Research on AI-Driven Personalized Science Communication Pathways

Abstract

Against the backdrop of scientific communication evolving toward refinement and intelligence, the traditional one-way model aimed at broad coverage can no longer meet the cognitive needs of diverse audiences. The development of artificial intelligence offers a dynamically adaptive approach to scientific communication based on individual interests, knowledge structures, and cognitive preferences, enabling precise knowledge delivery and deep assimilation. This paper elaborates on the connotation and cognitive characteristics of personalized scientific communication, analyzes the empowering role of artificial intelligence in semantic understanding, knowledge graph construction, and multimodal generation, and constructs a communication pathway centered on content generation, audience profiling, and integrated pathway linkage. It also explores optimization strategies for multimodal interaction and iterative updating. The study points out that artificial intelligence is driving a shift in scientific communication from linear diffusion to data-driven and cognitively coordinated approaches, offering new theoretical and methodological support for personalized and intelligent communication.