Incentives shape how humans co-create with generative AI
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Abstract (arXiv)
Generative AI is quickly becoming an integral part of people's everyday workflows. Early evidence has shown that while generative AI can increase individual-level productivity, it does so at the cost of collective diversity, potentially narrowing the set of ideas and perspectives produced. Our research stands in contrast to this concern: through a pre-registered randomized control trial, we show that incentives mediate AI's homogenizing force in a creative writing task where participants can use AI interactively. Participants rewarded for originality relative to peers produce collectively more diverse writing than those rewarded for quality alone. This divergence is driven not by abandoning AI, but by how participants use it: those incentivized for originality incorporate fewer AI suggestions verbatim, relying on the model more selectively for brainstorming, proofreading, and targeted edits. Our results reveal that the effects of generative AI depend not only on the technology itself, but also the behavioral strategies and incentive structures surrounding its use.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 4 Apr 2026.
Builds on
21 of 41 references.
References · 21
- 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
- 2023Whose Opinions Do Language Models Reflect?cited by 759
- 2022Contrastive Decoding: Open-ended Text Generation as Optimizationcited by 598
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- 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 165
- 2025EmbeddingGemma: Powerful and Lightweight Text Representationscited by 128
- 2024Homogenization Effects of Large Language Models on Human Creative Ideationcited by 127
- 2025Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Taskcited by 103
- 2022Same Author or Just Same Topic? Towards Content-Independent Style Representationscited by 83
- 2024AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuancescited by 59
- 2024Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinkingcited by 43
- 2025We're Different, We're the Same: Creative Homogeneity Across LLMscited by 41
9 more
- 2024Benchmarking Linguistic Diversity of Large Language Modelscited by 37
- 2024Generative Monoculture in Large Language Modelscited by 21
- 2023Understanding writing style in social media with a supervised contrastively pre-trained transformercited by 11
- 2024Competition and Diversity in Generative AIcited by 10
- 2024Human-AI Co-Creativity: Exploring Synergies Across Levels of Creative Collaborationcited by 9
- 2025Recognising, Anticipating, and Mitigating LLM Pollution of Online Behavioural Researchcited by 3
- 2026Incentive-Tuning: Understanding and Designing Incentives for Empirical Human-AI Decision-Making Studiescited by 2
- 2026The Digital Divide in Generative AI: Evidence from Large Language Model Use in College Admissions Essayscited by 2
- 2025When Thinking Pays Off: Incentive Alignment for Human-AI Collaborationcited by 1
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Citing papers per year: 2026: 1.
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