Human-AI Synergy Supports Collective Creative Search

2026-02-10

AI Summary

This study investigates the impact of generative AI on the quality and diversity of collective creative output in human-AI collaboration. Through a controlled word-guessing task, the authors compare creative performance across purely human, purely AI, and hybrid human-AI groups, employing semantic similarity metrics and a controlled experimental design to uncover higher-order interaction effects. The findings reveal that humans and AI mutually adapt and dynamically adjust their strategies during collaboration. Hybrid groups not only achieve superior task performance but also sustain higher output diversity compared to homogeneous AI teams. Moreover, human-AI collaboration demonstrates stronger complementary advantages than AI-only ensembles, underscoring its unique value in co-creative generation.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Generation

Application Category

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

Abstract

Generative AI is increasingly transforming creativity into a hybrid human-artificial process, but its impact on the quality and diversity of creative output remains unclear. We study collective creativity using a controlled word-guessing task that balances open-endedness with an objective measure of task performance. Participants attempt to infer a hidden target word, scored based on the semantic similarity of their guesses to the target, while also observing the best guess from previous players. We compare performance and outcome diversity across human-only, AI-only, and hybrid human-AI groups. Hybrid groups achieve the highest performance while preserving high diversity of guesses. Within hybrid groups, both humans and AI agents systematically adjust their strategies relative to single-agent conditions, suggesting higher-order interaction effects, whereby agents adapt to each other's presence. Although some performance benefits can be reproduced through collaboration between heterogeneous AI systems, human-AI collaboration remains superior, underscoring complementary roles in collective creativity.

Problem

• Human-AI collaboration
• collective creativity
• creative output diversity
• generative AI
• creative performance

Innovation

• human-AI collaboration
• collective creativity
• generative AI
• creative search
• interaction effects