AI-Enabled Empathy and Resilience for Elderly Care Education

Abstract

Abstract Empathy and resilience in elderly care are not fixed traits but clinical competencies that can be systematically trained. This chapter introduces an artificial intelligence (AI)-driven “emotional flight simulator” to help caregivers safely practice high-stress interactions—such as managing dementia-related agitation, resolving family–facility conflicts, and conducting end-of-life conversations—within a repeatable, feedback-rich environment. Grounded in humanistic principles (Rogers, Kitwood) and structured communication methods (SPIKES/NURSE), the authors translate compassion into observable, coachable behaviors: emotion labeling, pressure-tolerant de-escalation, and vocal self-regulation. A closed-loop pedagogical model (scenario → sensing → analysis → coaching → re-practice) is detailed, where conversational analytics—interruption frequency, open-question ratio, empathy timing—enable personalized, next-turn guidance without reducing learners to scores. A multi-tier evaluation framework connects simulated skill gains to real-world performance, family feedback, and caregiver well-being. Implementation emphasizes ethical guardrails: data minimization, privacy-by-design, cultural localization, and a strict ban on emotion surveillance. This approach offers educators and care institutions a scalable blueprint for fostering dignified, resilient, and empathically skillful elderly care.

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