Abstract:      Disruptive technologies, exemplified by artificial intelligence (AI), are rapidly transforming production relationships and serve as key drivers of the efficiency revolution, catalyzing the continuous emergence of new quality productive forces. However, current research predominantly focuses on the AI innovation process itself, often overlooking the crucial integration of technology with real-world contexts. This issue is particularly evident in the growing paradox of the industrialization of generative AI technologies, highlighting the need to transcend human-machine collaboration. Instead, there is a pressing need to explore new paradigms that accelerate the role of AI in empowering new quality productive forces. This study systematically reviewed the human-machine collaboration paradigm and drew on the context-driven innovation theory to explore how the human-machine-context collaboration, as a new paradigm, empowers new quality productive forces. We found that this paradigm could enhance organizational capabilities by enabling forward-looking decision-making and active environmental sensing, improving innovation efficiency and effectiveness through the deep integration of human intelligence, machine learning, and contextual needs. Additionally, this study documented the application of a "Context (C) - Technology (T) - Capability (C)" triad within the human-machine-context collaboration framework to advance the modernization of industrial systems (S). This triad fosters technological breakthroughs, innovative combinations of production factors, and deep transformation of industries, all of which contribute to the ongoing emergence of new quality productive forces through the CTCS mechanism. Finally, taking the Beijing Academy of Artificial Intelligence as an example, this study analyzed how it promotes efficient human-machine co-creation in complex dynamic contexts, continuously empowering the emergence of new quality productive forces throughout the process of achieving breakthroughs in AI original technologies and rapid industrialization. This study has expanded the theoretical understanding of human-machine collaboration and will offer valuable micro-level theoretical insights and practical guidance for China to capitalize on the opportunity presented by context-driven innovation, advancing the "AI+" initiative, thus fostering the development of new quality productive forces tailored to local contexts.

Key words: new quality productive force, artificial intelligence, context-driven innovation, human-machine-context collaboration, paradigm logic, practical approach