AI-Driven Cybersecurity Strategies for Educational Platforms: Toward Transparent and Resilient Learning Environments

Ilkin Javadov

Abstract: The shift toward AI-driven education highlights evolving instructional practices and heightened cybersecurity concerns. AI improves personalization, access, and operational scalability, but also introduces flexible and adaptive safety frameworks for complex hazard vectors. This letter presents a structured analysis of the mechanisms, recognizing the risks managed by AI within academic systems. It applies ethical hacking and cyber defense techniques to recognize practical contradictions in AI environments, modeling disadvantageous threats, and zero architectures. Additionally, we address current implementation limitations, highlight the important needs of a strong governance model, and explain KI (XAI). This has clearly decided to maintain confidence, transparency and flexibility in an intelligent education platform. This paper explores how artificial intelligence can both enhance and threaten cybersecurity in educational environments. It introduces adaptive AI-based defenses using ethical hacking, behavior profiling, threat modeling, and explainable AI frameworks. The proposed model emphasizes transparency, accountability, and institutional adaptability. It also outlines challenges in balancing privacy, accuracy, and oversight. The study highlights the urgency of proactive, integrated defenses for safeguarding digital education infrastructures.

Keywords: AI in education, cybersecurity framework, explainable AI, ethical hacking, threat modeling