of sensitive data they collect and process. Higher education institutions must navigate a layered set of risks—including data exposure, algorithmic bias, policy gaps, and vendor accountability—while maintaining student trust and academic integrity.
Key Insights
The presentation highlights four core areas of institutional responsibility:
- Understanding AI Applications – Use cases span instructional support, student success prediction, administrative automation, and research enablement, all of which generate new data flows and decision points.
- Navigating Privacy Concerns – AI tools often collect identifiable student interaction data, creating risks related to storage, access, re-identification, and regulatory alignment (e.g., GDPR, state privacy laws, institutional data policies).
- Addressing Security Risks – AI introduces expanded attack surfaces, including phishing, data breaches, and model exploitation. Algorithmic bias and AI-generated misinformation present additional threats to equity and academic integrity.
- Implementing Safeguards – A proactive approach requires clear institutional policy, vendor vetting, role-based visibility into data use, digital literacy training for faculty and students, and transparent communication with campus stakeholders.
Future Directions
Institutions seeking responsible AI adoption must pair innovation with governance. This includes developing campus-wide AI usage guidelines, expanding cybersecurity protocols to include model risk, and embedding ethical review processes into technology procurement and curriculum design.