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SCIENCE ARTICLE
Integrating VR, AR and AI into corporate employee training: A study of mixed methods towards
personalized learning design
1
Institute of Industrial Engineering and Management, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, Slovak Republic
2
Department of Logistics and Information Systems, University of Zielona Gora, Poland
Submission date: 2025-12-04
Final revision date: 2026-01-29
Acceptance date: 2026-02-24
Online publication date: 2026-04-01
Publication date: 2026-03-26
Corresponding author
Krzysztof Witkowski
Department of Logistics and Information Systems, University of Zielona Gora, ul. Podgorna 50, 65-246, Zielona Gora, Poland
Department of Logistics and Information Systems, University of Zielona Gora, ul. Podgorna 50, 65-246, Zielona Gora, Poland
Management 2026;(1):70-95
KEYWORDS
JEL CLASSIFICATION CODES
O33
J24
M53
M15
TOPICS
ABSTRACT
Research background and purpose:
Digital transformation and the emergence of Industry 4.0 and 5.0 intensify the need for innovative, efficient, and human-centred approaches to employee training in industrial enterprises. Immersive technologies—virtual reality (VR), augmented reality (AR), and artificial intelligence (AI)—offer new possibilities for enhancing experiential learning, personalization, and workplace safety. The main purpose of this article is to present a comprehensive model for integrating VR, AR, and AI into corporate employee training, based on an extensive literature review and empirical research conducted in Slovakia and the Czech Republic.
Design/methodology/approach:
The study applies a mixed-methods design combining theoretical analysis with empirical inquiry. The theoretical component synthesizes current knowledge on immersive learning, AI-supported personalization, and instructional design principles. The empirical part consists of a quantitative survey carried out between 2024 and 2025 among 106 industrial enterprises, supplemented by pilot testing in Slovak and Czech companies. The survey examined organizational readiness, perceived benefits and barriers, and existing use cases of VR, AR, and AI in employee training programs.
Findings:
Results indicate that although technological awareness is high, actual adoption of immersive tools in industrial training remains limited. Companies recognize substantial benefits, including the ability to simulate risk-intensive tasks safely, increase engagement, reduce training time, and support personalized learning through AI-driven analytics and adaptive content. However, several barriers hinder wider implementation: high equipment and development costs, limited hardware availability, insufficient digital competencies among employees, and a lack of systematic training strategies. Based on the research outcomes, a structured framework for designing personalized and immersive training programs was developed.
Value added and limitations:
The main value added by this study is the integration of theoretical insights with practical findings, resulting in a comprehensive framework to guide organizations in adopting immersive and intelligent technologies. The conclusions can support strategic decision-making in HR development, technological investment, and the optimization of training processes. The primary limitation lies in the composition of the survey sample, which includes predominantly medium and large industrial enterprises from two countries, potentially reducing representativeness. Future research should extend the sample, incorporate longitudinal evaluation, and analyse industry-specific applications of VR, AR, and AI.
Digital transformation and the emergence of Industry 4.0 and 5.0 intensify the need for innovative, efficient, and human-centred approaches to employee training in industrial enterprises. Immersive technologies—virtual reality (VR), augmented reality (AR), and artificial intelligence (AI)—offer new possibilities for enhancing experiential learning, personalization, and workplace safety. The main purpose of this article is to present a comprehensive model for integrating VR, AR, and AI into corporate employee training, based on an extensive literature review and empirical research conducted in Slovakia and the Czech Republic.
Design/methodology/approach:
The study applies a mixed-methods design combining theoretical analysis with empirical inquiry. The theoretical component synthesizes current knowledge on immersive learning, AI-supported personalization, and instructional design principles. The empirical part consists of a quantitative survey carried out between 2024 and 2025 among 106 industrial enterprises, supplemented by pilot testing in Slovak and Czech companies. The survey examined organizational readiness, perceived benefits and barriers, and existing use cases of VR, AR, and AI in employee training programs.
Findings:
Results indicate that although technological awareness is high, actual adoption of immersive tools in industrial training remains limited. Companies recognize substantial benefits, including the ability to simulate risk-intensive tasks safely, increase engagement, reduce training time, and support personalized learning through AI-driven analytics and adaptive content. However, several barriers hinder wider implementation: high equipment and development costs, limited hardware availability, insufficient digital competencies among employees, and a lack of systematic training strategies. Based on the research outcomes, a structured framework for designing personalized and immersive training programs was developed.
Value added and limitations:
The main value added by this study is the integration of theoretical insights with practical findings, resulting in a comprehensive framework to guide organizations in adopting immersive and intelligent technologies. The conclusions can support strategic decision-making in HR development, technological investment, and the optimization of training processes. The primary limitation lies in the composition of the survey sample, which includes predominantly medium and large industrial enterprises from two countries, potentially reducing representativeness. Future research should extend the sample, incorporate longitudinal evaluation, and analyse industry-specific applications of VR, AR, and AI.
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| eISSN: | 2299-193X |
| ISSN: | 1429-9321 (1997-2019) |
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