Human attribution of empathic behaviour to AI systems

2026-02-19

AI Summary

This study investigates how users perceive empathic qualities in AI-generated content, with a focus on differences between human- and large language model (LLM)-generated advice in socioemotional support contexts and the influence of authorship labels. Through two preregistered experiments employing multilevel linear modeling to analyze nested rating data, the research systematically evaluates cognitive, affective, and motivational dimensions of empathy. Results indicate that LLM-generated content receives higher ratings for overall quality, cognitive empathy, and motivational empathy, while no significant difference emerges in affective empathy across sources. Empathic perceptions are primarily driven by linguistic features rather than beliefs about author identity, and the negative impact of an AI label is limited; individual attitudes toward AI only weakly moderate judgments. These findings challenge the assumption of a pervasive negative bias against AI-generated content.

Technology Category

• Humans and AI: Emotional Intelligence
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• empathy attribution
• AI perception
• human-AI comparison
• authorship labeling
• empathic communication

Innovation

• empathy perception
• large language models
• AI authorship
• social support
• human-AI interaction