AI-Wrapped: Participatory, Privacy-Preserving Measurement of Longitudinal LLM Use In-the-Wild

TL;DR

This paper introduces AI-Wrapped, a privacy-preserving workflow for collecting naturalistic LLM usage data with participant reports, revealing usage patterns, emotional themes, and privacy concerns in real-world interactions.

Contribution

It presents a novel participatory method for measuring LLM use in-the-wild that balances data collection with privacy and transparency considerations.

Findings

Participants used LLMs for instrumental and reflective purposes.

Heavy users engaged in more reflective exchanges.

Participants remain hesitant to share chat data due to privacy concerns.

Abstract

Alignment research on large language models (LLMs) increasingly depends on understanding how these systems are used in everyday contexts. Yet naturalistic interaction data is difficult to access due to privacy constraints and platform control. We present AI-Wrapped, a prototype workflow for collecting naturalistic LLM chatbot usage data while providing participants with an immediate "wrapped"-style report on their usage statistics, top topics, and behavioral patterns. We report findings from an initial deployment with 82 U.S.-based adults across 48,495 conversations from their 2025 chat histories. Participants used LLMs for both instrumental and reflective purposes and had topics with emotional or existential themes. Some usage patterns reflect potential over-reliance or perfectionism. Heavy users showed comparatively more reflective exchanges than primarily transactional ones. Methodologically, even with zero data retention and PII removal, participants may remain hesitant to share chat data due to perceived privacy and judgment risks, underscoring the importance of transparent design when building measurement infrastructure for alignment research.