Demo Data from EDBT 2026 / reviewers in the wild / expert
Elena Cavallin
dblp:398/4418
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0001-8908-3807ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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% Design research and methods · 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 › human-AI co-creation
generative AI co-creation |
1.0 | 1 | 2026 | When Designers Sweat: Behavioral Traces of GenAI Co-Creation · CHI 2026 |
Design research and methods
design process |
0.3 | 1 | 2026 | When Designers Sweat: Behavioral Traces of GenAI Co-Creation · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
video-based behavior analysis · 1.0questionnaire · 1.0mixed-methods study · 1.0interaction tracking · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | When Designers Sweat: Behavioral Traces of GenAI Co-CreationabstractThe integration of generative artificial intelligence (GenAI) into design processes raises fundamental questions about behavioral patterns in human-GenAI interaction. This study examines how 16 professional designers interact with GenAI tools during concept development through a mixed-methods approach including pre/post-task questionnaires, video-based behavior analysis, and digital interaction tracking. Results reveal a critical distinction between reflective usage modes and creative modes, with differentiated cognitive impacts. Analysis of communication loops shows significant correlations between interaction difficulties and final design output quality. Three distinct clusters emerge: designers with fluid, problematic, and adaptive interaction patterns. By providing a methodological framework for evaluating GenAI tool effectiveness in design practice, this research contributes to theoretical understanding of behavioral processes in human-GenAI co-creation. Findings reveal specific strategies and workflow adaptations that optimize designer-GenAI collaboration, informing both design methodology and human-computer interaction practice. Elena Cavallin, Simone Spagnol |
CHI | 1 |