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Abstract

Advancements in artificial intelligence (AI) are transforming healthcare chatbots, improving their potential to support patient education and engagement. The effectiveness of healthcare chatbots depends not only on technical ability but also on how their communication style impacts user engagement, particularly through visual attention. To investigate this, a between-subjects study evaluated the effect of chatbot communication style (conversational vs. informative) on eye-gaze behavior in a knowledge-seeking task. Eye metrics were analyzed using quantile and linear regression models. Quantile regression models revealed the distribution of fixation duration was always higher in the informative condition, while saccadic amplitude and the K-coefficient varied across quantiles. The linear regression model showed that both communication style and stimulus progression significantly increased the K-coefficient over time. These findings demonstrate that chatbot communication style influences user visual attention over time, underscoring the need for future work to align chatbot communication styles with user attention patterns.

Keywords

eye tracking media richness theory gaze behavior

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References

Brandtzaeg

P. B.

(2018). Chatbots: Changing user needs motivations. Association for Computing Machinery (ACM) Interactions, 25(5), 38–43.

Carmichael

Poirier

S.-M.

Coursaris

Léger

P.-M.

Sénécal

(2021). Does media richness influence the user experience of Chatbots: A pilot study. In Davis

F. D.

Riedl

vom Brocke

Léger

P.-M.

Randolph

A. B.

Müller-Putz

(Eds.), Information systems and neuroscience (pp. 204–213). Springer International Publishing.

Daft

R. L.

Lengel

R. H.

(1983). Information richness. A New approach to managerial behavior and organization design. Research in Organizational Behavior. https://api.semanticscholar.org/CorpusID:7859959

Daft

R. L.

Lengel

R. H.

(1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571. https://doi.org/10.1287/mnsc.32.5.554

Daft

R. L.

Lengel

R. H.

Trevino

L. K.

(1987). Message equivocality, media selection, and manager performance: Implications for information systems. MIS Quarterly, 11(3), 355–366.

Duchowski

A. T.

(2007). Eye tracking methodology: Theory and practice (2nd ed.). Springer London.

Just

M. A.

Carpenter

P. A.

(1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329–354. https://doi.org/10.1037/0033-295X.87.4.329

Koscelny

S. N.

Neyens

D. M.

(2024). The effect of healthcare Chatbots’ information presentation styles on user acceptance in a knowledge seeking task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68, 493–498. https://doi.org/10.1177/10711813241263509

Koscelny

S. N.

Sadralashrafi

Neyens

D. M.

(2025). Generative AI responses are a dime a dozen; Making them count is the challenge—Evaluating information presentation styles in healthcare chatbots using hierarchical Bayesian regression models. Applied Ergonomics, 128, 104515. https://doi.org/10.1016/j.apergo.2025.104515

10.

Krejtz

Duchowski

Krejtz

Szarkowska

Kopacz

(2016). Discerning ambient/focal attention with coefficient K. ACM Transactions on Applied Perception, 13(3), 1–20. https://doi.org/10.1145/2896452

11.

Mariani

M. M.

Hashemi

Wirtz

(2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161(113838), 113838. https://doi.org/10.1016/j.jbusres.2023.113838

12.

McTear

M. F.

Callejas

Griol

(2016). The Conversational INterface: Talking to smart devices (Vol. 6, Issue 94). Springer.

13.

Miner

A. S.

Milstein

Schueller

Hegde

Mangurian

Linos

(2016). Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Internal Medicine, 176(5), 619. https://doi.org/10.1001/jamainternmed.2016.0400

14.

Rayner

(1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372–422. https://doi.org/10.1037/0033-2909.124.3.372

15.

Rohit

Shankar

Katiyar

Mehrotra

Alzeiby

E. A.

(2024). Consumer engagement in chatbots and voicebots. A multiple-experiment approach in online retailing context. Journal of Retailing and Consumer Services, 78, 103728. https://doi.org/10.1016/j.jretconser.2024.103728

16.

Tudor Car

Dhinagaran

D. A.

Kyaw

B. M.

Kowatsch

Joty

Theng

Y.-L.

Atun

. (2020). Conversational agents in health care: Scoping review and conceptual analysis. Journal of Medical Internet Research, 22(8), e17158–e17158.

17.

Unema

Pannasch

Joos

Velichkovsky

(2005). Time course of information processing during scene perception: The relationship between saccade amplitude and fixation duration. Visual Cognition, 12, 473–494. https://doi.org/10.1080/13506280444000409

18.

Wang

(2022). Media richness and continuance intention to online learning platforms: The mediating role of social presence and the moderating role of need for cognition. Frontiers in Psychology, 13, 950501. https://doi.org/10.3389/fpsyg.2022.950501

19.

Yin

Neyens

D. M.

(2024). Examining how information presentation methods and a chatbot impact the use and effectiveness of electronic health record patient portals: An exploratory study. Patient Education and Counseling, 119, 108055. https://doi.org/10.1016/j.pec.2023.108055

Visual Attention in Healthcare Chatbot Interactions: Quantifying Varying Communication Styles with the K-Coefficient

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