AI-driven tools for personalized learning, feedback creation, assessment automation, and simulation-based practice are particularly pertinent to Technical and Vocational Education and Training (TVET). Still, most prior research on AI in Computer System Operations (CSO) has focused on individual sub-domains—especially cybersecurity and networking—rather than on the CSO competency chain as a whole. Through a review of research and policy literature from 2015 up to June 2026, this paper explores the range of AI-enabled educational resources developed for CSO training, with special attention to hardware installation and configuration, operating system and network setup, cybersecurity, troubleshooting, and technical writing. Four major use-cases for AI in CSO were identified in this review: intelligent tutoring systems and adaptive learning paths, virtual labs and real-time simulations, generative AI and conversational agents, and AI-supported assessment and learning analytics. Our findings confirm that AI is capable of improving learners’ access to practice opportunities, prompt and useful feedback, engagement, and competency-based advancement. Nevertheless, the successful implementation of AI for CSO depends on pedagogical suitability, educator skills, access to technology, data privacy, and mitigation of AI inaccuracies and biased outputs. We outline an AI-CSO Pedagogical Design Framework that integrates competencies and real-world learning activities; AI-powered resources; human oversight and intervention; and ongoing assessment and quality assurance in the development of CSO learning materials. We offer this framework to provide practical guidance for producing responsible, equitable, and industry-aligned CSO materials that respond to the realities of TVET in Malaysia and elsewhere.
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