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Introduction: Variations in skin tone can significantly alter the appearance of symptoms such as rashes or bruises. Unfortunately, previous works utilizing Augmented Reality (AR) in simulating visual symptoms have often failed to consider this critical aspect, potentially leading to inadequate training and education. This study seeks to address this gap by integrating generative artificial intelligence (AI) into the AR filter design process. Methods: We conducted a 2 × 5 within-subjects study with second-year nursing students (N = 117) from the University of Florida. The study manipulated two factors: symptom generation style and skin tone. Symptom generation style was manipulated using a filter based on a real symptom image or a filter based on a computer-generated symptom image. Skin tone variations were created by applying AR filters to computer-generated images of faces with five skin tones ranging from light to dark. To control for factors like lighting or 3D tracking, 101 pre-generated images were created for each condition, representing a range of filter transparency levels (0-100). Participants used visual analog scales on a computer screen to adjust the symptom transparency in the images until they observed image changes and distinct symptom patterns. Participants also rated the realism of each condition and provided feedback on how the symptom style and skin tone impacted their perceptions. Results: Students rated the symptoms displayed by the computer-generated AR filters as marginally more realistic than those displayed by the real image AR filters. However, students identified symptoms earlier with the real-image filters. Additionally, SET-M and Theory of Planned Behavior questions indicate that the activity increased students' feelings of confidence and self-efficacy. Finally, we found that similar to the real world, where symptoms on dark skin tones are identified at later stages of development, students identified symptoms at later stages as skin tone darkened regardless of cue type. Conclusion: This work implemented a novel approach to develop AR filters that display time-based visual cues on diverse skin tones. Additionally, this work provides evidence-based recommendations on how and when generative AIbased AR filters can be effectively used in healthcare education. [ABSTRACT FROM AUTHOR]

The immersion of virtual reality (VR) can impact user perceptions in numerous forms, even racial bias and embodied experiences. These effects are often limited to head-mounted displays (HMDs) and other immersive technologies that may not be inclusive to the general population. This paper investigates racial bias and embodiment on a less immersive but more accessible medium: desktop VR. A population of participants (n = 158) participated in a desktop simulation where they embodied a virtual avatar and interacted with virtual humans to determine if desktop embodiment is induced and if there is a resulting effect on racial bias. Our results indicate that desktop embodiment can be induced at low levels, as measured by an embodiment questionnaire. Furthermore, one's implicit bias may actually influence embodiment, and the experience and perceptions of a desktop VR simulation can be improved through embodied avatars. We discuss these findings and their implications in the context of stereotype activation and existing literature in embodiment. [ABSTRACT FROM AUTHOR]

Effective communication among healthcare professionals is critical to delivering safe, high-quality patient care. One important real-world skill that nursing students must acquire is generating accurate handoff communication reports. The central aim of this study was to build, assess, and revise a virtual experience simulation that allows nursing students to observe a standardized clinical situation in an immersive environment and then practice the situation-background-assessment-recommendation communication method. This between-groups experimental study, which was modified in light of COVID-19 concerns, evaluated how well 69 prelicensure nursing students understood a handoff communication report after viewing a virtual human patient and nurse interact during a triage assessment scenario. Results indicate student comprehension levels did not differ based on which of two metacognitive learning strategies was used. Participants in both study groups were able to accurately complete a situation-background-assessment-recommendation instrument based on the virtual experience. Further, they believed that watching a virtual nurse perform a triage assessment would help them perform one themselves in a similar virtual environment. There was also an unexpected study finding related to patient safety within the context of the simulation. This finding warrants further research that will lead to revision of the virtual experience used to train future nurses., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)