@inproceedings{schumacher-etal-2026-detecting,
title = "Detecting {AI} Impostors: How Do Middle Schoolers Identify {LLM} Agents in a Live Collaborative Setting?",
author = "Schumacher, Dan and
Rajarajan, Pragathi Durga and
Kotara, Haven and
Rendon, Roman and
Atupulazi, Kosi and
Tagare, Deepti and
Sanusi, Ismaila Temitayo and
Martin, Fred G. and
Rios, Anthony",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1676/",
pages = "38128--38150",
ISBN = "979-8-89176-459-0",
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 doppelg{\"a}ngers. 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.[{\ensuremath{<}}https://github.com/danschumac1/Detecting{\_}AI{\_}Impostors{\ensuremath{>}}]}
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<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 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.[\ensuremath<https://github.com/danschumac1/Detecting_AI_Impostors\ensuremath>]</abstract>
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%0 Conference Proceedings
%T Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?
%A Schumacher, Dan
%A Rajarajan, Pragathi Durga
%A Kotara, Haven
%A Rendon, Roman
%A Atupulazi, Kosi
%A Tagare, Deepti
%A Sanusi, Ismaila Temitayo
%A Martin, Fred G.
%A Rios, Anthony
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F schumacher-etal-2026-detecting
%X 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 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.[\ensuremath<https://github.com/danschumac1/Detecting_AI_Impostors\ensuremath>]
%U https://aclanthology.org/2026.emnlp-main.1676/
%P 38128-38150
Markdown (Informal)
[Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?](https://aclanthology.org/2026.emnlp-main.1676/) (Schumacher et al., EMNLP 2026)
ACL
- Dan Schumacher, Pragathi Durga Rajarajan, Haven Kotara, Roman Rendon, Kosi Atupulazi, Deepti Tagare, Ismaila Temitayo Sanusi, Fred G. Martin, and Anthony Rios. 2026. Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38128–38150, Budapest, Hungary. Association for Computational Linguistics.