TY - GEN
T1 - Cracking the Case Together: Role Perceptions in Human-AI Mystery Solving Dialogues
AU - Breckner, Karin
AU - Schönböck, Johannes
AU - Kovacs, Carrie
AU - Hirschmann, Frederik
AU - Neumayr, Thomas
AU - Reyskens, Eva
AU - Augstein, Mirjam
N1 - Publisher Copyright:
© 2026 Copyright held by the owner/author(s).
PY - 2026/4/13
Y1 - 2026/4/13
N2 - Large Language Models (LLMs) aim to mimic a natural form of human conversation, likely contributing to an anthropomorphic perception of AI in contrast to conventional human-computer interfaces. Our study explores human-AI conversations and humans’ perception of their counterpart in a collaborative mystery solving task with Anthropic’s Claude 3.5 Sonnet v2 model. We collected self-report data on participants’ perception of the interaction, measured task performance, and analyzed conversational dynamics using LLM-based emotion coding. We found that humans’ perception of AI, ranging from that of a teammate or colleague to a tool, did not necessarily impact performance in mystery solving, but correlated with aspects of the interaction itself. When participants perceived the AI as a teammate or colleague, they felt a stronger sense of team cohesion and their conversations were more collaborative, with more positive emotions. These findings may help practitioners design human-AI interfaces that foster positive interactions without endangering performance.
AB - Large Language Models (LLMs) aim to mimic a natural form of human conversation, likely contributing to an anthropomorphic perception of AI in contrast to conventional human-computer interfaces. Our study explores human-AI conversations and humans’ perception of their counterpart in a collaborative mystery solving task with Anthropic’s Claude 3.5 Sonnet v2 model. We collected self-report data on participants’ perception of the interaction, measured task performance, and analyzed conversational dynamics using LLM-based emotion coding. We found that humans’ perception of AI, ranging from that of a teammate or colleague to a tool, did not necessarily impact performance in mystery solving, but correlated with aspects of the interaction itself. When participants perceived the AI as a teammate or colleague, they felt a stronger sense of team cohesion and their conversations were more collaborative, with more positive emotions. These findings may help practitioners design human-AI interfaces that foster positive interactions without endangering performance.
KW - Collaborative Interaction
KW - Human-AI Conversation
KW - Human-AI Interaction
KW - Human-AI Teams
UR - https://www.scopus.com/pages/publications/105038798232
U2 - 10.1145/3772318.3791013
DO - 10.1145/3772318.3791013
M3 - Conference contribution
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2026 - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
A2 - Oliver, Nuria
A2 - Shamma, David A.
A2 - Candello, Heloisa
A2 - Cesar, Pablo
A2 - Lopes, Pedro
A2 - Bozzon, Alessandro
A2 - Kosch, Thomas
A2 - Liao, Vera
A2 - Ma, Xiaojuan
A2 - Artizzu, Valentino
A2 - Draxler, Fiona
A2 - Lopez, Gustavo
A2 - Reinschluessel, Anke V.
A2 - Tong, Xin
A2 - Toups Dugas, Phoebe O.
ER -