Linguistic humanization in conversational AI: a systematic–narrative review of humor, empathy, tone, and imperfection in relational communication

Abstract

As conversational AI systems and virtual humans increasingly mediate social, educational, and therapeutic interactions, their linguistic design has become central to how users construct meaning, establish trust, and experience emotional connection. This review synthesizes interdisciplinary research on linguistic humanization in conversational AI, focusing on how humor, empathy, tone, and imperfection operate as relational strategies that shape affective and cognitive responses. Drawing on frameworks, such as the Media Equation and Social Response Theory, this analysis integrates insights from communication, psychology, and human–computer interaction through a systematic–narrative hybrid approach. Across 136 studies, the findings reveal that linguistic cues not only enhance social presence and authenticity but also mediate trust and intimacy through mechanisms of affective mediation and social attribution. By identifying these mechanisms, the review advances the conceptual understanding of synthetic relationality—the emergence of emotional reciprocity between humans and AI agents. This discussion highlights design implications for creating ethically grounded, emotionally intelligent conversational systems, emphasizing context sensitivity, cultural adaptation, and transparency in human–AI communication. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.