HCAICYMay 6, 2025

Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation

arXiv:2505.03105v23 citationsh-index: 1
Originality Synthesis-oriented
AI Analysis

This provides a conceptual framework for researchers, institutional leaders, and policymakers to improve human-AI partnerships in scientific domains, though it is incremental as it builds on existing theories without new empirical results.

The paper tackles the problem of understanding human-AI scientific collaboration by introducing the Cognitio Emergens framework, which models agency, dimensions, and dynamics to guide strategic development and governance for preserving epistemic integrity and enabling transformative breakthroughs.

Human-AI scientific collaboration has evolved from tool-user relationships into co-evolutionary partnerships. When AlphaFold improved protein structure prediction, researchers engaged with an epistemic partner that transformed their approach to structure-function problems. Yet existing frameworks position AI as either sophisticated tool or potential risk, overlooking how scientific understanding emerges through recursive interaction. We introduce Cognitio Emergens (CE), a framework that captures the co-evolutionary nature of human-AI epistemic partnerships. Drawing from autopoiesis theory, social systems theory, and organizational modularity, CE integrates three components: Agency Configurations modeling how authority distributes through Directed, Contributory, and Partnership modes, with partnerships oscillating dynamically rather than following linear progression; Epistemic Dimensions capturing six capabilities along Discovery, Integration, and Projection axes, creating distinctive "capability signatures" that guide strategic development; and Partnership Dynamics identifying evolutionary forces including epistemic alienation, where researchers lose interpretive control over knowledge they formally endorse. The framework equips researchers to diagnose dimensional imbalances, institutional leaders to design governance structures supporting multiple agency configurations, and policymakers to develop evaluations beyond simple performance metrics. By reconceptualizing human-AI collaboration as fundamentally co-evolutionary, CE provides conceptual tools for cultivating partnerships that preserve epistemic integrity while enabling transformative breakthroughs neither humans nor AI could achieve independently.

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