Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative. This paper asks under what conditions a machine can be called genuinely creative, and how human agency can be preserved within shared cognitive and creative environments. It develops a requirement framework derived from Designics, the science of meaning-bearing intentional change. The paper argues that genuine machine creativity should not be defined by output novelty, current performance, or transient architecture alone. Instead, creativity is understood as the structural transformation of incomplete situations through recursive intervention dynamics. On this view, it depends on ten requirements: environment representation, scoped perception, conflict identification, intervention capability, consequence observation, knowledge and environment update, rescoping, local-to-global unfolding, value-based scoping, and human-AI co-living. These are organized through the three laws of Designics: perception, conflict, and capability. The paper illustrates the computational tractability of these requirements through selected cyber-physical and cyber-biological studies, including recursive element extraction, autonomous mesh generation, and neurophysiological and workload analysis. It then treats open-ended systems, automated discovery frameworks, self-modifying agents, foundation models, and agentic workflows as pressure cases: they demonstrate powerful generative means but do not by themselves establish genuine machine creativity. Finally, the paper argues that proactive AI ethics is internal to genuine machine creativity rather than an after-the-fact filter. Value-based scoping and human-AI co-living must shape how creative machines perceive environments, identify conflicts, select interventions, observe consequences, update knowledge, and rescope future action.

翻译：近期的人工智能系统能够生成看似具有创造性的文本、软件架构、假设、设计及科学工作流。本文探讨机器在何种条件下可被称为真正具有创造性，以及如何在共享的认知与创造环境中维护人类能动性。研究基于设计学（Designics）——这一关于承载意义之意图性变化的科学——构建了一个需求框架。本文主张，真正的机器创造性不应仅由输出新颖性、当前性能或临时性架构来定义。相反，创造性应被理解为通过递归干预动力学对不完全情境进行结构性转化。基于这一观点，它取决于十项需求：环境表征、范围化感知、冲突识别、干预能力、后果观察、知识与环境更新、范围重设、局部到全局展开、基于价值的范围界定以及人机共生。这些需求通过设计学三大法则（感知法则、冲突法则与能力法则）加以组织。本文通过选定的信息物理与信息生物学研究（包括递归元素提取、自主网格生成及神经生理与工作负荷分析）阐明了这些需求的计算可处理性。随后，将开放系统、自动发现框架、自我修改代理、基础模型及智能工作流作为压力测试案例：它们展示了强大的生成手段，但本身并未确立真正的机器创造性。最后，本文主张前瞻性的人工智能伦理内嵌于真正的机器创造性之中，而非事后的过滤机制。基于价值的范围界定与人机共生必须塑造创造性机器如何进行环境感知、冲突识别、干预选择、后果观察、知识更新及未来行动的范围重设。