Demo Data from SIGIR 2026 / reviewers in the wild / expert
Karen Aul
dblp:318/7745
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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-AI interaction · 77% Health and well-being technologies · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › virtual agents
virtual human |
0.6 | 1 | 2022 | The Effect of Virtual Humans Making Verbal Communication Mistakes on Learners' Perspectives of their Credibility, Reliability, and Trustworthiness · VR 2022 |
Health and well-being technologies
medical simulation |
0.2 | 1 | 2022 | The Effect of Virtual Humans Making Verbal Communication Mistakes on Learners' Perspectives of their Credibility, Reliability, and Trustworthiness · VR 2022 |
Methods — techniques the papers use, named apart from their topics
user study · 0.6mixed design · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | The Effect of Virtual Humans Making Verbal Communication Mistakes on Learners' Perspectives of their Credibility, Reliability, and TrustworthinessabstractSimulating real-world experiences in a safe environment has made virtual human medical simulations a common use case for research and interpersonal communication training. Despite the benefits virtual human medical simulations provide, previous work suggests that users struggle to notice when virtual humans make potentially life-threatening verbal communication mistakes inside virtual human medical simulations. In this work, we performed a 2x2 mixed design user study that had learners (n = 80) attempt to identify verbal communication mistakes made by a virtual human acting as a nurse in a virtual desktop environment. A virtual desktop environment was used instead of a head-mounted virtual reality environment due to Covid-19 limitations. The virtual desktop environment experience allowed us to explore how frequently learners identify verbal communication mistakes in virtual human medical simulations and how perceptions of credibility, reliability, and trustworthiness in the virtual human affect learner error recognition rates. We found that learners struggle to identify infrequent virtual human verbal communication mistakes. Additionally, learners with lower initial trustworthiness ratings are more likely to overlook potentially life-threatening mistakes, and virtual human mistakes temporarily lower learner credibility, reliability, and trustworthiness ratings of virtual humans. From these findings, we provide insights on improving virtual human medical simulation design. Developers can use these insights to design virtual simulations for error identification training using virtual humans. Jacob Stuart, Karen Aul, Michael D. Bumbach, Anita Stephen, Alexandre Gomes de Siqueira, Benjamin Lok |
VR | 2 |