Two-player Alternate Uses Test: A Controlled Testbed for Interactive Human-AI and Human-Human Co-Creation

2026-07-08

AI Summary

This study addresses the challenge in current AI creativity research of simulating authentic collaborative dynamics while maintaining experimental control, due to the absence of comparable co-creative contexts. The authors introduce a controlled, turn-taking alternate uses test platform that, for the first time, enables direct comparison between human–human and human–AI interactive co-creativity under rigorously matched conditions, incorporating a “creative seed” intervention mechanism. Integrating a GPT-4 interaction system, a behavioral experimentation platform, psychological scales (e.g., BAS Drive), and multidimensional creativity metrics, the research reveals that GPT-4 partners generate ideas of originality comparable to human partners within identical time constraints. Furthermore, individual motivation moderates the positive effect of interaction on originality, cognitive offloading diminishes originality in human partners, and prior exposure to highly creative ideas significantly enhances subsequent performance. This framework disentangles three classes of influences—participant traits, partner perception, and content dynamics—offering a novel paradigm for AI co-creativity research.

Abstract

Controlled research on AI ideation typically compares independent agents, while field studies of human-AI collaboration sacrifice experimental control. We introduce a controlled, two-player extension of the Alternate Uses Test (AUT) that enables comparison of human-human and human-AI co-creation under matched interactive conditions, alongside calibrated non-interactive baselines. The platform supports decomposition of performance into three typically confounded factors: participant traits, partner perceptions, and content dynamics. An in-person pilot (N = 62) demonstrates its utility. Under matched time limits, originality with a GPT-4 partner is statistically equivalent to that with a human partner. Approach motivation (BAS Drive) moderates whether interactive partnership benefits originality, and self-reported cognitive outsourcing predicts lower originality specifically in human-human dyads. Prior exposure to highly creative ideas improves later performance, suggesting a "seeding" intervention. We release the platform, code, and dataset as a shared testbed for controlled studies of human-AI co-creation.

Problem

• human-AI collaboration
• co-creation
• Alternate Uses Test
• experimental control
• interactive ideation

Innovation

• human-AI co-creation
• Alternate Uses Test
• controlled experiment
• creative collaboration
• cognitive outsourcing