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With the rise of the aging population, the demand of care increases yet the turnover rate of caregivers for geriatric patients has increased over the past few decades. While many factors contribute to the caregivers’ turnover rate, inadequate training and lack of communication skills and relationship-building skills are vital issues. Technology can help in providing training for soft skills. Embodied Virtual Human (VH) assistants and Augmented Reality (AR) can be a key method to train caregivers and to improve their experience in interacting with older adults, and their perception of the interaction. We designed and developed an immersive simulation in AR where caregivers interact with an embodied VH in two different conditions, i.e., first with an unaware VH and then with a VH having simulated awareness achieved through human-in-the-loop. We conducted a study with caregivers of older adults to evaluate the usability and technology acceptance of our system. The findings suggest that the majority of the participants found the system acceptable, and rated the system as user-friendly and easy to use. 

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. Network-based approaches, such as quantitative ethnography (QE), can bridge this methodological gap. By leveraging the strengths of both methods, QE provides profound insights into need-finding interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used QE to analyze the data, and compared their daily activities in real life 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 human factors. This is illustrated by presenting an example of using QE to analyze expert interviews with nursing assistants as caregivers to inform subsequent design processes.