From Human Empathy to AI-Generated Empathic Performance: Conceptual Boundaries and Ethical Risks for Human-Centered AI

The increasing integration of artificial intelligence (AI) into everyday communication has complicated the conceptual boundaries of empathy by enabling systems to generate responses that appear emotionally attentive, supportive, and context-sensitive. Through a concept-centered theoretical synthesis of scholarship on human empathy, artificial empathy, human–AI interaction, and AI ethics, this article distinguishes three analytically distinct phenomena: human empathy, AI-generated empathic performance, and perceived AI empathy. In the present framework, human empathy is organized around four dimensions—cognitive understanding, affective responsiveness, self–other differentiation, and communicative expression—which are used as analytical reference points for comparing the observable capacities and conceptual limits of AI-generated empathic performance. The analysis argues that AI systems can approximate communicative features associated with empathy through emotional and contextual inference, affect-sensitive expression, adaptive response generation, and conversational continuity, but such performance should not be treated as equivalent to the relational and experiential dimensions of human empathy. Perceived AI empathy is shaped by source attribution, interactional context, and user interpretation. The resulting ethical implications concern attributional and relational risks, vulnerability, data and fairness, transparency, accountability, and governance. Operationally, the framework supports separate evaluation of system performance and perceived empathy, disclosure of AI status and inferential limits, communication of uncertainty in emotion inference, calibration of anthropomorphic and relational cues, and pathways to human support in emotionally sensitive or high-stakes contexts.