基于AI表现的人格特质可通过对话影响人类自我概念
AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations
AI总结：
本研究发现基于AI的人格特质可通过对话影响用户自我概念，揭示了AI在人机交互中的潜在影响及设计启示。
摘要：
最近基于大语言模型（LLM）的AI可以在对话中表现出可识别和测量的人格特质，以提高用户体验。然而，由于人类对自身人格特质的理解可能受互动伙伴特质的影响，潜在的风险是AI的人格特质可能塑造和偏见用户的自我概念。为了探索这一可能性，我们进行了随机行为实验。我们的结果表明，在与使用GPT-4o默认人格特质的基于LLM的AI聊天机器人讨论个人话题后，用户的自我概念与AI测量的人格特质相一致。对话时间越长，这种一致性越强。这种一致性导致用户自我概念的同质性增加。我们还观察到，自我概念一致性的程度与用户的对话愉悦感呈正相关。我们的发现揭示了AI人格特质如何通过人机对话影响用户的自我概念，突显了其中的风险和机遇。我们为开发更具责任感和伦理性的AI系统提供了重要的设计启示。
英文摘要：
Recent Large Language Model (LLM) based AI can exhibit recognizable and measurable personality traits during conversations to improve user experience. However, as human understandings of their personality traits can be affected by their interaction partners' traits, a potential risk is that AI traits may shape and bias users' self-concept of their own traits. To explore the possibility, we conducted a randomized behavioral experiment. Our results indicate that after conversations about personal topics with an LLM-based AI chatbot using GPT-4o default personality traits, users' self-concepts aligned with the AI's measured personality traits. The longer the conversation, the greater the alignment. This alignment led to increased homogeneity in self-concepts among users. We also observed that the degree of self-concept alignment was positively associated with users' conversation enjoyment. Our findings uncover how AI personality traits can shape users' self-concepts through human-AI conversation, highlighting both risks and opportunities. We provide important design implications for developing more responsible and ethical AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
