# Interrogating AI: Characterizing Emergent Playful Interactions with ChatGPT

arXiv: https://arxiv.org/abs/2401.08405 · HTML: https://mlsanity.com/paper/2401.08405/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 98 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation](https://mlsanity.com/paper/2204.11788/) (2022)
- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (2021)
- [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021)
- [Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook](https://mlsanity.com/paper/2301.12243/) (2023)
- [Bias in Multimodal AI: Testbed for Fair Automatic Recruitment](https://mlsanity.com/paper/2004.07173/) (2020)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [Player-AI Interaction: What Neural Network Games Reveal About AI as Play](https://mlsanity.com/paper/2101.06220/) (2021)
- [GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network](https://mlsanity.com/paper/2203.03827/) (2022)
- ["Finding the Magic Sauce": Exploring Perspectives of Recruiters and Job Seekers on Recruitment Bias and Automated Tools](https://mlsanity.com/paper/2301.11958/) (2023)

## Built on by (most cited first, 5 of 5)

- [MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education](https://mlsanity.com/paper/2404.06711/) (2024)
- [Characterizing LLM-driven Social Network: The Chirper.ai Case](https://mlsanity.com/paper/2504.10286/) (2025)
- [Mental Models of Autonomy and Sentience Shape Reactions to AI](https://mlsanity.com/paper/2512.09085/) (2025)
- [Conceptualization, Operationalization, and Measurement of Machine Companionship: A Scoping Review](https://mlsanity.com/paper/2506.18119/) (2025)
- [CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration](https://mlsanity.com/paper/2509.11461/) (2025)

## Abstract (arXiv)

In an era of AI's growing capabilities and influences, recent advancements are reshaping HCI and CSCW's view of AI. Playful interactions emerged as an important way for users to make sense of the ever-changing AI technologies, yet remained underexamined. We target this gap by investigating playful interactions exhibited by users of a popular AI technology, ChatGPT. Through a thematic analysis of 372 user-generated posts on the ChatGPT subreddit, we found that more than half (54%) of user discourse revolved around playful interactions. The analysis further allowed us to construct a preliminary framework to describe these interactions, categorizing them into six types: reflecting, jesting, imitating, challenging, tricking, and contriving; each included sub-categories. This study contributes to HCI and CSCW by identifying the diverse ways users engage in playful interactions with AI. It examines how these interactions can help users understand AI's agency, shape human-AI relationships, and provide insights for designing AI systems.