EEG assessment of artificial intelligence-generated content impact on student creative performance and neurophysiological states in product design - Shuxin Wang; Xin Tao; Hongbo Ma; Fanglian Li; Chuanqi Wu (Frontiers in Psychology, 2025)
ObjectivesThe purpose of this study has been to evaluate the use of Artificial Intelligence-Generated Content (AIGC) tools in design education, in terms of their effects on creative performance, concentration, and relaxation levels, for university students enrolled in an undergraduate design program.MethodsAn experimental design was implemented, using two groups differentiated by their design tool usage (AIGC tools versus traditional software). The sample consisted of 64 third-year undergraduate design students from a public university in Eastern China. Participants completed a three-hour intelligent walking cane design task. The AIGC group used ChatGPT, Midjourney, and Stable Diffusion, while the control group used traditional design software. Neurophysiological states were continuously monitored using BrainLink Pro EEG headband devices. Creative performance was evaluated using standardized design assessment criteria; concentration and relaxation levels were measured through EEG data analysis.ResultsThe study’s participants demonstrated that use of AIGC tools significantly enhanced creative performance (M = 115.13, SD = 6.44) compared to traditional methods (M = 110.69, SD = 9.37), t(62) = 2.208, p = 0.031, d = 0.55. The AIGC group showed significantly higher concentration levels (M = 51.06, SD = 2.54) than controls (M = 48.31, SD = 2.87), t(62) = 4.062, p < 0.001, d = 1.02. No significant difference was found in relaxation levels between groups (p = 0.191). Correlation analysis revealed a strong positive relationship between concentration level and creative performance (r = 0.67), while relaxation showed weaker associations (r = 0.29).ConclusionThis study has demonstrated that use of AIGC tools improves creative performance and concentration in design students, with the enhancement primarily driven by improved attentional focus and cognitive resource optimization. The integration of AIGC and EEG technologies provides objective neurophysiological evidence for understanding AI-assisted creativity in design education. It is suggested that AIGC tools should be incorporated into design curricula to enhance student creative outcomes while maintaining appropriate balance with traditional design methods.