Chia-Ching Lin

Acknowledging the constraints inherent in conventional teaching approaches for cultivating empathy, the present study sought to assess the efficacy of an immersive Virtual Reality (VR) training intervention tailored for social work students. Utilizing a quasi-experimental design with pre-test and post-test measures alongside a control group, the research involved 82 participants enrolled in social work programs. The experimental cohort  participated in a 15-minute immersive VR simulation portraying a high-risk family scenario, whereas the control cohort  engaged with an equivalent text-based case study. Empathy levels were quantified using the Interpersonal Reactivity Index (IRI). Analysis of Covariance (ANCOVA) indicated that participants in the VR condition exhibited significantly greater enhancements in Perspective-Taking (PT) and Empathic Concern (EC) relative to those in the control condition  Additionally, the degree of experienced presence within the VR environment was positively associated with an increase in empathy. These results offer compelling empirical evidence supporting the integration of VR as an effective pedagogical instrument, promoting an embodied cognition framework within social work education and providing a validated module for the development of innovative curricular designs. Received: September 20, 2025 Accepted: October 7, 2025 DOI: https://dx.doi.org/10.17654/0973700626004

The global demographic transition toward an aging population presents unprecedented challenges for long-term care systems, with critical workforce shortages affecting $92 \%$ of nursing homes and $70 \%$ of assisted living facilities. This mixed-methods study investigates the effectiveness of AI-enhanced human-machine collaboration in improving long-term care service efficiency and quality. Following PRISMA and STROBE guidelines, we conducted a systematic review of 105 studies and controlled trials across 218 facilities ( 94 intervention, 124 control) over 18 months. The AI-enhanced system analyzed 150 daily clinical data points per patient, providing real-time alerts for condition changes, fall risk assessment, and medication monitoring. Results demonstrated significant improvements in $89 \%$ of quality measures, including a $9 \%$ reduction in major falls ( $p=0.034$ ), 22\% decrease in ADL dependency ( $p DOI: https://dx.doi.org/10.17654/0973700626007 Received: October 18, 2025 Accepted: November 3, 2025;