Compassion in Crisis: Nudging Prosocial Behavior Through LLM Conversational Agents

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Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Digital humanitarian systems increasingly integrate AI-driven conversational agents to support crisis response, yet how such systems shape prosocial behavior remains poorly understood. In particular, prior work treats compassion as a uniform construct, overlooking how distinct compassion framings influence behavioral outcomes. This study investigates whether and how different forms of compassion embedded within large language model powered conversational agents (LLM-CAs) nudge prosocial behavior. Drawing on four theoretically grounded compassion types, proximal, distal, universal, and relative, we plan to instruction tune LLM-CAs to express these types as well as a non compassionate control. We will conduct a controlled pretest posttest experiment with over 500 participants in a simulated hurricane crisis scenario. Participants will interact with an assigned LLM-CA and make behavioral choices, including donations, engaging in digital volunteerism, and sharing crisis information. By examining the behavioral effects of compassion framings, this study aims to advance the crisis informatics literature.

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