基于适应结构化理论的AI赋能水利科普绘本生成机制与调适路径

Generation Mechanism and Adaptation Path of AI-Enabled Water Conservancy Science Popularization Picture Books Based on Adaptive Structuration Theory

摘要: 在人工智能快速发展的当下，传统水利科普方式在效率、成本与吸引力方面逐渐显露出不足，亟需探索生成式人工智能技术赋能路径。现有水利科普研究多聚焦于传播效果与受众分析，而对AI技术介入科普创作后引发的结构化互动、调适机制的探讨未见。本研究基于适应结构化理论(AST)，采用案例研究法，以洪泽古灌区世界灌溉工程遗产水利科普绘本制作为研究对象，分析了AI赋能水利科普绘本制作的结构化互动机制。研究发现，AI赋能科普绘本制作的关键在于先进信息技术结构、外部结构源与内部结构源三者的动态适配与调适。实践表明，合理的技术调适可使制作成本与创作时间显著下降。本研究从适应性结构化视角进行分析，不仅揭示了AI赋能科普创作的内在机制，也可为AI赋能科普工作提供了理论解释框架与实践路径参考。

Abstract: In the current era of rapid development of artificial intelligence, traditional water conservancy science popularization methods are gradually showing shortcomings in efficiency, cost, and attractiveness, and there is an urgent need to explore the empowerment path of generative artificial intelligence technology. However, existing water conservancy science popularization research mostly focuses on communication effects and audience analysis, lacking exploration of the structured interaction and adjustment mechanisms triggered by AI technology intervention in science popularization creation. This study is based on the Adaptive Structure Theory (AST) and Schwieger’s modified model, using a single case study method. The water science popularization picture books of the Hongze Lake World Irrigation Project Heritage were used as research objects to systematically investigate the structured interactive mechanism of AI-enabled water science popularization picture book production. Research has found that the key to AI empowering the production of science popularization picture books lies in the dynamic adaptation and adjustment of advanced information technology structures, external structural sources, and internal structural sources. Practice has shown that reasonable technological adjustments can significantly reduce production costs and creation time. This study analyzes from the perspective of adaptive structuring, not only revealing the inherent mechanism of AI empowering science popularization creation, but also providing a new theoretical explanation framework and practical path reference for AI empowering science popularization work.