The increasing dependence of educational institutions on Learning Management Systems (LMS), institutional portals, and online content repositories has expanded the attack surface of digital learning environments. Among the threats affecting these systems, web defacement is a critical concern because unauthorized changes to visual elements, announcements, course materials, and page structures may mislead users, disrupt learning continuity, and damage institutional credibility. Existing defacement detection approaches are commonly designed for general-purpose websites and often rely on single-method detection, making them less suitable for the dynamic and multi-modal nature of educational web platforms. This paper proposes an education-centered, multi-modal artificial intelligence (AI) framework for web defacement detection. The proposed framework integrates computer vision for visual anomaly detection, natural language processing for textual and semantic inconsistency analysis, and structural anomaly detection for identifying irregularities in HTML and Document Object Model (DOM) patterns. The framework is organized into data acquisition, preprocessing, feature extraction, AI-based detection, fusion and decision-making, and response-feedback layers. Use-case scenarios are presented to demonstrate how the framework can support LMS and institutional portal monitoring. The study contributes a conceptual architecture that can guide future implementation, dataset development, and empirical evaluation of AI-assisted content integrity protection in educational web platforms.

 

artificial intelligence in education, anomaly detection, computer vision, e-learning security, educational technology systems, learning management systems, natural language processing, web defacement detection