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

Elena Cavallin

dblp:398/4418 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Human-AI interaction › human-AI co-creation
generative AI co-creation
1.012026
When Designers Sweat: Behavioral Traces of GenAI Co-Creation · CHI 2026
Design research and methods
design process
0.312026
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
YearPublicationVenuePosition
2026 When Designers Sweat: Behavioral Traces of GenAI Co-Creation
abstract
The 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
CHI1