Disentangling authorship and disclosure in the perception of empathic AI

Abstract: Artificial intelligence systems increasingly generate text intended to provide social and emotional support, yet whether users perceive that text as genuinely empathic remains unclear. It is therefore relevant to understand how users perceive empathic qualities in such content. We examined differences in perceived empathy signals between human-written and large language model (LLM)-generated relationship advice, and the influence of authorship labels. Across two preregistered experiments (Study 1: n = 641; Study 2: n = 500), participants rated advice texts on overall quality and perceived cognitive, emotional, and motivational empathy. Multilevel models accounted for the nested rating structure. LLM-generated advice was consistently perceived as higher in overall quality, cognitive empathy, and motivational empathy. Evidence for a widely reported negativity bias toward AI-labelled content was limited: it emerged only for overall quality in Study 2, and not for any empathy component in either study. Emotional empathy showed no consistent source advantage. Individual differences in AI attitudes modestly influenced judgments but did not alter the overall pattern. These findings suggest that perceptions of empathic communication are primarily driven by linguistic features rather than authorship beliefs, extending prior work by disentangling actual from believed authorship and by evaluating empathy at the level of its separate components, with implications for the design of AI-mediated support systems.