Social interactions promote well-being, yet barriers like geographic distance, time limitations, and mental health conditions can limit face-to-face interactions. Emotionally responsive AI systems, such as chatbots, offer new opportunities for social and emotional support, but raise critical questions about how empathy is perceived and experienced in human-AI interactions. This study examines how empathy is evaluated in AI-generated versus human responses. Using personal narratives, we explored how persona attributes (e.g., gender, empathic traits, shared experiences) and story qualities affect empathy ratings. We compared responses from standard and fine-tuned AI models with human judgments. Results show that while humans are highly sensitive to emotional vividness and shared experience, AI-responses are less influenced by these cues, often lack nuance in empathic expression. These findings highlight challenges in designing emotionally intelligent systems that respond meaningfully across diverse users and contexts, and informs the design of ethically aware tools to support social connection and well-being.

翻译：社交互动能促进身心健康，然而地理距离、时间限制和心理健康状况等障碍可能限制面对面的交流。情感响应型AI系统（如聊天机器人）为社会和情感支持提供了新的机遇，但也引发了关于人机互动中共情如何被感知和体验的关键问题。本研究探讨了AI生成回应与人类回应中的共情评价方式。通过使用个人叙事，我们探究了人物属性（如性别、共情特质、共同经历）和故事品质如何影响共情评分。我们将标准及微调AI模型的回应与人类判断进行了比较。结果显示，虽然人类对情感生动性和共同经历高度敏感，但AI回应受这些线索的影响较小，且在共情表达上往往缺乏细微差别。这些发现凸显了设计能够跨不同用户和情境作出有意义回应的情感智能系统所面临的挑战，并为设计支持社交连接和身心健康的伦理意识工具提供了参考。