Human-AI Collaboration: Designing Generative AI and Training Users to Engage in Collaborative Patterns

Abstract

Generative AI (GenAI) applications are increasingly adopted in co-creative workflows, taking an active role in design and development tasks. GenAI offers unique opportunities through content generation and synthesis across resources, yet it can lead to losses in co-creativity, due to limited collaborative interactions between humans and GenAI. This loss results in user overreliance on GenAI outputs, skill degradation, and reduced creativity. This study compares two GenAI interaction styles: Standard, providing direct outputs to user prompts; and Collaborative, supporting problem definition, diverging, converging, and organizing. A user training intervention was also assessed. Findings show that collaborative GenAI led to higher perceptions of collaboration, including exploration and expressiveness. In contrast, standard GenAI led to more direct use of the AI ideas, with fewer discussions and evaluations. Training supported more problem-definition and less direct use of AI ideas without discussion. Both AI teammate design and user training contribute to fostering collaboration in HATs.