Interaction-Centered Intelligence: Toward an Interaction-Based Theory of Human-AI Co-Creation

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computational Creativity, Human-Computer Interaction · Depth: Expert, extended

Summary

The paper "Interaction-Centered Intelligence" introduces a new paradigm for understanding human-AI co-creation, proposing interaction itself as the primary unit of analysis, rather than isolated computation or static outputs. It synthesizes theories like distributed cognition, embodied cognition, enaction, and participatory sense-making, which progressively shifted the view of intelligence from internal processes to relational dynamics. The framework is computationally operationalized through Creative Sense-Making and quantified co-creation, which model evolving interaction dynamics via activity traces, creative trajectories, and sense-making curves. This approach emphasizes that intelligence, creativity, and meaning emerge from ongoing participation, coordination, and adaptive regulation between humans, AI systems, and their environments, exemplified by systems like the Drawing Apprentice and AI Drawing Partner.

Key takeaway

For AI Architects designing collaborative systems, you should shift evaluation from static outputs to dynamic interaction metrics. Focus on building systems that can monitor and adapt to interaction trajectories, participatory balance, and coordination patterns. This will enable more robust, human-centered AI that fosters sustained engagement and emergent creativity, moving beyond simple prompt-response models.

Key insights

Intelligence emerges from dynamic human-AI interaction, not isolated computation or static outputs.

Principles

• Intelligence is relational and participatory.
• Interaction dynamics reveal emergent cognition.
• Interactional coherence predicts co-creative success.

Method

Model interaction dynamics computationally using activity traces, interaction histories, creative trajectories, and sense-making curves to quantify participation, coordination, and conceptual shifts in human-AI collaboration.

In practice

• Analyze interaction trajectories for collaboration health.
• Visualize participation balance in co-creative tools.
• Develop systems for adaptive interaction regulation.

Topics

• Human-AI Co-creation
• Interaction-Centered AI
• Distributed Cognition
• Computational Creativity
• Enactive AI
• Quantified Co-creation

Best for: AI Scientist, Research Scientist, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.