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TRUVACE RECORD VERSION
record: TRV-2026-1848
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T18:14:14.641221Z
status: published
lens: p_space
sector: crime
headline: Artificial Intelligence, Cybersecurity and Digital Learning Resilience in Higher Education: An Integrative Review and Capacity-Sensitive Framework for Resource-Constrained Universities
dek: 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…
gain_title: (none)
problem_title: Large language models lower the cost of persuasive phishing, and AI-text detectors and automated proctoring raise reliability and fairness concerns, threatening continuity of digital learning in universities.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Large language models lower the cost of persuasive phishing, and AI-text detectors and automated proctoring raise reliability and fairness concerns, threatening continuity of digital learning in universities.
problem_evidence: large language models lower the cost of persuasive phishing | AI-text detectors and automated proctoring raise reliability and fairness concerns
quick_read: A narrative integrative review synthesised 60 sources, mostly published between 2019 and 2026, to examine how AI-related and conventional cyber threats disrupt digital learning in higher education. Nigeria serves as the illustrative context, and the review identified seven overlapping threat clusters, from AI-enhanced social engineering to weaknesses in governance and infrastructure.

The work matters because AI is embedded in teaching, assessment and administration, which expands both educational opportunity and cyber exposure, and low-capacity universities have limited means to respond. The proposed framework is conceptual and unvalidated, so whether it shortens recovery time or improves equitable access remains untested.
limitation: The framework is conceptual and unvalidated, and comparative implementation studies are needed to establish its effect on recovery time and equitable access.
tag: Evidence-backed problem
key_points: Seven threat clusters were identified, including ransomware and extortion, privacy leakage and surveillance, and supply-chain compromise. | Experimental evidence shows large language models make persuasive phishing cheaper to produce. | AI-assisted defence brings triage benefits but also false alarms, concept drift, privacy exposure and automation bias. | The AIC-DLR Framework adapts NIST CSF 2.0 and overlays NIST AI risk-management guidance. | The protected outcome is usable, equitable recovery of teaching and assessment, not only system restoration.
rundown: The review used a narrative integrative method covering 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 classified by the type of inference each can support.

Threat clusters also include manipulation of AI-enabled learning systems, synthetic media and assessment-integrity attacks, and identity abuse. AI-text detectors and automated proctoring are flagged for reliability and fairness concerns.

For low-capacity institutions, the priorities 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. A tiered roadmap and operationally defined indicators are meant to make the framework testable.
sources:
- peer_reviewed | Asian Journal of Research in Computer Science | https://doi.org/10.9734/ajrcos/2026/v19i10925 | 2026-10-17
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