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:20251016T135125Z
LOCATION:Grand B
DTSTART;TZID=America/Chicago:20251015T093000
DTEND;TZID=America/Chicago:20251015T110000
UID:HFESAM_ASPIRE 2025_sess214_PAN145@linklings.com
SUMMARY:Two Sides of the Same Coin? Joint Perspectives from Shared Mental 
 Models and Interactive Team Cognition Theories on Human-AI Team Cognition
DESCRIPTION:Karen Feigh (Georgia Institute of Technology); Nancy Cooke (Ar
 izona State University); Stephen Fiore (University of Central Florida); Jo
 seph Lyons (U.S. Air Force Research Laboratory); Laura Militello (Applied 
 Decision Science, LLC; Unveil, LLC); Ranjani Narayanan (Georgia Institute 
 of Technology); and Myke Cohen (Arizona State University; Aptima, Inc.)\n\
 nThe ubiquity of artificial intelligence (AI) has made team cognitive scie
 nce a foundation for understanding what increasingly complex human-AI inte
 ractions can achieve. However, different models and theoretical frameworks
  from human teamwork literature offer varying explanations for what human-
 AI team cognition entails. Predominant theoretical frameworks founded on t
 he concept of Shared Mental Models (SMMs) frame team cognition in terms of
  overlaps between team members’ knowledge structures that help them unders
 tand their task context. SMM-based techniques remain widely used to model 
 team performance in knowledge-intensive team tasks; yet, conceptual and me
 thodological concerns about their applicability in human-AI teams (HATs) h
 ave prompted the use of other research frameworks. Among these is Interact
 ive Team Cognition (ITC) theory, which posits that team interaction is tea
 m cognition—a team-level activity that is observable in real-time. With ad
 vancements in both the knowledge and interaction capabilities of AI, this 
 panel will discuss whether and how key intersections between these two the
 ories can contribute to a multi-anchored approach to addressing open resea
 rch questions about human-AI team cognition.\n\nTrack: Human AI Robot Team
 ing (AI)\n\nSession Chairs: Myke Cohen (Arizona State University; Aptima, 
 Inc.) and Ranjani Narayanan (Georgia Institute of Technology)\n\n
END:VEVENT
END:VCALENDAR
