This paper explores how artificial intelligence (AI) can be applied to enhance user interface (UI) and user experience (UX) design in web applications. By analyzing real-time user interaction data such as mouse movements, click patterns, and session time, machine learning models identify usability issues and recommend interface improvements. The study compares traditional heuristic evaluation with AI-driven approaches and demonstrates how data-informed UX redesigns significantly improve engagement metrics. Experimental results suggest that AI integration leads to reduced bounce rates and more efficient user navigation, validating its role in next-generation UX design workflows
This paper explores how artificial intelligence (AI) can be applied to enhance user interface (UI) and user experience (UX) design in web applications. By analyzing real-time user interaction data such as mouse movements, click patterns, and session time, machine learning models identify usability issues and recommend interface improvements. The study compares traditional heuristic evaluation with AI-driven approaches and demonstrates how data-informed UX redesigns significantly improve engagement metrics. Experimental results suggest that AI integration leads to reduced bounce rates and more efficient user navigation, validating its role in next-generation UX design workflows
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Shoyqulov S.Q. | Professor | Karshi State University |
| № | Name of reference |
|---|---|
| 1 | 1.Huang, Y., Li, X., & Zhang, L. (2021). Predicting user friction in web navigation using clickstream analytics. Journal of UX Research, 13(2), 45–60.2.Kumar, R., & Sharma, A. (2020). Clustering user behavior in web interfaces for adaptive UX improvement. Applied Artificial Intelligence, 34(7), 567–584. https://doi.org/10.1080/08839514.2020.17834433.Nielsen, J., & Budiu, R. (2012). Mobile usability. New Riders.4.Krug, S. (2014). Don't make me think, revisited: A common sense approach to web usability(3rd ed.). New Riders.5.Shoyqulov Sh.Q. Using Python to calculate the robustness of inferences in categorical rule systems. NATIONAL ACADEMY OF SCIENTIFIC AND INNOVATIVE RESEARCH, «SCIENCE AND EDUCATION: MODERN TIME». (VOLUME 1 ISSUE 10, 2024), ISSN 3005-4729 / e-ISSN 3005-47376.Shoyqulov Sh.Q. Modern methods and means of protecting information on the Internet. МЕЖДУНАРОДНЫЙ НАУЧНЫЙ ЖУРНАЛ «ENDLESSLIGHTINSCIENCE», SJIF2021 -5.81. 2022 -5.94, октябрь 2024 г. Туркестан, Казахстан,7.Shoyqulov Sh.Q. Analysis and optimization of graphics programming in C# using Unity. «Science and innovation» xalqaro ilmiy jurnali, Volume 3 Issue 10, 8.Shoyqulov Sh.Q. Main Internet threats and ways to protect against them. Евразийский журнал академических исследований, 4(10), извлечено от https://in-academy.uz/index.php/ejar/article/view/387099.Shoyqulov Sh.Q. Using Python programming in computer graphics. «Science and innovation» xalqaro ilmiy jurnali, Volume 3 Issue 1010.Shoyqulov Sh.Q. Data visualization in Python, EURASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES (Т. 4, Выпуск 10, сс. 15–22).11.Shoyqulov Sh.Q. Graphical programming of 2D applications in C#. EURASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES (Т. 4,Выпуск 10, сс. 7–14).12.Shoyqulov Sh.Q. Methods for plotting function graphs in computers using backend and frontend internet technologies. Published in European Scholar Journal (ESJ). Spain, Impact Factor: 7.235, https://www.scholarzest.com, Vol. 2 No. 6,June 2021, ISSN: 2660-5562.13.Shoyqulov Sh.Q. Multimedia possibilities of Web-technologies. Eurasian journal of mathematical, theory and computer sciences, UIF = 8.3 , SJIF = 5.916, ISSN 2181-2861, Vol. 3 Issue 3, Mart 2023, p. 11-15 |