Demo Data from EDBT 2026 / reviewers in the wild / expert
Patrycja Zajac
dblp:419/8237
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
trust in robots |
0.9 | 1 | 2025 | 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 |
0.3 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | The AI of the Beholder: Experience of Healthcare AI Robots is Shaped by User-Centred Factors, Not Their Visual AppearanceabstractHealthcare 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 |