PersonaCite: 基于VoC的可访谈代理合成AI人设用于可验证的用户与设计研究
PersonaCite: VoC-Grounded Interviewable Agentic Synthetic AI Personas for Verifiable User and Design Research
- Adobe
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
PersonaCite通过检索增强的交互，将AI人设作为证据受限的研究工具，提升用户与设计研究中的可验证性与透明度。
AI中文摘要：
基于LLM和代理的合成人设日益被用于设计和产品决策中，但先前研究显示，基于提示的人设往往会产生具有说服力但不可验证的回应，这些回应会掩盖其证据基础。我们提出了PersonaCite，这是一种代理系统，通过检索增强的交互将AI人设重新定义为证据受限的研究工具。与以往依赖提示式角色扮演的方法不同，PersonaCite在每次对话回合中检索实际的客户声音（VoC） artifacts，限制回应到检索到的证据，当证据缺失时明确回避，并提供回应层面的来源归因。通过半结构化访谈和对14名行业专家的部署研究，我们识别了关于感知收益、有效性担忧和设计张力的初步发现，并提出了Persona Provenance Cards作为一种文档模式，用于在以人为核心的设计流程中负责任地使用AI人设。
英文摘要：
LLM-based and agent-based synthetic personas are increasingly used in design and product decision-making, yet prior work shows that prompt-based personas often produce persuasive but unverifiable responses that obscure their evidentiary basis. We present PersonaCite, an agentic system that reframes AI personas as evidence-bounded research instruments through retrieval-augmented interaction. Unlike prior approaches that rely on prompt-based roleplaying, PersonaCite retrieves actual voice-of-customer artifacts during each conversation turn, constrains responses to retrieved evidence, explicitly abstains when evidence is missing, and provides response-level source attribution. Through semi-structured interviews and deployment study with 14 industry experts, we identify preliminary findings on perceived benefits, validity concerns, and design tensions, and propose Persona Provenance Cards as a documentation pattern for responsible AI persona use in human-centered design workflows.
