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.

翻译：内省是身份建构和未来规划的核心，然而大多数数字工具将自我视为统一实体。与之相对，对话自我理论（Dialogue Self Theory, DST）认为自我由多种内在视角（如价值观、关切和抱负）构成，这些视角之间可能产生冲突或对话。基于这一观点，我们设计了InnerPond——一种以多智能体系统形式呈现的研究探针，将上述内在视角表示为基于大语言模型（LLM）的独立智能体以支持内省。其设计通过空间隐喻、交互支架和对话编排的迭代探索逐步成型，最终转化为一个用于组织并关联多重内在视角的共享空间环境。在针对17名面临职业选择困境的青年开展的用户研究中，参与者通过以下方式使用该探针：与AI共同创造内在声音、构建关系性内在景观，以及以观察者和调解者身份编排对话——这揭示了此类系统如何支持内省。总体而言，本研究为支持探索自我多元性的AI辅助内省工具提供了设计启示。