构建用户与AI交互的三层次行为分类框架，助力更安全智能的对话系统设计。
Taxonomy of User Needs and Actions
- 基于1193次人机对话分析，提出分层行为分类体系
- 涵盖信息获取、内容创作、社交互动等六类核心用户行为
- 适合研究者与产品设计者评估和优化对话系统
随着对话式AI日益普及，亟需能捕捉用户工具性目标及其情境化、适应性与社会性实践的框架。现有对话行为分类或过于泛化、或局限于特定领域、或将交互简化为有限对话功能。为此，本文提出基于实证研究的《用户需求与行为分类框架》（TUNA），通过1193次人机对话的迭代质性分析，并结合理论综述与跨场景验证建立。TUNA将用户行为组织为三层结构，涵盖信息寻求、整合、流程指导、内容创作、社交互动及元对话等类别。该框架聚焦用户自主性与使用适配实践，支持多尺度评估，促进产品间政策协调，并可作为领域特定分类的底层架构。本研究为描述AI使用提供系统化术语，推动对话系统在安全性、响应性与可问责性方面的学术理解与实践改进。
原文摘要 · Abstract (English)
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.
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