Taxonomy of User Needs and Actions
This provides a systematic vocabulary for describing AI use, advancing scholarly understanding and practical design of conversational systems, though it is incremental as it builds on existing taxonomies.
The authors tackled the lack of comprehensive frameworks for conversational AI by developing the Taxonomy of User Needs and Actions (TUNA), an empirically grounded framework based on analysis of 1193 human-AI conversations, which organizes user actions into a three-level hierarchy to support evaluation and design.
The growing ubiquity of conversational AI highlights the need for frameworks that capture not only users' instrumental goals but also the situated, adaptive, and social practices through which they achieve them. Existing taxonomies of conversational behavior either overgeneralize, remain domain-specific, or reduce interactions to narrow dialogue functions. To address this gap, we introduce the Taxonomy of User Needs and Actions (TUNA), an empirically grounded framework developed through iterative qualitative analysis of 1193 human-AI conversations, supplemented by theoretical review and validation across diverse contexts. TUNA organizes user actions into a three-level hierarchy encompassing behaviors associated with information seeking, synthesis, procedural guidance, content creation, social interaction, and meta-conversation. By centering user agency and appropriation practices, TUNA enables multi-scale evaluation, supports policy harmonization across products, and provides a backbone for layering domain-specific taxonomies. This work contributes a systematic vocabulary for describing AI use, advancing both scholarly understanding and practical design of safer, more responsive, and more accountable conversational systems.