Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Patrycja Zajac

dblp:419/8237 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 44% Human-AI interaction · 44% Immersive interaction · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
trust in robots
0.912025
The AI of the Beholder: Experience of Healthcare AI Robots is Shaped by User-Centred Factors, Not Their Visual Appearance · IEEE Trans. Vis. Comput. Graph. 2025
Immersive interaction
virtual reality user study
0.312025
The AI of the Beholder: Experience of Healthcare AI Robots is Shaped by User-Centred Factors, Not Their Visual Appearance · IEEE Trans. Vis. Comput. Graph. 2025

Methods — techniques the papers use, named apart from their topics

virtual reality scenario · 0.9user study · 0.9
YearPublicationVenuePosition
2025 The AI of the Beholder: Experience of Healthcare AI Robots is Shaped by User-Centred Factors, Not Their Visual Appearance
abstract
Healthcare artificial intelligence (HAI) robots could alleviate the current healthcare provision crisis. However, their successful deployment rests on achieving and responsibly calibrating appropriate patient trust in them. Over two user studies, we systematically investigate trust formation in and usage intentions for HAI robots. We first investigate manipulations of visual appearance in a passive observation study without healthcare-specific situational context (n=87) and then in a realistic VR scenario depicting a medical consultation with a HAI robot (n=177) that introduces such situational context (diagnosis and treatment severity). Results show that within a healthcare context, a unique combination of (i) situational (diagnosis and treatment severity), (ii) robot (perceived competence) and (iii) user characteristics (personality and attitudes) determine trust and ultimately influence usage intentions for HAI robots. Furthermore, our results emphasize the absence of HAI visual appearance effects on trust. Our findings support and inform a human centred design approach of HAI robots.
Jinha Yoon, Patrycja Zajac, Lauren Pococke, Anisia Jicol, Christopher Clarke, Eamonn O'Neill, Karin Petrini, Christof Lutteroth, Crescent Jicol
IEEE Trans. Vis. Comput. Graph.2