BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20251016T135151Z
LOCATION:Grand B
DTSTART;TZID=America/Chicago:20251014T150000
DTEND;TZID=America/Chicago:20251014T152000
UID:HFESAM_ASPIRE 2025_sess203_LECT460@linklings.com
SUMMARY:Understanding Human-Agent Teaming: A Mixed-Methods Examination of 
 Perceptions and Interactions
DESCRIPTION:Valerie Tuccillo Robbins-Roth, Kendall Carmody, Bella DeLoach,
  Meredith Carroll, Amanda Thayer, and Jessica Wildman (Florida Institute o
 f Technology)\n\nWhile quantitative research on trust in HATs is well-esta
 blished, there is a gap in understanding individuals' subjective experienc
 es during trust violations and repair. This study explores how people perc
 eive and respond to these events, guided by three key questions: (1) How d
 o participants describe emotional and behavioral responses to trust violat
 ions and repair? (2) How do personality traits influence reactions? (3) Wh
 at qualitative themes emerge in the dissolution of trust? Using a mixed-me
 thods approach, participants engaged in a simulated search-and-rescue miss
 ion with four autonomous agents, during which, one agent committed two tru
 st violations, followed by repair attempts across five conditions (none, i
 ndividual agents, or full team). Trust ratings were collected, and partici
 pants provided written reflections. Qualitative responses underwent themat
 ic analysis, while survey and behavioral data were examined using regressi
 on and ANOVA. Five recurring themes emerged: emotional responses (e.g., fr
 ustration), performance changes, blame attribution, loss of trust, and exp
 ectations of the violation. Emotional responses were most prevalent, with 
 higher frequency in individual repair conditions. Interestingly, repairs f
 rom the violating agent were often not recognized as such by participants.
  Exploratory analyses showed strong correlations among themes, and regress
 ion models suggested that individual differences and thematic responses ex
 plained significant variance in trust dissolution.\n\nTrack: Human AI Robo
 t Teaming (AI)\n\nSession Chair: Eileen Roesler (George Mason University)\
 n\n
END:VEVENT
END:VCALENDAR