AI-SEC-EDU Conceptual Framework: Securing E-Learning in Low-Income Countries’ Higher Education Institutions - Mukalere, Justine; Omona, David Andrew; Ikwap, Agatha Flavia (Asosiasi Doktor Sistem Informasi Indonesia, 2025)
The evolving digital threat landscape, characterized by sophisticated AI-driven attacks, increasingly targets Higher Education Institutions (HEIs) through e-learning systems. This study introduces the AI-SEC-EDU framework to guide the integration of security controls and AI-enabled intelligence into cybersecurity strategies for e-learning platforms. The framework is based on a narrative review of existing cybersecurity interventions for e-learning in Low-Income Countries (LICs) and their approach to managing cybersecurity in the age of Artificial Intelligence. A search across four databases—ACM, Springer, ScienceDirect, and Google Scholar—in May 2025 identified 621 papers, of which eight met the inclusion criteria using PICO and PRISMA guidelines. The selected papers focused on cybersecurity in e-learning, discussing frameworks, models, and algorithms for platforms like Moodle, Google Classroom, and Coursera, some of which incorporate AI and open-source options. The study identifies three key security risk domains: technological infrastructure, human factors, and institutional governance, all of which are compounded by limited AI integration. Existing measures focus on system hardening but fail to address AI-based threat prediction and human behavior vulnerabilities. The AI-SEC model integrates AI, user awareness, and governance controls to provide adaptive, context-sensitive cybersecurity solutions for e-learning in LICs. This framework serves as a diagnostic and planning tool, aligning policies, institutional practices, and national strategies.