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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135140Z
LOCATION:Grand A
DTSTART;TZID=America/Chicago:20251014T113000
DTEND;TZID=America/Chicago:20251014T115000
UID:HFESAM_ASPIRE 2025_sess219_LECT195@linklings.com
SUMMARY:Visual Attention in Healthcare Chatbot Interactions: Quantifying V
 arying Communication Styles with K-Coefficient
DESCRIPTION:Samuel Koscelny, Robin Rucker II, Michael Reed, and Andrew Duc
 howski (Clemson University) and David Neyens (Clemson University`)\n\nAdva
 ncements in artificial intelligence (AI) are transforming healthcare chatb
 ots, improving their potential to support patient education and engagement
 . The effectiveness of healthcare chatbots depends not only on technical a
 bility but also on how their communication style impacts user engagement, 
 particularly through visual attention. To investigate this, a between-subj
 ects study evaluated the effect of chatbot communication style (conversati
 onal vs. informative) on eye-gaze behavior in a knowledge-seeking task. Ey
 e metrics were analyzed using quantile and linear regression models. Quant
 ile regression models revealed the distribution of fixation duration was a
 lways higher in the informative condition, while saccadic amplitude and th
 e K-coefficient varied across quantiles. The linear regression model showe
 d that both communication style and stimulus progression significantly inc
 reased the K-coefficient over time. These findings demonstrate that chatbo
 t communication style influences user visual attention over time, undersco
 ring the need for future work to align chatbot communication styles with u
 ser attention patterns.\n\nTrack: Cognitive Engineering & Decision Making\
 n\nSession Chair: xiaoli wu (Nanjing University of Science and Technology,
  University College London)\n\n
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