The Technology Acceptance Model (TAM) has been used extensively to understand technology adoption in the context of virtual reality (VR). The model includes external variables that are important drivers of attitudes towards adopting technology. In this mixed-methods study, we assessed the effects of cognitive engagement and individual dimensions of cognitive load (CL) on the attitudes driving the intention to use an AI-supported VR system for nursing students' patient management training: perceived usefulness (PU) and perceived ease of use (PEOU). The participants were a group of nursing students from a university in the Midwestern United States. We also explored interview data to understand the participants' perceptions about the resulting factors. The quantitative results showed that engagement and PEOU are significant predictors of PU, and so are frustration (one of the dimensions of CL) and engagement with PEOU. Interview data revealed that participants' frustrations did not always have a negative effect. They generally enhanced their engagement by making the scenarios feel realistic and valuable for skill development.

This study investigates iVRLab, an immersive virtual reality (VR) microfabrication training system, and its influence on college students' self-efficacy and interest in microelectronics during a one-hour workshop held within a 1.5-day training camp. iVRLab simulates photolithography cleanroom operations, providing hands-on virtual experiences that merge theory with practice. A pre-post analysis revealed a significant rise in self-efficacy (M = 4.11 to 4.37, p = 0.0066), demonstrating the system's effectiveness in building confidence. Although interest only showed slight gains, high baseline scores (3.6-3.7 on a 4-point scale) indicate a ceiling effect. Correlations among engagement, embodiment (r = 0.91, p < 0.001), and immersion (r = 0.60, p < 0.01) underscore the value of active, embodied VR learning. Qualitative feedback further highlighted iVRLab's immersive realism and its capacity to spark curiosity about microfabrication careers. These findings suggest that VR training can effectively boost self-efficacy in microelectronics, reinforcing students' enthusiasm in an informal learning setting and indicating the potential broader adoption across STEM fields.

Cave Automatic Virtual Environment (CAVE) is a virtual reality (VR) environment that has not been fully studied due to its high cost and complexity in system integration. Previous CAVE-related studies mainly focused on comparing its effectiveness with other learning media, such as textbooks, desktop VR, or head-mounted display (HMD) VR. In this study, through the utilization of CAVE in a meteorology class, we concentrated on CAVE itself, measured how CAVE impacted learners' learning outcomes before and after using CAVE in an actual ongoing undergraduate-level class, and investigated how learners perceived their learning experiences. Quantitative data were collected to examine the students' knowledge acquisition and learning experience. We also triangulated the quantitative results with qualitative data from the interviews regarding learners' perceptions of the CAVE-enabled class and their knowledge mastery. The results indicated that their learning outcomes increased through learning with CAVE and that their perceptions of immersion, presence, and engagement significantly correlated with each other. The interview results showed a great fondness of and satisfaction with the learning experience, group collaboration, and effectiveness of the CAVE-enabled class from the learners. We also learned that the learners' learning experiences in CAVE could be further improved if we provided them with more learner-environment interaction, offered them a better sense of immersion, and reduced cybersickness. Implications of these findings are discussed.

Engagement has been traditionally linked to presence in desktop-based virtual reality learning environments. Although several studies have been performed to determine other factors affecting cognitive engagement, the role of cognitive load as a factor of student's engagement in desktop-based virtual reality (VR) has received little attention in the literature. The main purpose of this study was to explain if individual dimensions of cognitive load (mental demand, effort, and frustration level) can be used in addition to factors like presence and self-efficacy to predict student's cognitive engagement. The results of the study confirmed presence and self-efficacy as significant predictors of student's engagement. Also, a three-step hierarchical regression analysis revealed that two of the three individual dimensions of cognitive load (effort and frustration level) were also significant predictors of student's engagement.