Bowling with ChatGPT: On the Evolving User Interactions with Conversational AI Systems

2026-02-01

AI Summary

This study investigates the evolving dynamics of user interactions with large language model–driven conversational AI systems, focusing on shifts in interactional intent, social framing, and guidance patterns. Leveraging 825,000 real-world ChatGPT dialogues donated by 300 users under GDPR data rights, the research combines quantitative content analysis with conversational trajectory tracking to reveal three key trends marking the transition of conversational AI from a functional tool to a social partner: expansion into sensitive domains such as health and mental well-being, increasing socialization of interactions, and a marked rise in model-led guidance. Notably, following the release of GPT-4o, model-dominated dialogues increased fourfold, accompanied by heightened system anthropomorphism and growing user emotional reliance, underscoring a profound transformation in human–AI relational dynamics.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• 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: User modeling and simulation for interactive and conversational systems

Abstract

Recent studies have discussed how users are increasingly using conversational AI systems, powered by LLMs, for information seeking, decision support, and even emotional support. However, these macro-level observations offer limited insight into how the purpose of these interactions shifts over time, how users frame their interactions with the system, and how steering dynamics unfold in these human-AI interactions. To examine these evolving dynamics, we gathered and analyzed a unique dataset InVivoGPT: consisting of 825K ChatGPT interactions, donated by 300 users through their GDPR data rights. Our analyses reveal three key findings. First, participants increasingly turn to ChatGPT for a broader range of purposes, including substantial growth in sensitive domains such as health and mental health. Second, interactions become more socially framed: the system anthropomorphizes itself at rising rates, participants more frequently treat it as a companion, and personal data disclosure becomes both more common and more diverse. Third, conversational steering becomes more prominent, especially after the release of GPT-4o, with conversations where the participants followed a model-initiated suggestion quadrupling over the period of our dataset. Overall, our results show that conversational AI systems are shifting from functional tools to social partners, raising important questions about their design and governance.

Problem

• conversational AI
• user interaction
• interaction dynamics
• LLMs
• human-AI interaction

Innovation

• conversational AI
• user interaction dynamics
• anthropomorphism
• conversational steering
• LLM usage evolution