Lena Hegemann
Yi-Chi Liao
Antti Oulasvirta
ABSTRACT
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.
Footnotes
1 Material Design 3, https://m3.material.io/
Footnote2 Lint for Sketch, https://github.com/saranshsolanki/sketch-lint
Footnote3 2022 Design Tools Survey, https://uxtools.co/survey/2022/
Footnote4 Aalto Interface Metrics (AIM), https://interfacemetrics.aalto.fi
Footnote
Supplemental Material
- 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.Google Scholar
- Sebastiaan De Peuter, Antti Oulasvirta, and Samuel Kaski. 2021. Toward AI Assistants That Let Designers Design. arXiv preprint arXiv:2107.13074(2021).Google Scholar
- 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).Google Scholar
- Yue Jiang, Luis A. Leiva, Hamed Rezazadegan Tavakoli, Paul R. B. Houssel, Julia Kylmälä, and Antti Oulasvirta. 2023. UEyes: Understanding Visual Saliency across User Interface Types. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA.Google ScholarDigital Library
- Yue Jiang, Wolfgang Stuerzlinger, Matthias Zwicker, and Christof Lutteroth. 2020. ORCSolver: An Efficient Solver for Adaptive GUI Layout with OR-Constraints. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376610Google ScholarDigital Library
- 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.Google Scholar
- Joongi Shin, Michael A Hedderich, AndréS Lucero, and Antti Oulasvirta. 2022. Chatbots Facilitating Consensus-Building in Asynchronous Co-Design. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 1–13.Google ScholarDigital Library
- Kashyap Todi, Gilles Bailly, Luis Leiva, and Antti Oulasvirta. 2021. Adapting user interfaces with model-based reinforcement learning. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
Index Terms
- Computational Assistance for User Interface Design: Smarter Generation and Evaluation of Design Ideas
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