# How AI Companionship Develops: Evidence from a Longitudinal Study

arXiv: https://arxiv.org/abs/2510.10079 · HTML: https://mlsanity.com/paper/2510.10079/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2025.

## Built on by (most cited first, 6 of 6)

- [Sycophantic AI makes human interaction feel more effortful and less satisfying over time](https://mlsanity.com/paper/2605.07912/) (2026)
- [VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents](https://mlsanity.com/paper/2603.23840/) (2026)
- [Best Friends, Not Forever: Evaluating Long-Horizon Persona Collapse and Behavioral Drift in AI Companions](https://mlsanity.com/paper/2607.28818/) (2026)
- [Lexical Indicators of Mind Perception in Human-AI Companionship](https://mlsanity.com/paper/2604.04105/) (2026)
- [Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts](https://mlsanity.com/paper/2606.18258/) (2026)
- [Longitudinal Evidence That General-Purpose Chatbots Actively Foster Relational Engagement](https://mlsanity.com/paper/2608.10672/) (2026)

## Abstract (arXiv)

The quickly growing popularity of AI companions poses risks to mental health, personal wellbeing, and social relationships. Past work has identified many individual factors that can drive human-companion interaction, but we know little about how these factors interact and evolve over time. In Study 1, we surveyed AI companion users (N = 303) to map the psychological pathway from users' mental models of the agent to parasocial experiences, social interaction, and the psychological impact of AI companions. Participants' responses foregrounded multiple interconnected variables (agency, parasocial interaction, and engagement) that shape AI companionship. In Study 2, we conducted a longitudinal study with a subset of participants (N = 110) using a new generic chatbot. Participants' perceptions of the generic chatbot significantly converged to perceptions of their own companions by Week 3. These results suggest a longitudinal model of AI companionship development and demonstrate an empirical method to study human-AI companionship.