Introducing Mental Workload Assessment for the Design of Virtual Reality Training Scenarios
Open accessAbstract
Training is one of the major use cases of Virtual Reality (VR) due to the flexibility and reproducibility of VR simulations. However, the use of the user’s cognitive state, and in particular mental workload (MWL), remains largely unexplored in the design of training scenarios. In this paper, we propose to consider MWL for the design of complex training scenarios involving multiple parallel tasks in VR. The proposed approach is based on the assessment of the MWL elicited by each potential task configuration in the training application. Following the assessment, the resulting model is then used to create training scenarios able to modulate the user’s MWL over time. This approach is illustrated by a VR flight training simulator based on the Multi-Attribute Task Battery II, which solicits different cognitive resources, able to generate 12 different tasks configurations. A first user study (N = 38) was conducted to assess the MWL for each task configuration using self-reports and performance measurements. This assessment was then used to generate three training scenarios in order to induce different levels of MWL over time. A second user study (N = 14) confirmed that the proposed approach was able to induce the expected mental workload over time for each training scenario. These results pave the way to further studies exploring how MWL modulation can be used to improve VR training applications.
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One publication reported that the paper-based assessment in between the IVR training broke immersion.
“One publication reported that the paper-based assessment in between the IVR training broke immersion [58]”
From A systematic review of immersive virtual reality for industrial skills training (2021) · cites this paper as [58]
Section 4.6.3 (page unavailable) says participants answered questions after each VR scenario, supporting the presence of assessments between scenarios. It does not say those assessments were paper-based or that they broke immersion; searches for paper-based questionnaires and interruptions to immersion found no such report. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThese publications measured the change in knowledge retained after one week and two weeks, while one publication also measured perceived levels of enjoyment.
“These publications measured the change in knowledge retained after one week [66, 94] and two weeks [10, 53, 58, 67, 68, 81, 106], while one publication also measured perceived levels of enjoyment [66].”
From A systematic review of immersive virtual reality for industrial skills training (2021) · cites this paper as [58]
Sections 4.6.3–4.6.4 describe ratings of perceived difficulty after VR scenarios, not tests of knowledge retained one or two weeks later. Searches for week-based retention and perceived enjoyment found no passage addressing either measure in our copy. (LLM, conf=0.76)
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References 71 · Cited by 37
Research works printed but not linked: 8
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- Authors
- Luong, T., Argelaguet, F., Martin, N., & Lecuyer, A.
- Affiliations
- Institut de Recherche Technologique B-com (FR)
- Centre National de la Recherche Scientifique (FR)
- Venue
- 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 662-671
- ISSN
- 1087-8270 (ISSN-L)
- OpenAlex
- W4235432271
- Cited by
- 37
- References
- 71
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 6 · 2023: 8 · 2022: 8 · 2021: 4 · 2020: 2
- FWCI
- 7.75
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
- Keywords
- mental workload, virtual reality training, Multi-Attribute Task Battery, cognitive load, flight simulator training, cognitive resources
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