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

TL;DR

InnerPond is a multi-agent system inspired by Dialogical Self Theory, enabling users to explore and dialogue with multiple internal perspectives for enhanced introspection.

Contribution

This work introduces a novel multi-agent system that models the self's internal perspectives and facilitates dialogical introspection through spatial and conversational design.

Findings

Participants engaged in co-creating inner voices with AI.

The system supported organizing and relating multiple inner perspectives.

User interactions provided insights into AI-supported self-exploration.

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.