AI-Wrapped: Participatory, Privacy-Preserving Measurement of Longitudinal LLM Use In-the-Wild
This addresses the challenge for alignment researchers in measuring real-world LLM interactions while preserving privacy, though it is incremental as it builds on existing participatory methods.
The researchers tackled the problem of accessing naturalistic LLM usage data due to privacy constraints by developing AI-Wrapped, a workflow that collects such data while providing participants with personalized reports, and found from an initial deployment with 82 U.S. adults across 48,495 conversations that usage included emotional themes and patterns like over-reliance.
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.