Usable but Conventional: An Empirical Study on the UX of AI-Generated Interface Prototypes
Resumo
This paper investigates User Experience (UX) with prototypes generated by Generative Artificial Intelligence (GenAI) tools. An empirical survey with 92 participants evaluated AI-generated and human-created prototypes without prior identification of authorship. We measured UX using the UEQ-S, covering pragmatic and hedonic dimensions. Results indicate positive evaluations in pragmatic aspects, such as usability and efficiency, and neutral or negative evaluations in hedonic aspects, including originality and innovation. We concluded that GenAI can produce functional interfaces but tends to reinforce visual and structural patterns that affect perceptions of originality.Referências
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Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200):675–701.
Kitchenham, B. A. and Pfleeger, S. L. (2008). Guide to Advanced Empirical Software Engineering, chapter 3: Personal Opinion Surveys, pages 63–92. Springer London, London.
Lee, K. (2025). Towards a working definition of designing generative user interfaces. In Companion Publication of the 2025 ACM Designing Interactive Systems Conference, DIS ’25, page 489–495. ACM.
Li, J., Cao, H., Lin, L., Hou, Y., Zhu, R., and El Ali, A. (2024). User experience design professionals’ perceptions of generative artificial intelligence. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, CHI ’24, New York, NY, USA. Association for Computing Machinery.
Nallatamby, V., Benard, A., and El-Husseini, S. (2025). From idea to app: Introducing stitch, a new way to design uis. [link]. Google Developers Blog, acesso em 17 dez. 2025.
Padmasiri, P., Kalutharage, P., Jayawardhane, N., and Wickramarathne, J. (2023). Ai-driven user experience design: Exploring innovations and challenges in delivering tailored user experiences. In 2023 8th International Conference on Information Technology Research (ICITR), pages 1–6.
Pasetti, M., Santos, J., Corrêa, N., Oliveira, N., and Palhares Barbosa, C. (2025). Technical, legal, and ethical challenges of generative artificial intelligence: an analysis of the governance of training data and copyrights. Discover Artificial Intelligence, 5.
Schrepp, M., Hinderks, A., and Thomaschewski, J. (2017). Design and evaluation of a short version of the user experience questionnaire (ueq-s). International Journal of Interactive Multimedia and Artificial Intelligence, 4:103–108.
Shapiro, S. S. and Wilk, M. B. (1965). An analysis of variance test for normality (complete samples)†. Biometrika, 52(3-4):591–611.
Shi, Y., Gao, T., Jiao, X., and Cao, N. (2023). Understanding design collaboration between designers and artificial intelligence: A systematic literature review. Proc. ACM Hum.-Comput. Interact., 7(CSCW2).
Tholander, J. and Jonsson, M. (2023). Design ideation with ai - sketching, thinking and talking with generative machine learning models. In Proceedings of the 2023 ACM Designing Interactive Systems Conference, DIS ’23, page 1930–1940, New York, NY, USA. Association for Computing Machinery.
Trocin, C., Åsne Stige, and Mikalef, P. (2023). Machine learning (ml) diffusion in the design process: A study of norwegian design consultancies. Technological Forecasting and Social Change, 194:122724.
Wang, Z., Shen, L., Kuang, E., Zhang, S., and Fan, M. (2024). Exploring the impact of artificial intelligence-generated content (aigc) tools on social dynamics in ux collaboration. In Proceedings of the 2024 ACM Designing Interactive Systems Conference, DIS ’24, page 1594–1606, New York, NY, USA. Association for Computing Machinery.
Conover, W. J. (1999). Practical Nonparametric Statistics. Wiley, 3 edition.
da Silva Scapim, G. R., Leal, G. C. L., and Guerino, G. C. (2025). New perspectives on low-fidelity prototyping: A systematic mapping study on tools and functionalities. In Anais do XXIV Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais, pages 1565–1588, Porto Alegre, RS, Brasil. SBC.
Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200):675–701.
Kitchenham, B. A. and Pfleeger, S. L. (2008). Guide to Advanced Empirical Software Engineering, chapter 3: Personal Opinion Surveys, pages 63–92. Springer London, London.
Lee, K. (2025). Towards a working definition of designing generative user interfaces. In Companion Publication of the 2025 ACM Designing Interactive Systems Conference, DIS ’25, page 489–495. ACM.
Li, J., Cao, H., Lin, L., Hou, Y., Zhu, R., and El Ali, A. (2024). User experience design professionals’ perceptions of generative artificial intelligence. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, CHI ’24, New York, NY, USA. Association for Computing Machinery.
Nallatamby, V., Benard, A., and El-Husseini, S. (2025). From idea to app: Introducing stitch, a new way to design uis. [link]. Google Developers Blog, acesso em 17 dez. 2025.
Padmasiri, P., Kalutharage, P., Jayawardhane, N., and Wickramarathne, J. (2023). Ai-driven user experience design: Exploring innovations and challenges in delivering tailored user experiences. In 2023 8th International Conference on Information Technology Research (ICITR), pages 1–6.
Pasetti, M., Santos, J., Corrêa, N., Oliveira, N., and Palhares Barbosa, C. (2025). Technical, legal, and ethical challenges of generative artificial intelligence: an analysis of the governance of training data and copyrights. Discover Artificial Intelligence, 5.
Schrepp, M., Hinderks, A., and Thomaschewski, J. (2017). Design and evaluation of a short version of the user experience questionnaire (ueq-s). International Journal of Interactive Multimedia and Artificial Intelligence, 4:103–108.
Shapiro, S. S. and Wilk, M. B. (1965). An analysis of variance test for normality (complete samples)†. Biometrika, 52(3-4):591–611.
Shi, Y., Gao, T., Jiao, X., and Cao, N. (2023). Understanding design collaboration between designers and artificial intelligence: A systematic literature review. Proc. ACM Hum.-Comput. Interact., 7(CSCW2).
Tholander, J. and Jonsson, M. (2023). Design ideation with ai - sketching, thinking and talking with generative machine learning models. In Proceedings of the 2023 ACM Designing Interactive Systems Conference, DIS ’23, page 1930–1940, New York, NY, USA. Association for Computing Machinery.
Trocin, C., Åsne Stige, and Mikalef, P. (2023). Machine learning (ml) diffusion in the design process: A study of norwegian design consultancies. Technological Forecasting and Social Change, 194:122724.
Wang, Z., Shen, L., Kuang, E., Zhang, S., and Fan, M. (2024). Exploring the impact of artificial intelligence-generated content (aigc) tools on social dynamics in ux collaboration. In Proceedings of the 2024 ACM Designing Interactive Systems Conference, DIS ’24, page 1594–1606, New York, NY, USA. Association for Computing Machinery.
Publicado
19/07/2026
Como Citar
ROMERO, Karoline; WIESE, Igor; BALANCIEIRI, Renato; LEAL, Gislaine Camila; GUERINO, Guilherme.
Usable but Conventional: An Empirical Study on the UX of AI-Generated Interface Prototypes. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 830-841.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.21904.
