Evaluating AI’s Ideas: The Role of Individual Creativity and Expertise in Human-AI Co-Creativity

As generative artificial intelligence (genAI) becomes increasingly integrated into education and work, understanding who benefits most from human-AI collaboration is crucial. This study examines how domain expertise and individual differences—creative self-efficacy and baseline creative ability—influence human-AI co-creativity in an engineering design task. Using pre-generated ideas from GPT-3.5-turbo, engineering (N = 99) and psychology students (N = 212) generated an initial solution, evaluated AI-generated ideas, and revised their idea. Linear mixed-effects models demonstrated expertise and generation ability predicted solution quality. Engineering students produced more original and effective solutions, yet both groups improved comparably supporting the “rising tides lift all boats” hypothesis. A novel categorization scheme revealed group differences in idea inspiration: engineers generated more novel solutions, while psychology students tended to adopt existing ideas. These findings highlight the role of domain knowledge and individual differences in pre-existing creativity in maximizing human-AI co-creativity, emphasizing the need to develop these human abilities alongside genAI.