用多智能体模拟内心对话，帮人探索自我多元性。
InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for Introspection
- 将内心不同视角建模为独立AI代理，形成可交互的对话空间。
- 17名年轻人通过共创内声音、构建关系图景，深化职业选择反思。
- 适合想深度自省或做心理辅助工具设计的研究者。
introspection 是身份建构与未来规划的核心，但多数数字工具将自我视为单一实体。相反，对话式自我理论（DST）认为自我由多个内部视角组成，如价值观、关切与抱负，这些视角可能相互冲突或对话。受此启发，我们设计了 InnerPond——一个基于多智能体系统的探究性原型，将内部视角表示为基于LLM的独立智能体以支持内省。其设计经过对空间隐喻、互动结构和对话协调的迭代探索，最终形成共享空间环境来组织和关联多个内在视角。在一项针对17名青年成年人的职业选择研究中，参与者通过与AI共同创建内在声音、构想关系内景，并以观察者和调解者的角色进行对话，揭示此类系统如何支持内省。本研究为支持内省的AI工具设计提供了实践启示。
原文摘要 · Abstract (English)
Introspection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view, we designed InnerPond, a research probe in the form of a multi-agent system that represents these internal perspectives as distinct LLM-based agents for introspection. Its design was shaped through iterative explorations of spatial metaphors, interaction scaffolding, and conversational orchestration, culminating in a shared spatial environment for organizing and relating multiple inner perspectives. In a user study with 17 young adults navigating career choices, participants engaged with the probe by co-creating inner voices with AI, composing relational inner landscapes, and orchestrating dialogue as observers and mediators, offering insight into how such systems could support introspection. Overall, this work offers design implications for AI-supported introspection tools that enable exploration of the self's multiplicity.
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