Problem
Research questions and friction points this paper is trying to address.
AI Impostors
Middle Schoolers
LLM Agents
Detection
Social Settings
Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?
🤖 AI Summary
研究通过设计名为DoppelBot的游戏，探讨中学生如何识别和应对AI模仿者，发现学生的识别准确性随时间提高，并从依赖语言线索转向利用社交和情境信号。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
DoppelBot
social deduction game
AI impersonation detection
adolescents
shared social and contextual signals
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