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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135131Z
LOCATION:Grand B
DTSTART;TZID=America/Chicago:20251015T150000
DTEND;TZID=America/Chicago:20251015T152000
UID:HFESAM_ASPIRE 2025_sess210_LECT244@linklings.com
SUMMARY:Examining Transparency Needs and Display Features: An Empirical St
 udy of Dynamic Dual-task HAT Simulation with AI Integration
DESCRIPTION:Shruthi Venkatesha Murthy, Dr. Sang-Hwan Kim, Yejin Lee, Wonji
  Doh, and Youngnoh Goh (University of Michigan, Dearborn)\n\nThis study ex
 amines how different task allocation strategies and communication modaliti
 es affect Human-AI team (HAT) performance in dynamic dual-task environment
 s. Using a modified Ballas Task simulator with 32 participants from milita
 ry or similar backgrounds, we evaluated four strategies - Operator Monitor
 ing (OM), Action Split (AS), Target Split (TS), and Take Over (TO) - under
  varying transparency and communication conditions engaged in tracking and
  tactical assessment tasks. Results show that task allocation effectivenes
 s depends on transparency and communication mode. OM consistently lowered 
 workload and assessment time while improving tracking, target accuracy, an
 d situational awareness, especially with verbal – low transparency communi
 cation. Non-verbal communication reduced tracking errors and enhanced situ
 ational awareness in AS and TS strategies. While TO offered fewer benefits
  compared to other strategies, it still outperformed an all-human baseline
 . These findings offer practical insights for optimizing human-AI collabor
 ation in high-pressure, time-sensitive environments, with implications ext
 ending beyond military contexts to various dynamic dual-task settings.\n\n
 Track: Human AI Robot Teaming (AI)\n\nSession Chair: Maha Khalid (Rice Uni
 versity)\n\n
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