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An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. To address these limitations, network-based approaches such as Epistemic Network Analysis (ENA) can bridge the methodological gap. By leveraging the strengths of both methods, ENA provides profound insights into healthcare expert interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used ENA to analyze the data, and compared their real-life daily activities with training experiences. A two-sample t-test with a large effect size (Cohen’s d = 1.63) indicated a significant difference between real-life and training activities. The findings suggested incorporating more empathetic training scenarios into the future design of our geriatric care simulation. The results have implications for human-computer interaction and effective nursing training. This is illustrated by presenting an example of using quantitative ethnography to analyze expert interviews with nursing assistants as caregivers and inform subsequent simulation and design processes. 

An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. To address these limitations, network-based approaches such as Epistemic Network Analysis (ENA) can bridge the methodological gap. By leveraging the strengths of both methods, ENA provides profound insights into healthcare expert interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used ENA to analyze the data, and compared their real-life daily activities with training experiences. A two-sample t-test with a large effect size (Cohen’s d = 1.63) indicated a significant difference between real-life and training activities. The findings suggested incorporating more empathetic training scenarios into the future design of our geriatric care simulation. The results have implications for human-computer interaction and effective nursing training. This is illustrated by presenting an example of using quantitative ethnography to analyze expert interviews with nursing assistants as caregivers and inform subsequent simulation and design processes. 

For serious games on education, understanding the effectiveness of different learning methods in influencing cognitive processes remains a significant challenge. In particular, limited research addresses the comparative effectiveness of serious games and videos in analyzing brain behavior for graph structure learning, which is an important part of the Science, Technology, Engineering, Math, and Computing (STEM+C) disciplinary education. This study investigates the impact of serious games on graph structure learning. For this, we compared our in-house game-based learning (GBL) and video-based learning (VBL) methodologies by evaluating their effectiveness on cognitive processes by oxygenated hemoglobin levels using functional near-infrared spectroscopy (fNIRS). We conducted a 2×1 between-subjects preliminary study with twelve participants, involving two conditions: game and video. Both groups received equivalent content related to the basic structure of a graph, with comparable session lengths. The game group interacted with a quiz-based game, while the video group watched a pre-recorded video. The fNIRS was employed to capture cerebral signals from the prefrontal cortex, and participants completed pre- and post-questionnaires capturing user experience and knowledge gain. In our study, we noted that the mean levels of oxygenated hemoglobin (delta HbO) were higher in the GBL group, suggesting the potential enhanced cognitive involvement. Our results show that the lateral prefrontal cortex (LPFC) has greater hemodynamic activity during the learning period. Moreover, knowledge gain analysis showed an increase in mean score in the GBL group compared to the VBL group. Although we did not observe statistically significant changes due to participant variability and sample size, this preliminary work contributes to understanding how GBL and VBL impact cognitive processes, providing insights for enhanced instructional design and educational game development. Additionally, it emphasizes the necessity for further investigation into the impact of GBL on cognitive engagement and learning outcomes. 

Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants.