AI-VR Synergistic Technology in Virtual Simulation Training for Job-seeking Skills: Personalized Learning, Multimodal Interaction, and Adaptive Assessment

Information from the abstract

This systematic review examines the integration of artificial intelligence and virtual reality in virtual simulation training for job-seeking skills, with attention to personalised learning, multimodal interaction, and adaptive assessment. The review synthesises 31 peer-reviewed sources drawn from five academic databases (Google Scholar, Scopus, ACM Digital Library, IEEE Xplore, Web of Science), covering literature on virtual reality training, artificial intelligence-supported personalised learning, multimodal learning analytics, immersive learning theory, and educational assessment. The review is framed around the need to improve conventional virtual training systems, which often rely on fixed scenarios, limited interaction patterns, and insufficiently individualized feedback. The findings suggest that artificial intelligence may extend the educational value of virtual reality by supporting learner modelling, adaptive content delivery, real-time feedback, and process-oriented assessment. In job-seeking skills training, these functions may help learners practise interview communication, professional behaviour, and workplace-related interaction in simulated environments that can be adjusted to individual performance and learning needs. The reviewed literature also indicates that immersive learning is not automatically effective; learning outcomes appear to depend on instructional design, cognitive load management, scaffolding, and the meaningful use of feedback. Multimodal data, including behavioural, speech, gaze, and interaction indicators, may provide richer evidence for diagnosing learner progress, although privacy, fairness, transparency, and accessibility remain important concerns. Overall, the review indicates that AI-VR integration has potential value for vocational education and job-seeking skills training, but current evidence remains limited by the scarcity of direct empirical studies on fully integrated AI-VR systems; many conclusions are therefore based on theoretical synthesis from related domains rather than direct empirical confirmation. Further research should use rigorous designs, authentic educational settings, and long-term outcome measures to examine whether such systems improve transferable employment-related skills.

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Related topics: Virtual Reality Applications and Impacts · Intelligent Tutoring Systems and Adaptive Learning · Educational Games and Gamification