Integration Theory of Inventive Problem Solving into the AIGC Design Method: An Empirical Study

This study integrates the theory of inventive problem solving (TRIZ) to reconstruct the method of artificial intelligence generated content (AIGC) design. In order to reveal the difference between AIGC design and AIGC+TRIZ design methods, an empirical study for undergraduate students (n = 20) was designed. The independent variables of the experiment were different types of design methods (AIGC design, AIGC+TRIZ design), the dependent variables of the experiment were process behavior data (behavior coding analysis), quality of design works (design experts’ score), subjective scale (NASA-TLX scale, Attitudes toward artificial intelligence at work), semi-structured interview, and Bluetooth speaker design as the experimental task. The study found that in terms of design behavior, the AIGC+TRIZ method significantly reduced the dependence on external resources, improved the use efficiency and local optimization ability of text AI. In terms of the quality of design works, AIGC+TRIZ method showed statistical significance in the dimensions of innovation, functionality, feasibility and conflict resolution (p < 0.05). Finally, in terms of cognitive load and attitude toward AIGC, AIGC+TRIZ method improved the temporal demand and performance (p < 0.05). This synergy accelerates the development of innovative solutions and conceptual design, and forms a “methodology-technology” collaborative innovation paradigm to produce more effective and innovative design results.