Augmented Learning for Joint Creativity in Human-AI Co-Creation

Our research introduces the theory of augmented learning by reconceptualizing its traditional definition and providing both theoretical insights and empirical evidence to explain its role in human-AI co-creation for enhancing joint creativity over time. We propose shifting the focus of augmented learning from traditional human cognitive learning to a collective learning process, where humans and AI collaboratively rearrange their levels of involvement in co-creation activities to continuously improve joint creativity over time. To explore this reconceptualized augmented learning, we adopted a mixed-methods approach across three studies. Studies 1 and 2 inductively revealed that human-AI co-creation does not automatically achieve augmented learning and enhance their joint creativity even after multiple rounds of co-creation experiences. A deeper qualitative analysis of human-AI dialogues identified a decline in Idea Co-development—a co-creation activity characterized by feedback exchanges and iterative idea refinement—as the primary reason for this failure. Based on this finding, Study 3 deductively demonstrated that providing instructions and guidance on Idea Co-development improved joint creativity over time. Building on these findings, we further refine our theory of augmented learning, offering actionable insights for optimizing human-AI co-creation to achieve greater joint creativity.