Research on Simplified Design of Interface Interaction Process Based on TRIZ and Generative AI

Digital applications frequently suffer from interaction overhead caused by functional coupling in user interfaces, leading to high cognitive load and complex interaction flows. This study proposes a GAI-TRIZ framework that integrates generative AI with TRIZ (theory of inventive problem solving) methodology under human-centered AI principles to systematically optimize interface interaction processes. Following a “Human-in-the-loop” paradigm, GAI handles automated process functional analysis and trimming candidate generation, while human experts retain validation authority over AI-generated proposals. We applied the framework to a representative e-commerce shopping flow and conducted a within-subjects user study (N = 12). Results demonstrate significant improvements: task completion time reduced by 31.2% (p < 0.01), interaction steps decreased by 42.1% (p < 0.01), and System Usability Scale scores improved from 58.3 to 76.8 (p < 0.05). These findings validate that the GAI-TRIZ framework effectively reduces procedural burden while maintaining user control through structured human–AI collaboration.