Comparative Analysis of Web Design Performance: AI‑Assisted Tools Versus Traditional Figma‑Based Design (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
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.