同理心与人工智能聊天机器人的人际时刻差距：同理心置换理论的见解
Empathy and the Human-Moment Gaps of AI Chatbots: Insights from Empathy Displacement Theory
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中文总结 AI 辅助
研究人工智能聊天机器人在需要同理心领域的人际时刻差距问题，引入人类时刻差距框架和同理心置换理论两个概念模型，旨在开发综合理论框架，为理解人工智能介导的同理心及相关系统开发治理提供统一框架与启示。
中文摘要 AI 辅助
人工智能聊天机器人越来越多地应用于医疗、教育和客户服务等需要同理心的领域，但它们维持真实人际时刻的能力在结构上仍有限。本文引入两个相互关联的概念模型来解释和解决这一限制。人类时刻差距框架（HMGF）识别出人工智能介导互动中的三种结构性同理心缺陷：情感表面化、记忆碎片化和道德框架不匹配。同理心置换理论（EDT）解释了人工智能模拟的同理心如何在个人、关系和组织背景下逐渐替代、扭曲和取代真正的人类同理心。HMGF是EDT的因果基础。该研究是概念性和探索性的，旨在开发一个综合理论框架，而非提供实证验证。HMGF和EDT共同为理解人工智能介导的同理心提供了统一框架，为有同理心的人工智能系统的负责任开发和治理产生了可测试的命题和启示。文章得出结论，有同理心的人工智能的核心挑战不是机器能否真正关心，而是模拟关怀如何重塑人类的情感期望、人际行为和制度规范。
英文摘要
Artificial intelligence (AI) chatbots are increasingly deployed in domains where empathy is essential, including healthcare, education, and customer service. However, their capacity to sustain authentic human moments remains structurally limited. This paper introduces two interlinked conceptual models to explain and address this limitation. First, the Human-Moment Gap Framework (HMGF) identifies three structural empathy deficits in AI-mediated interaction: affective surfaceism (emotional imitation without depth), memory fragmentation (lack of relational continuity), and moral framing mismatch (efficiency prioritised over dignity). Second, the paper develops the Empathy Displacement Theory (EDT), which explains how AI-simulated empathy can progressively substitute, distort, and displace genuine human empathy across individual, relational, and organisational contexts. HMGF serves as the causal foundation of EDT by demonstrating how technical and moral deficiencies in chatbot design may evolve into broader social and institutional consequences. The study is conceptual and exploratory, aiming to develop an integrative theoretical framework rather than provide empirical validation. Together, HMGF and EDT provide a unified framework for understanding AI-mediated empathy, generating testable propositions and implications for the responsible development and governance of empathetic AI systems. The paper concludes that the central challenge of empathetic AI is not whether machines can genuinely care, but how simulated care reshapes human emotional expectations, interpersonal behaviour, and institutional norms.
