Artificial Intelligence in Technical and Vocational Education and Training: Empirical Evidence, Implementation Challenges, and Future Directions

Background: The pressures of Industry 4.0 have driven the incorporation of artificial intelligence (AI) in Technical and Vocational Education and Training (TVET) to improve the development of practical skills. Nonetheless, there is still a lack of empirical agreement regarding the effects and implementation of AI. Methods: We conducted a literature review using databases like IEEE Xplore, Scopus, ERIC, and Google Scholar, as well as grey literature from conference proceedings and UNESCO-UNEVOC reports, to find empirical studies on AI in Technical and Vocational Education and Training (TVET). Our search included keywords such as "artificial intelligence," "machine learning," and "vocational training." After screening titles/abstracts and full texts against our inclusion criteria (focused on TVET settings with measurable outcomes), we identified 11 studies published between 2021 and 2025. Each study was coded by methodology, AI technology type, vocational domain, country, and reported outcomes. Results: Evaluations in vocational trades show AI-driven simulators enhance hands-on skills. Lee et al. found that an AI-guided XR welding trainer improved welding accuracy and learning rate over traditional VR instruction. An Indonesian "AI teaching factory" boosted students' technical proficiency, efficiency, and industry readiness. Surveys indicate high student satisfaction: Malaysian polytechnic students using an AI-powered robotics trainer saw increased understanding and confidence, while TVET students with ChatGPT reported improved comprehension and engagement. Analytical studies highlight curriculum alignment: a decision analysis in the End-of-Life Vehicle sector identified AI integration, tool training, and industry partnerships as priorities for employability. Discussion: Overall, AI applications promise to enhance vocational skill acquisition and engagement. However, much of the research focuses on short-term pilots or perceptions rather than long-term outcomes. Ongoing challenges include limited infrastructure and inadequate teacher preparedness. Future efforts should prioritize rigorous, longitudinal evaluations of AI-enabled TVET interventions using standardized skill and employment outcomes metrics.