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.

With the fast evolving advances in technology to create immersive learner experiences in Virtual Reality (VR), there are significant opportunities for developing immersive and engaging VR Learning Environments (VRLEs) to train neurodiverse individuals. Integrating Pedagogical Agents (PAs) in these VRLEs has the potential for supporting neurodiverse learners by providing personalized, adaptive guidance tailored to their unique needs. However, a key challenge lies in ensuring the PAs can adapt effectively to the diverse circumstances of the learners. In this paper, we present a novel VRLE viz., uSucceed that leverages a Reinforcement Learning (RL)-driven PA to provide adaptive, personalized support to enhance the training experiences by tailoring the VRLE to the needs of neurodiverse learners. By designing the RL-driven PA algorithm to use Deep Q-Network, we study its capability in guiding the actions of the student in an ongoing learning basis. Simulation experiment results of the speed, accuracy and efficiency of the RL-driven PA in a VRLE demonstrate the benefits of our approach. These results build a strong base for future dynamic VRLE research that can be programmed to adapt based on the learners' experience.

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.

Data users (researchers, scientists) in healthcare institutions need access to integrated healthcare data to conduct timely analysis of diseases to serve the right population at the right time. However, preserving patient privacy and timely access to quality healthcare data is a critical challenge. Current healthcare data governance systems are largely manual. Besides, processing process data requests is extremely slow, often taking months. To address this gap, we designed an honest-broker-based healthcare application to support data users in accessing healthcare data securely and to design a comprehendible process of data governance for data users. This study applied two iterations of a user experience (UX) evaluation of an honest broker prototype. Results show that participants found the new system promising for their research prospects. Implications suggest that technological knowledge should not be a requirement for using healthcare applications to promote broader adoption in the community. This study highlights the necessity of a process to balance the control of access to sensitive data between data providers and users as well as to educate data users on data privacy. Iterative UX studies can be a fruitful approach in gradually uncovering problems and improving the design of complex systems.

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.

Big data computation tools are vital for researchers and educators from various domains such as plant science, animal science, biomedical science and others. With the growing computational complexity of biology big data, advanced analytic systems, known as prescriptive systems, are being built using machine learning models to intelligently predict optimum computation solutions for users for better data analysis. However, lack of user-friendly prescriptive systems poses a critical roadblock to facilitating informed decision-making by users. In this paper, we detail a formative usability study to address the complexities faced by users while using prescriptive systems. Our usability research approach considers bioinformatics workflows and community cloud resources in the KBCommons framework's science gateway. The results show that recommendations from usability studies performed in iterations during the development of prescriptive systems can improve user experience, user satisfaction and help novice as well as expert users to make decisions in a well-informed manner.