Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI, machine learning, and many generative systems, the dominant unit of analysis remains the individual model or autonomous system evaluated through outputs, benchmarks, prediction accuracy, or optimization performance. While these approaches have produced major advances, they often under-theorize the role of interaction in the emergence of intelligence, creativity, meaning, and adaptive behavior. This paper proposes interaction as the primary unit of analysis for co-creative AI and interaction-centered intelligence more broadly. Drawing from distributed cognition, embodied cognition, enaction, participatory sense-making, human-computer interaction, and computational creativity, the paper traces a historical progression toward increasingly relational accounts of intelligence. Building upon prior work in Creative Sense-Making, quantified co-creation, and co-creative systems such as the Drawing Apprentice and AI Drawing Partner, it argues that intelligence emerges through evolving interaction dynamics among agents, environments, and socio-technical systems rather than solely through internal computation. The paper introduces Interaction-Centered Intelligence as a framework for understanding human-AI co-creation, collaborative emergence, adaptive participation, and interactional dynamics. Rather than evaluating intelligence solely through generated outputs, the framework emphasizes interaction trajectories, coordination patterns, participatory engagement, adaptive regulation, and interactional drift unfolding through time. Implications for explainable co-creative AI, hybrid intelligence, enactive AI, and future human-AI systems are discussed.

翻译：传统人工智能主要将智能概念化为封闭智能体内部的孤立计算。在经典人工智能、机器学习及众多生成式系统中，主导分析单元仍是通过输出、基准测试、预测精度或优化性能进行评估的个体模型或自主系统。虽然这些方法取得了重大进展，但它们往往对交互在智能、创造力、意义及适应性行为涌现中的作用缺乏理论化探讨。本文提出将交互作为共创型人工智能及更广泛的以交互为中心的智能的首要分析单元。借鉴分布式认知、具身认知、生成认知、参与式意义建构、人机交互及计算创造力等领域的研究，本文追溯了走向日益关系性智能观的历史演进。基于先前在创造性意义建构、量化共创及共创系统（如"绘图学徒"和"AI绘画搭档"）方面的工作，本文论证了智能通过智能体、环境及社会技术系统间不断演化的交互动态涌现，而非仅通过内部计算产生。本文提出"以交互为中心的智能"框架，用于理解人机共创、协作涌现、适应性参与及交互动态。该框架不单纯通过生成输出评估智能，而是强调随时间展开的交互轨迹、协调模式、参与式投入、适应性调节及交互漂移。讨论了其对可解释共创型人工智能、混合智能、生成式人工智能及未来人机系统的启示。