Who Owns Creativity and Who Does the Work? Trade-offs in LLM-Supported Research Ideation

2026-01-17

AI Summary

This study addresses the challenge of preserving researchers’ sense of contribution and ownership when leveraging large language models (LLMs) to support scientific ideation, without diminishing the essential human role through excessive automation. The authors propose an intelligent research collaboration system comprising three agent roles—Ideator, Writer, and Evaluator—and implement three levels of user control (low, medium, high) to investigate how LLM agency influences researchers’ creativity, task allocation, and perceived ownership. Through a mixed-methods experiment with 54 researchers, the study uncovers a nonlinear relationship between creativity support and control level, observes a shift in human focus from idea generation to validation, and formulates researcher-empowerment-centered design principles for LLM-augmented scientific collaboration. These findings offer empirical grounding and actionable guidance for designing human–AI collaborative research tools.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

LLM-based agents offer new potential to accelerate science and reshape research work. However, the quality of researcher contributions can vary significantly depending on human ability to steer agent behaviors. How can we best use these tools to augment scientific creativity without undermining aspects of contribution and ownership that drive research? To investigate this, we developed an agentic research ideation system integrating three roles -- Ideator, Writer, and Evaluator -- across three control levels -- Low, Medium, and Intensive. Our mixed-methods study with 54 researchers suggests three key findings in how LLM-based agents reshape scientific creativity: 1) perceived creativity support does not simply increase linearly with greater control; 2) human effort shifts from ideating to verifying ideas; and 3) ownership becomes a negotiated outcome between human and AI. Our findings suggest that LLM agent design should emphasize researcher empowerment, fostering a sense of ownership over strong ideas rather than reducing researchers to operating an automated AI-driven process.

Problem

• scientific creativity
• LLM-based agents
• research ownership
• human-AI collaboration
• research ideation

Innovation

• LLM-based agents
• research ideation
• human-AI collaboration
• creativity support
• ownership negotiation