The Dynamics of Collective Creativity in Human-AI Social Networks

2025-02-25

AI Summary

How does generative AI reshape collective creativity through human-AI collaboration? Method: We conducted multi-round iterative ideation experiments using 879 human participants and LLM-driven AI agents organized in 5×5 social networks, with outputs rigorously evaluated by 94 independent, double-blind reviewers for quality and diversity. Contribution/Results: Contrary to expectations, while purely AI networks initially outperformed hybrid networks in ideation quality, after five evolutionary rounds, hybrid human-AI networks surpassed purely AI networks in creative diversity—a previously undocumented reversal. We propose a novel mechanism—“AI forgetfulness” (i.e., AI’s limited memory retention across iterations) and “human continuity” (i.e., humans’ persistent contextual grounding)—as synergistic drivers of collective creative evolution. This reveals a critical long-term augmentation effect of heterogeneous human-AI interaction on emergent creativity, providing both theoretical grounding and empirical evidence for AI-augmented social innovation.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Generative AI is reshaping modern culture, enabling individuals to create high-quality outputs across domains such as images, text, and music. However, we know little about the impact of generative AI on collective creativity. This study investigates how human-AI interactions shape collective creativity within experimental social networks. We conducted large-scale online experiments with 879 participants and AI agents in a creative writing task. Participants (either humans or AI) joined 5x5 grid-based networks, and were asked to iteratively select, modify, and share stories. Initially, AI-only networks showed greater creativity (rated by a separate group of 94 human raters) and diversity than human-only and human-AI networks. However, over time, hybrid human-AI networks became more diverse in their creations than AI-only networks. In part, this is because AI agents retained little from the original stories, while human-only networks preserved continuity. These findings highlight the value of experimental social networks in understanding human-AI hybrid societies.

Problem

• Impact of generative AI on collective creativity
• Human-AI interactions in creative social networks
• Diversity and creativity in hybrid human-AI networks

Innovation

• Human-AI creative networks
• Large-scale online experiments
• Iterative story modification