发表机构
University of Texas at San Antonio（圣安东尼奥德克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过自研协作社交推理游戏DoppelBot，对中学生展开实验，探究其识别AI冒充者的策略、检测准确率变化规律及相关隐私反思，并发布了相关匿名数据集。
AI 中文摘要
大语言模型（LLM）可模仿人类写作方式，这引发了社交场景中关于冒充、信任及检测的担忧，这些担忧对青少年尤为重要——他们频繁使用生成式AI，却可能难以识别AI。我们推出DoppelBot，这是一款协作社交推理游戏，旨在研究年轻人如何检测和应对AI冒充行为。通过对中学生的研究，我们探究DoppelBot是否能促使人们反思隐私与冒充问题、当智能体更具个性化时重复接触会如何影响AI检测准确率，以及学生采用哪些策略识别AI“分身”。我们发现，学生的检测准确率随时间提升，驱动因素是从依赖语言线索转向利用共享的社交与情境信号；学生还展现出对AI局限性（如具身性）的理解，并反思了数据隐私等更广泛问题。为支持未来研究，我们发布了匿名化的游戏文本记录与投票行为数据集。
英文摘要
LLMs can imitate how people write, which raises concerns about impersonation, trust, and detection in social settings. These concerns are especially important for adolescents, who use generative AI frequently but may struggle to recognize it. We introduce \textit{DoppelBot}, a cooperative social deduction game designed to study how young people detect and respond to AI impersonation. Through studies with middle schoolers, we investigate whether a DoppelBot prompts reflection on privacy and impersonation, how repeated exposure affects AI-detection accuracy as agents become more personalized, and which strategies students use to identify AI doppelgängers. We find that students' detection accuracy improves over time, driven by a shift from relying on linguistic cues to leveraging shared social and contextual signals. Students also demonstrated an understanding of AI limitations such as embodiment and reflected on broader issues such as data privacy. To support future research, we release an anonymized dataset of game transcripts and voting behavior.
CommentsAccepted to EMNLP 2026 Main
