認知湧現：人機知識共創中的能動性、維度與動力學

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

May 6, 2025

作者: Xule Lin

cs.AI

摘要

隨著人類與人工智慧系統超越工具使用者的關係，邁向共同演化的認知夥伴關係，科學知識的創造正經歷根本性的轉變。當AlphaFold革新了蛋白質結構預測領域時，研究者們描述了一種與認知夥伴的互動，這種互動重塑了他們對基礎關係的構想。本文介紹了“認知湧現”（Cognitio Emergens, CE）框架，該框架針對現有模型中的關鍵局限，這些模型聚焦於靜態角色或狹隘的指標，未能捕捉到科學理解如何通過人類與人工智慧之間遞歸的互動隨時間湧現。CE整合了三個組件來應對這些局限：描述權威如何在人類與人工智慧之間分配的“代理配置”（Directed, Contributory, Partnership），其中夥伴關係在配置間動態振盪而非線性推進；捕捉通過發現、整合與投射軸線上合作湧現的六種特定能力的“認知維度”，形成指導發展的獨特“能力簽名”；以及識別塑造這些關係演化力量的“夥伴動力學”，特別是研究者對其正式認可的知識失去解釋控制的“認知異化”風險。借鑒自創生理論、社會系統理論與組織模塊性，CE揭示了知識共創如何通過角色、價值觀與組織結構的持續協商而湧現。通過將人類與人工智慧的科學合作重新構想為根本上共同演化的過程，CE提供了一種平衡的視角，既不盲目讚揚也不無端畏懼人工智慧角色的演變，而是提供了培育夥伴關係的概念工具，這些工具在保持人類有意義參與的同時，促成了變革性的科學突破。

English

Scientific knowledge creation is fundamentally transforming as humans and AI systems evolve beyond tool-user relationships into co-evolutionary epistemic partnerships. When AlphaFold revolutionized protein structure prediction, researchers described engaging with an epistemic partner that reshaped how they conceptualized fundamental relationships. This article introduces Cognitio Emergens (CE), a framework addressing critical limitations in existing models that focus on static roles or narrow metrics while failing to capture how scientific understanding emerges through recursive human-AI interaction over time. CE integrates three components addressing these limitations: Agency Configurations describing how authority distributes between humans and AI (Directed, Contributory, Partnership), with partnerships dynamically oscillating between configurations rather than following linear progression; Epistemic Dimensions capturing six specific capabilities emerging through collaboration across Discovery, Integration, and Projection axes, creating distinctive "capability signatures" that guide development; and Partnership Dynamics identifying forces shaping how these relationships evolve, particularly the risk of epistemic alienation where researchers lose interpretive control over knowledge they formally endorse. Drawing from autopoiesis theory, social systems theory, and organizational modularity, CE reveals how knowledge co-creation emerges through continuous negotiation of roles, values, and organizational structures. By reconceptualizing human-AI scientific collaboration as fundamentally co-evolutionary, CE offers a balanced perspective that neither uncritically celebrates nor unnecessarily fears AI's evolving role, instead providing conceptual tools for cultivating partnerships that maintain meaningful human participation while enabling transformative scientific breakthroughs.