Cognitive Trajectory Modeling: Quantifying Human-AI Co-Creation through Cognitively Grounded Interaction Trajectories
arXiv:2606.15358v1 Announce Type: cross Abstract: Co-creative AI research increasingly seeks methods capable of representing how interaction dynamics evolve through time. While many existing approaches focus on observable interaction characteristics, interaction metrics, behavioral coding schemes, or activity traces, these methods often struggle to capture higher-order interaction dynamics, including how collaborative processes reorganize, stabilize, regulate, and evolve through time. This paper introduces Cognitive Trajectory Modeling (CTM) as a cognitive theory of interaction dynamics that c
The increasing sophistication and integration of AI into human workflows necessitate more robust methods for understanding and optimizing human-AI collaboration beyond mere observable metrics.
Advanced cognitive models for human-AI interaction will be crucial for developing more effective, adaptable, and truly co-creative AI systems, impacting productivity and the nature of work.
This research shifts the focus from superficial interaction characteristics to deeper cognitive dynamics, potentially enabling AI to better understand and adapt to human thought processes during collaboration.
- · AI developers
- · Cognitive science researchers
- · Companies implementing AI co-creation tools
- · Knowledge workers
- · Platforms with simplistic AI interaction models
- · Legacy AI systems lacking dynamic adaptability
Improved human-AI co-creation efficiency and quality of output through better understanding of interaction dynamics.
Development of next-generation AI agents capable of anticipatory and adaptive collaborative behaviors based on cognitive recognition.
Potential for new ethical and philosophical considerations regarding the 'mind-reading' capabilities of AI in collaborative contexts, challenging notions of individual thought and creativity.
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Read at arXiv cs.AI