Exploring learner acceptance of the use of virtual reality in medical education: a case study of desktop and projection-based display systems

TLDR

Advanced technologies such as human‑patient simulators, immersive VR systems, and video conferencing are widely used in medical education, and evaluating learner acceptance of these VR environments is essential for maximizing their effectiveness. The study aims to assess learner acceptance of a prototype 3D VR learning system built with high‑performance real‑time software (VR4MAX). The prototype was developed using VR4MAX and evaluated through a questionnaire survey administered to 167 university students to gauge attitudes toward VR‑based learning. Immersion and imagination features of the VR course content were found to enhance perceived usefulness and ease of use, thereby increasing learners’ behavioral intention to use VR, confirming the link between these features and adoption and offering guidance for future VR education research.

Abstract

Advanced technologies have been widely applied in medical education, including human-patient simulators, immersive virtual reality Cave Automatic Virtual Environment systems, and video conferencing. Evaluating learner acceptance of such virtual reality (VR) learning environments is a critical issue for ensuring that such technologies are used to greatest effect. This research describes the use of high performance real-time interactive software (VR4MAX) to build a prototype 3D VR learning system. A questionnaire survey was distributed to 167 university students to investigate learner attitudes toward learning via VR applications. Experimental results show that immersion and imagination features of VR-mediated course contents have a positive impact on perceived usefulness, and can also predict perceived ease of use, both of which contributors to behavioral intention of learners to use VR learning systems. Overall, this research validates the relationship between three features of VR and learners' behavioral intention to use VR learning. The results could prove helpful in guiding future research related to VR learning.