Comparative Analysis of Web Design Performance: AI‑Assisted Tools Versus Traditional Figma‑Based Design

This study compared the performance of traditional Figma workflows and AI-assisted Figma workflows in developing high-fidelity UI/UX prototypes among BSIT students of Quezon City University during the Academic Year 2025–2026. Using a descriptive-comparative within-subjects design, the study evaluated both workflows in terms of Design Quality (Functional Suitability, Usability, and Performance Efficiency) and Creative Output (Creative Autonomy, Innovation, and Ease of Workflow). Data were collected from 60 purposively selected respondents through a researcher-developed questionnaire and analyzed using weighted mean, standard deviation, Wilcoxon Signed-Rank Test, and Spearman’s Rank-Order Correlation. Results showed that both workflows received positive evaluations; however, the traditional Figma workflow consistently obtained higher ratings. Significant differences were found in Functional Suitability, Usability, and Creative Autonomy, favoring the traditional workflow, while no significant differences were identified in Performance Efficiency, Innovation, and Ease of Workflow. Strong positive correlations were also observed between Design Quality and Creative Output in both workflows. The findings suggest that while AI-assisted tools improve workflow efficiency and convenience, traditional Figma workflows remain more effective in supporting usability, functional accuracy, and creative control. The study concludes that AI-assisted design tools are most effective when used as complementary technologies alongside human creativity and manual design expertise.

creative output, design quality, prototyping performance, usability, workflow efficiency, web designs, artificial intelligence, Figma

1. H. P. S. M. F. I. Ghorban Tanhaei, "Predictive analytics in customer behavior: Anticipating trends and preferences," vol. 17, 2024. https://www.sciencedirect.com/science/article/pii/S2666720724000924 [Google Scholar] [Crossref]

2. O. N. Akande, H. B. Akande, E. O. Asani and B. T. Dautare, "Customer Segmentation through RFM Analysis and K-means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy," 2024. https://ieeexplore.ieee.org/abstract/document/10630052/ [Google Scholar] [Crossref]

3. S. R. &. L. Vuyyuru, "Unlocking future consumer insights: Using predictive analytics and AI to shape proactive retail strategies," European Journal of Advances in Engineering and Technology, p. 110–115, 2023. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5283693 [Google Scholar] [Crossref]

4. J. Tang, "Unlocking Retail insights: predictive modeling and customer segmentation through data analytics," Journal of Theoretical and Applied Electronic Commerce Research, vol. 2, no. 20, p. 59, 2025. https://www.mdpi.com/0718-1876/20/2/59 [Google Scholar] [Crossref]

5. R. K. &. N. N. Koppichetti, " Customer Retention with Predictive Analytics in the Retail Industry," International Journal of Multidisciplinary Research and Growth Evaluation, vol. 03, p. 776, 2022. https://repository.rsis.international/ijrias/11/1/predictive-sales-and-inventory-system-with-customer-segmentation-for-enhance-customer-relationship-management-5918 [Google Scholar] [Crossref]

6. M. Z. Rodriguez et al., "Clustering algorithms: A comparative approach," PLoS One, 2019. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210236 [Google Scholar] [Crossref]

7. A. R. Mendoza, "POINT OF SALE SYSTEM WITH INVENTORY FOR ARM’S FOOD AND DELICACIES," International Journal of Advanced Research in Computer Science, vol. 14, no. 1, p. 1371, 2019. https://www.researchgate.net/profile/Louie-Agustin/publication/337707746_POINT_OF_SALE_SYSTEM_WITH_INVENTORY_FOR_ARM'S_FOOD_AND_DELICACIES/links/5de653e2a6fdcc2837010d26/POINT-OF-SALE-SYSTEM-WITH-INVENTORY-FOR-ARMS-FOOD-AND-DELICACIES.pdf [Google Scholar] [Crossref]

8. Kamnerddee, C., Putjorn, P., & Intarasirisawat, J. (2024). AI-Driven Design Thinking: A Comparative Study of Human-Created and AI-Generated UI Prototypes for Mobile Applications. 2024 8th International Conference on Information Technology (InCIT), 237–242. https://doi.org/10.1109/InCIT63192.2024.10810565 [Google Scholar] [Crossref]

9. Dahibhate, Y. (2021). A Study on the Intersection of AI and Human Creativity. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5945094 [Google Scholar] [Crossref]

10. Fu, Y., Zhou, T., Allen, P. G., Chen, Y., Wobbrock, J. O., & Hiniker, A. (2024). Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers’ Creative Thinking. https://arxiv.org/abs/2411.00168 [Google Scholar] [Crossref]

11. Akça, Y. K. (n.d.). GENERATIVE AI IN THE UX/UI DESIGN PROCESS: DEFINING KEY CONSIDERATIONS FOR DESIGNER-CENTERED TOOLS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY. https://open.metu.edu.tr/handle/11511/119032 [Google Scholar] [Crossref]

12. Priefer, D., Rost, W., Strüber, D., Taentzer, G., & Kneisel, P. (2021). Applying MDD in the content management system domain: Scenarios, tooling, and a mixed-method empirical assessment. Software and Systems Modeling, 20(6), 1919–1943. https://doi.org/10.1007/s10270-021-00872-3 [Google Scholar] [Crossref]

13. Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE Publications. https://books.google.com/books?hl=en&lr=&id=83L2EAAAQBAJ&oi=fnd&pg=PA18&dq=Field,+A.+(2018).+Discovering+Statistics+Using+IBM+SPSS+Statistics+(5th+ed.).+SAGE+Publications.+&ots=UbPYFrFOCG&sig=J7Lr-jazguWApIUeYUujQjtVc3g [Google Scholar] [Crossref]

14. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://psycnet.apa.org/record/1977-25733-001 [Google Scholar] [Crossref]

15. Fu, X., et al. (2024). The impact of generative AI on the creative process: Authorial identity and satisfaction. Proceedings of the CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613904.3642452 [Google Scholar] [Crossref]

16. Mustafa, A. (2023). The impact of artificial intelligence on UX design: Efficiency vs. creativity. Journal of Applied UX Research. https://www.researchgate.net/publication/371842858_The_Impact_of_Artificial_Intelligence_on_UX_Design [Google Scholar] [Crossref]

17. Namoun, A., & Alanezi, A. (2024). Evaluating user interface quality and user satisfaction in the age of generative AI. IEEE Access. https://ieeexplore.ieee.org/document/10363228 [Google Scholar] [Crossref]

18. Ngo, L., Ranabhat, R., & LAB University of Applied Sciences. (2025). UI design in the era of AI [Thesis].https://www.theseus.fi/bitstream/handle/10024/908915/Ngo_Ranabhat.pdf?sequence=4&isAllowed=y [Google Scholar] [Crossref]

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