Artificial Intelligence, Cybersecurity and Digital Learning Resilience in Higher Education: An Integrative Review and Capacity-Sensitive Framework for Resource-Constrained Universities

Abstract

Purpose: Artificial intelligence (AI) is now embedded in teaching, assessment and administration in higher education, simultaneously widening educational opportunity and institutional cyber exposure. This review examines how AI-related and conventional cyber threats affect the continuity of digital learning, how AI can both amplify and mitigate those threats, and which integrated responses are feasible for resource-constrained universities, with Nigeria as the illustrative context. Design/Methodology/Approach: A narrative integrative review following established integrative-review guidance synthesised 60 sources published mainly between 2019 and 2026, comprising peer-reviewed studies, technical standards, policy and legal instruments, and institutional or industry reports. Sources were organised by threat mechanism, institutional vulnerability, educational consequence, response and implementation constraint, and each was classified by the type of inference it can support. Findings: Seven overlapping threat clusters were identified: AI-enhanced social engineering and identity abuse; ransomware and extortion; privacy leakage and surveillance; platform and supply-chain compromise; manipulation of AI-enabled learning systems; synthetic media and assessment-integrity attacks; and human, governance and infrastructure weaknesses. Experimental evidence shows that large language models lower the cost of persuasive phishing, while AI-text detectors and automated proctoring raise reliability and fairness concerns. AI-assisted defence offers measurable triage benefits but introduces false alarms, concept drift, privacy exposure and automation bias. Originality/Value: The proposed AI-Cybersecurity-Digital Learning Resilience (AIC-DLR) Framework adapts NIST CSF 2.0 functions to the educational mission, overlays NIST AI risk-management guidance, and treats usable, equitable recovery of teaching and assessment, rather than system restoration alone, as the protected outcome. A tiered roadmap and a set of operationally defined indicators translate the framework into testable practice. Practical Implications: Priorities for low-capacity institutions are accountable ownership, phishing-resistant identity for privileged users, tested and isolated backups, proportionate data practices, governed AI use and pre-authorised alternative learning channels. The framework is conceptual and unvalidated; comparative implementation studies are required to establish its effect on recovery time and equitable access.