VR-based health and safety training in various high-risk engineering industries: a literature review

Abstract

This article provides a critical review of the current studies in VR-based health and safety training, assessment techniques, training evaluation, and its potential to improve the training evaluation outcomes in various high-risk engineering industries. The results of this analysis indicate the breadth of VR-based applications in training users on a combination of topics including risk assessment, machinery, and/or process operation in various industries. Data showed that the use of fully immersive VR increased significantly due to the improvements in hardware, display resolution, and affordability. Most of the articles used external assessment to measure the changes in the satisfaction and the declarative knowledge of trainees as these are easier to implement, while some articles started to implement internal assessment that provides an automated assessment capable of measuring complex skills. The results of the study also suggest that it has the potential to improve the training evaluation outcomes compared to traditional training methods. The findings from this study help practitioners and safety managers by providing a training design framework that may be adopted to optimise the condition of a VR-based training.

Health and safety induction training in the construction industry: a review

“…This type of analysis allows the researcher to extract the information directly from the data without the need of prior relevant theories or when the knowledge is fragmented (Horani, 2023). In this study, inductive content analysis was used with the prompts introduced by the research question (Toyoda et al, 2022) since the study purpose was not to test previous theories. Furthermore, the identified articles were used to achieve the study objectives: To identify H&S induction training main research areas, to establish how H&S induction training can be used to contribute to H&S culture and to propose future research directions.…”

“…An extensive review was conducted to review literature on H&S induction training within the construction industry. This approached was also used recently by Toyoda et al (2022) on current studies in virtual reality-based H&S training. A systematic literature review using meta-analysis or bibliometric was not used to analyse the review results.…”

“…Hence, ten articles were retained for final analysis. To improve the quality assessment, the current study adapted the five questions to rate the articles as proposed by Khallaf and Khallaf (2021) and later used by Toyoda et al (2022). Five questions: Do the authors state the research aims clearly?…”

The Impact of the Use of VR on the Effectiveness of Training in the Field of Occupational Health and Safety and Ergonomics

“…There is also an increase in interest in the use of VR elements in occupational health and safety and ergonomics training. The advantages of implementing this solution include [4,5] On the other hand, however, there are general disadvantages of using VR in occupational safety training in terms of: high costs of the solution, high organizational requirements, the possibility of employees experiencing discomfort related to the use of goggles (e.g. eye and headache), the need to provide a trainer with extensive knowledge about VR, the need to allocate time during training to explain how VR technology works [6].…”

Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

“…AIEd takes advantage of immense data processing and analytics to enable human-like cognition and functionalities, which has become a field of scientific research to improve online education or blended learning. Particularly, AIEd has significantly promoted the emergence of new functionalities in education such as learning performance prediction (Jiao et al, 2022 ; Liao, et al, 2022 ), learning path recommendation (Nabizadeh et al, 2020 ), teaching strategy optimization (Taheri et al, 2021 ; Toyoda et al, 2022 ), etc. AI-enabled academic performance prediction is one of the cutting-edge applications in AIEd, which assists in identifying the students that tend to fail, establishing student-centered learning pathways to improve learning effectiveness, and optimizing instructional design and development (Mozer et al, 2019 ; Nabizadeh et al, 2020 ; Taheri et al, 2021 ).…”