Isabelle Kwan

dblp:402/8958 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0006-5074-7913ORCID · corroborated

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

Human-computer interaction and ubiquitous computing · 2 · 2 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
Learning and educational technologies · 44% User interface design and tools · 44% Design research and methods · 13%

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

TopicWeightPapersLastEvidence papers
Learning and educational technologies
self-directed learning
0.912025
MILESTONES: The Design and Field Evaluation of a Semi-Automated Tool for Promoting Self-Directed Learning Among Online Learners · CHI 2025
Design research and methods
field study
0.312025
MILESTONES: The Design and Field Evaluation of a Semi-Automated Tool for Promoting Self-Directed Learning Among Online Learners · CHI 2025

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

tool design · 0.9field evaluation · 0.9
YearPublicationVenuePosition
2026 How Creatives Approach GenAI Image Generation: Tensions Between Structured Guidance, Self-Experimentation, and Creative Autonomy
abstract
As generative AI tools increasingly influence creative practice, they raise longstanding HCI questions about how creatives learn complex software and how they can be better supported. We conducted an interview study with artists and hobbyists (n=8) and a follow-up survey (n=159) to understand how this population approaches and seeks guidance for GenAI image tools. We found that creatives commonly use either self-experimentation or tutorials to explore GenAI tools, yet many struggle with confusing AI terminology. To gain further insight into creatives’ learning experiences, we developed a research probe to elicit creatives’ perceptions of structured guidance. Our user study with 17 creatives revealed that, even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity. Our findings highlight a central tension in supporting AI literacy for creatives: balancing guidance and promoting literacy while preserving creative freedom.
Haidan Liu, Isabelle Kwan, Taiga Okuma, Jeffrey Loverock, Nicholas Vincent, Parmit K. Chilana
Creativity & Cognition2
2025 MILESTONES: The Design and Field Evaluation of a Semi-Automated Tool for Promoting Self-Directed Learning Among Online Learners
Rimika Chaudhury, Courtenay Huffman, Isabelle Kwan, Gurnoor S. Deol, Supreet Dhillon, Parmit K. Chilana
CHI3