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DTSTAMP:20251016T135138Z
LOCATION:Grand B
DTSTART;TZID=America/Chicago:20251014T133000
DTEND;TZID=America/Chicago:20251014T135000
UID:HFESAM_ASPIRE 2025_sess205_LECT342@linklings.com
SUMMARY:Less Trust, More Mirroring Words: Lexical Alignment as a Response 
 to Uncertainty in Human-AI Teams
DESCRIPTION:Mengyao Li and Emanuel Rojas (Georgia Institute of Technology)
 \n\nUnderstanding trust contagion—how trust spreads among human operators 
 toward an AI teammate—is critical for enhancing cooperation in human-AI te
 ams. While previous research has examined trust contagion (Rojas & Li, 202
 4), the role of conversational alignment, specifically lexical and structu
 ral alignment, in facilitating this trust contagion process has received l
 ess attention. This study investigates how lexical and structural alignmen
 t contribute to this trust contagion process. By manipulating a confederat
 e’s expressed trust level (high, low, neutral) toward an AI teammate, we a
 nalyzed how this influenced the linguistic alignment patterns of human tea
 mmates in collaborative interactions. Surprisingly, participants aligned t
 heir word choices (lexical alignment) more when their teammate expressed l
 ow trust, suggesting that linguistic mirroring may act as a compensatory b
 ehavior under uncertainty rather than a signal of trust. Structural (synta
 ctic) alignment showed no significant differences across conditions. These
  findings reveal that people may unconsciously adapt their language more i
 n low-trust environments to navigate uncertainty, rather than to build tru
 st. Understanding these patterns can inform the design of conversational A
 I systems that monitor linguistic cues to assess and support team trust dy
 namics in real time.\n\nTrack: Human AI Robot Teaming (AI)\n\nSession Chai
 rs: David Azari (U.S. Army Futures Command) and Xiaoyun Yin (Arizona State
  University; Center for Human, Artificial Intelligence, and Robot Teaming)
 \n\n
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