Nonslop: A Gamified Experiment in Human-AI Collaborative Writing

# Nonslop: A Gamified Experiment in Human-AI Collaborative Writing
Source: [https://arxiv.org/abs/2606.12350](https://arxiv.org/abs/2606.12350)
[View PDF](https://arxiv.org/pdf/2606.12350)[HTML \(experimental\)](https://arxiv.org/html/2606.12350v1)

> Abstract:The rapid proliferation of large language models \(LLMs\) raises critical questions about human creativity and individual expression in an era of AI\-assisted creation\. When do humans adopt AI suggestions, and what are the implications for individual voice? This study examines these questions through a gamified writing exercise where 74 participants \(214 responses\) replied to prompts while AI\-generated word suggestions were available as they wrote\. The game simulates a dystopian future in which an AI is attempting to learn from what remains of human individuality, and disincentivizes AI\-like writing\. In doing so, it attempts to create conditions that reveal authentic user preferences rather than default behaviors, such as accepting a readily available AI\-generated suggestion\. Note that this is a deliberate inversion of the "helpful assistant" design pattern; the system is explicitly forbidding you from accepting AI suggestions\. We analyze user behavior patterns across different task types, user behaviors, and response characteristics to understand the factors influencing human\-AI interaction in creative tasks\. The study focuses on when users choose to maintain creative autonomy versus violating the rules of the game and accepting AI assistance\. It also explores how these choices relate to response patterns, task characteristics, and user behavior\. This gamified approach offers both a framework for studying authentic human\-AI interaction and a provocative lens for understanding the tension between efficiency and authenticity in AI\-augmented creativity\.

## Submission history

From: Maria Edwards \[[view email](https://arxiv.org/show-email/1df949f5/2606.12350)\] **\[v1\]**Wed, 10 Jun 2026 17:21:38 UTC \(1,268 KB\)