Adapting AI to the Moment: Understanding the Dynamics of Parent-AI Collaboration Modes in Real-Time Conversations with Children

2026-03-29

AI Summary

Existing parent–AI collaboration systems predominantly rely on static designs that struggle to accommodate the dynamically shifting contextual demands of parent–child conversations. Addressing this limitation, this study employs a co-design methodology to develop COMPASS, a probe system enabling parents to flexibly combine AI-assisted functions. Through a controlled experiment and qualitative analysis involving 21 parent–child dyads, the research uncovers systematic mechanisms by which collaborative patterns evolve in response to conversational context. The findings delineate three dynamic strategies—parent-oriented, child-oriented, and relationship-oriented—that adaptively guide interactions. This work provides both theoretical grounding and empirical evidence for designing context-aware AI systems that support parents in highly sensitive relational settings.

Abstract

Parent-AI collaboration to support real-time conversations with children is challenging due to the sensitivity and open-ended nature of such interactions. Existing systems often simplify collaboration into static modes, providing limited support for adapting AI to continuously evolving conversational contexts. To address this gap, we systematically investigate the dynamics of parent-AI collaboration modes in real-time conversations with children. We conducted a co-design study with eight parents and developed COMPASS, a research probe that enables flexible combinations of parental support functions during conversations. Using COMPASS, we conducted a lab-based study with 21 parent-child pairs. We show that parent-AI collaboration unfolds through evolving modes that adapt systematically to contextual factors. We further identify three types of parental strategies--parent-oriented, child-oriented, and relationship-oriented--that shape how parents engage with AI. These findings advance the understanding of dynamic human-AI collaboration in relational, high-stakes settings and inform the design of flexible, context-adaptive parental support systems.

Problem

• parent-AI collaboration
• real-time conversations
• dynamic adaptation
• conversational context
• child interaction

Innovation

• dynamic collaboration
• context-adaptive AI
• parent-AI interaction
• real-time conversation
• co-design