The DOI matches its Crossref record, with a small difference in title or year.
Computational Assistance for User Interface Design: Smarter Generation and Evaluation of Design Ideas
Open accessAbstract
This paper describes a lab demo by the User Interfaces group at Aalto University. The demo allows attendees to interactively experience recent research prototypes aiming to facilitate designers’ creative and problem-solving capabilities in user interface (UI) design. Empirical work on designers suggests that UI design is challenging, partially because of the presence of very large design spaces, multiple and ill-defined objectives, design fixation and biases, as well as multiple requirements that need to to kept in mind. At the exhibition, members of the lab provide live demonstrations of six computational features, with a special focus on plug-ins created for Figma, a popular UI design tool. The demos draw from the group’s latest research published at HCI conferences. They demonstrate how to interactively exploit machine learning methods ranging from deep nets to Bayesian inference and NLP. We also present our design approach and provide a summary of findings from empirical evaluations with designers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In complex co-creation, AI agency patterns are typically co-operative or semi-active, often involving shared or dynamically negotiated locus of control.
“AI agency patterns are typically co-operative or semi-active [66, 191], often involving a shared or dynamically negotiated locus of control [8, 47, 109].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [66]
The Discussion and Conclusions section identifies shared control as an outstanding question and proposes mixed-initiative tools that can intervene when needed. It does not establish that a shared or dynamically negotiated locus of control is common in existing AI agency patterns; it presents this as a design goal. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 8
- Liwei Chan, Yi-Chi Liao, George B Mo, John J Dudley, Chun-Lien Cheng, Per Ola Kristensson, and Antti Oulasvirta. 2022. Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques. In CHI Conference on Human Factors in Computing Systems. 1–14.
- Sebastiaan De Peuter Antti Oulasvirta and Samuel Kaski. 2021. Toward AI Assistants That Let Designers Design. arXiv preprint arXiv:2107.13074(2021). · arXiv
- Lena Hegemann, Niraj Ramesh Dayama, Abhishek Iyer, Erfan Farhadi, Ekaterina Marchenko, and Antti Oulasvirta. 2023. CoColor: Interactive Exploration of Color Designs. In 28th International Conference on Intelligent User Interfaces (Sydney, Australia).
- 10.1145/3544548.3581096 · DOI
- 10.1145/3313831.3376610 · DOI
- Antti Oulasvirta, Samuli De Pascale, Janin Koch, Thomas Langerak, Jussi Jokinen, Kashyap Todi, Markku Laine, Manoj Kristhombuge, Yuxi Zhu, Aliaksei Miniukovich, 2018. Aalto Interface Metrics (AIM) A Service and Codebase for Computational GUI Evaluation. In The 31st Annual ACM Symposium on User Interface Software and Technology Adjunct Proceedings. 16–19.
- 10.1145/3526113.3545671 · DOI
- 10.1145/3411764.3445497 · DOI
Cited by
Sorted by citations
Zhang, S., Wang, H., & Yi, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757594 · Cited by 36
Metadata
- Authors
- Hegemann, L., Jiang, Y., Shin, J. G., Liao, Y.-C., Laine, M., & Oulasvirta, A.
- Affiliations
- Aalto University (FI)
- Department of Electrical Engineering, Aalto University, Finland
- Venue
- Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-5
- OpenAlex
- W4366588992
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3
- FWCI
- 1.62
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- user interface design, design space exploration, design fixation, machine learning in design, Bayesian inference, natural language processing, deep learning, design evaluation, designer creativity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
