AI-Assisted UI Design: Enhancing Efficiency and Creativity through Generative Tools

Lingxin Sun

doi:10.70393/6a6374616d.333638

OPEN ACCESS |Research Article ||5 January 2026

AI-Assisted UI Design: Enhancing Efficiency and Creativity through Generative Tools

Lingxin Sun

Rochester Institute of Technology

lingxin0613@gmail.com

College of Art and Design, Rochester Institute of Technology, Rochester, USA.

* Corresponding Author¹: Lingxin Sun, E-Mail: lingxin0613@gmail.com

Publication

Accepted 2025 December 29 ; Published 2026 January 5

Journal of Computer Technology and Applied Mathematics, 2026, 3(1), 3007-4126.

Abstract

The growing adoption of Artificial Intelligence (AI) in web-based products has made the design of user interfaces that effectively surface and control AI capabilities increasingly critical. In this context, understanding the key characteristics and best practices for user interfaces that support AI-driven functionality is both timely and practically relevant. This research discusses the fundamental principles of user interface (UI) design. It analyzes the specific challenges posed by integrating AI into web applications, including transparency, controllability, and appropriate levels of automation. It emphasizes the need to balance the advanced capabilities of AI systems with users’ ability to understand, trust, and steer those systems. This paper examines the dynamic responses of AI-driven recommendation systems and personalized interfaces on various systems, as well as the design of user preferences and adaptive layouts. Based on this analysis, a feasible evaluation framework for recommendation systems with practical applications is presented. This framework supports empirical evaluations conducted through usability testing to demonstrate significant effects, thereby helping designers and developers achieve more intuitive and noticeable interface effects for AI-driven applications.

Keywords

Artificial Intelligence , User Interface Design , AI-enabled Web Applications , Human–AI Interaction .

Metadata

DOI:

10.70393/6a6374616d.333638

ARK:

ark:/40704/JCTAM.v3n1a03

Pages: 19-27

References: 36

Disciplines: Computer Application Technology

Subjects: Human-Computer Interaction

Cite This Article

APA Style

Sun, L. (2026). Ai-assisted ui design: enhancing efficiency and creativity through generative tools. Journal of Computer Technology and Applied Mathematics, 3(1), 19-27. https://doi.org/10.70393/6a6374616d.333638

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Hameed, H., Maurya, M., & Arif, M. (2025). Optimizing User Engagement: The Synergy of Social Media Marketing and UI/UX Design. In Contemporary Issues in Social Media Marketing (pp. 86-102). Routledge.

2.

Nugraha, A. F. S., Fitriyadi, F., & Lukitasari, E. H. (2024, November). UI/UX Design for Triwindu Market Surakarta. In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1-6). IEEE.

3.

Saeidnia, H. R., & Ausloos, M. (2024). Integrating artificial intelligence into design thinking: A comprehensive examination of the principles and potentialities of AI for design thinking framework. InfoScience Trends, 1(2), 1-9.

4.

Nandan, M., Jaskyte, K., & Mandayam, G. (2020). Human centered design as a new approach to creative problem solving: Its usefulness and applicability for social work practice. Human Service Organizations: Management, Leadership & Governance, 44(4), 310-316.

5.

Korteling, J. E., van de Boer-Visschedijk, G. C., Blankendaal, R. A., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human-versus artificial intelligence. Frontiers in artificial intelligence, 4, 622364.

6.

Emma, L. (2024). Bridging the Gap Between Natural Language Processing (NLP) and Robotics for Human-Robot Interaction.

7.

Li, L., Huang, Q., Yeung, K., & Jian, Z. (2018). Human-computer interaction and value co-creation in electronic service. Industrial Management & Data Systems, 118(1), 218-235.

8.

McCormack, J., Hutchings, P., Gifford, T., Yee-King, M., Llano, M. T., & D’inverno, M. (2020). Design considerations for real-time collaboration with creative artificial intelligence. Organised Sound, 25(1), 41-52.

9.

Jyoti, S. N., Islam, M. R., & Kudapa, S. P. (2024). The Role of Test Automation Frameworks In Enhancing Software Reliability: A Review Of Selenium, Python, And API Testing Tools. International Journal of Business and Economics Insights, 4(4), 01-34.

10.

Pandhare, H. V. (2025). Future of Software Test Automation Using AI/ML. International Journal Of Engineering And Computer Science, 13(05).

11.

Zhang, X., Kim, J., & Li, Y. (2020).Designing with automation: Human–AI interaction in generative design systems. Design Studies, 68, 100971.

12.

Kim, J., Yeh, T., & Myers, B. A. (2021).Creating user interfaces by demonstration and natural language. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–15.

13.

Yuan, Y., & Xue, H. (2025). Cross-Media Data Fusion and Intelligent Analytics Framework for Comprehensive Information Extraction and Value Mining.

14.

Yuan Y, Xue H. Multimodal Information Integration and Retrieval Framework Based on Graph Neural Networks[C]//Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network. 2025: 135-139.

15.

Shneiderman, B. (2022).Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 38(1), 1–16.

16.

Oh, C., Song, J., Choi, J., Kim, S., Lee, S., & Suh, B. (2018).I Lead, You Help but Only with Enough Details: Understanding the User Experience of Co-Creation with Artificial Intelligence. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–13.

17.

Li Z, Ji Q, Ling X, et al. A comprehensive review of multi-agent reinforcement learning in video games[J]. Authorea Preprints, 2025.

18.

Zhang, Z., Wang, J., Li, Z., Wang, Y., & Zheng, J. (2025). Anncoder: A mti-agent-based code generation and optimization model. Symmetry, 17(7), 1087.

19.

Wang, Q., Li, Y., & Myers, B. A. (2023). Designing AI-powered interactive systems with large language models. Communications of the ACM, 66(9), 78–87.

20.

Kery, M. B., Horvitz, E., & Hullman, J. (2020). Supporting iterative data exploration with conversational interfaces. IEEE Transactions on Visualization and Computer Graphics, 26(1), 332–342.

21.

Zhao H, Chen Y, Dang B, et al. Research on Steel Production Scheduling Optimization Based on Deep Learning[C]//2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). IEEE, 2024: 813-816.

22.

Sha F, Meng J, Zheng X, et al. Sustainability Under Fire: How China-US Tensions Impact Corporate ESG Performance? [J]. Finance Research Letters, 2025: 107882.

23.

Sha F, Ding C, Zheng X, et al. Weathering the Policy Storm: How Trade Uncertainty Shapes Firm Financial Performance through Innovation and Operations[J]. International Review of Economics & Finance, 2025: 104274.

24.

Deng X. Cooperative Optimization Strategies for Data Collection and Machine Learning in Large-Scale Distributed Systems[C]//2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025: 2151-2154.

25.

Tan, C., Gao, F., Song, C., Xu, M., Li, Y., & Ma, H. (2024). Highly Reliable CI-JSO based Densely Connected Convolutional Networks Using Transfer Learning for Fault Diagnosis.

26.

Davis, N., Hsiao, C. P., Popova, Y., & Magerko, B. (2015). An enactive model of creativity for computational collaboration and co-creation. In Creativity in the digital age (pp. 109-133). London: Springer London.

27.

Amershi, S., Weld, D., Vorvoreanu, M., et al. (2019).Guidelines for human–AI interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13.

28.

Tan, C., Gao, F., Song, C., Xu, M., Li, Y., & Ma, H. (2024). Proposed Damage Detection and Isolation from Limited Experimental Data Based on a Deep Transfer Learning and an Ensemble Learning Classifier.

29.

Gonzalez, J., Tran, V., Meredith, J., Xu, I., Penchala, R., Ribo, L. V., ... & 23andMe Research Team. (2025). Subjective Response to Opioids Predicts Risk for Opioid Use Disorder. medRxiv: the preprint server for health sciences.

30.

Tan C. The Application and Development Trends of Artificial Intelligence Technology in Automotive Production[J]. Artificial Intelligence Technology Research, 2024, 2(5).

31.

Zhang, L., & Meng, Q. (2025). User Portrait-Driven Smart Home Device Deployment Optimization and Spatial Interaction Design.

32.

H. Yang et al., "Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems," arXiv preprint arXiv:2506.17551, 2025.

33.

Han X, Dou X. User recommendation method integrating hierarchical graph attention network with multimodal knowledge graph[J]. Frontiers in Neurorobotics, 2025, 19: 1587973.

34.

Zhuang R. Evolutionary Logic and Theoretical Construction of Real Estate Marketing Strategies under Digital Transformation[J]. Economics and Management Innovation, 2025, 2(2): 117-124.

35.

Z. Yang et al., "RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders," arXiv preprint arXiv:2508.05289, 2025

36.

Deng, Xiaoyu, and Jinzhu Yang. "Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework." In 2025 5th International Conference on Computer Science and Blockchain (CCSB), pp. 278-282. IEEE, 2025.

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