InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for Introspection

Abstract

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.

Figures (10)

• Figure 1: InnerPond fosters AI-mediated introspection through multi-agent dialogue. The system transforms users' I-positions into conversing LLM agents (lotus leaves), supporting dialogue among multiple I-positions within the self.
• Figure 2: Core concepts of Dialogical Self Theory, illustrating the relationship between multiple I-positions in the dialogical space and a meta-position.
• Figure 3: Three-phase metaphor evolution for the dialogical space: From group chat as battle ring (Phase 1), to stones highlighting individuality yet emphasizing separation (Phase 2), to lotus leaves appearing distinct yet sharing roots, enabling a meta-position perspective (Phase 3).
• Figure 4: I-position extraction and story enrichment pipeline: user knowledge is transformed into distinct I-positions, iteratively refined through scaffolding questions and user responses.
• Figure 5: The interface for [Stage 1: I-position Construction]: (a) I-positions visualized as lotus leaves on the main pond view, with a profile modal showing name, viewpoint, and narrative; (b) "Story Enrichment" modal with scaffolding questions to refine the narrative; (c) "1:1 Dialogue" modal for direct conversation with a leaf agent.