Human attribution of empathic behaviour to AI systems

TL;DR

This study investigates how people perceive empathy in AI-generated versus human-written advice, finding that linguistic features primarily influence perceptions of empathy, with minimal impact from authorship labels or individual AI attitudes.

Contribution

It provides empirical evidence that perceived empathy in AI content is mainly driven by language, challenging assumptions about the influence of authorship labels.

Findings

LLM advice rated higher in quality and empathy

Limited evidence for negativity bias towards AI labels

Perceptions driven mainly by linguistic features

Abstract

Artificial intelligence systems increasingly generate text intended to provide social and emotional support. Understanding how users perceive empathic qualities in such content is therefore critical. 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. 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, with implications for the design of AI-mediated support systems.