Modern Social Science Research

Under the background of the "Double First-Class" initiative and the "Healthy China 2030" strategy, the scientific fitness literacy of college students has become a core issue in physical education. Traditional sports science popularization models suffer from two major defects: homogenization and one-dimensional indoctrination, severely restricting their educational effectiveness. This study mainly uses big data and artificial intelligence as anchors to systematically explore the logical path and practical strategies for overcoming the predicament of college sports science popularization. The study points out that big data, through multi-dimensional analysis of student data sources, can realize a paradigm shift from "broad-spectrum" publicity to "targeted" supply, providing data-driven decision-making basis for personalized customization of science popularization content and enabling the creation of "precise student profiles." Artificial intelligence (AI) transforms abstract sports science knowledge into perceptible and feedback-based practical references by constructing intelligent interactive scenarios for sports science popularization. This forms a dynamic closed-loop system for sports science popularization evaluation that is "data-driven cognition and intelligently optimized for experience." The paper also analyzes the practical challenges this paradigm faces in areas such as data ethics and the scientific validity of content, aiming to provide systematic theoretical references and practical guidance for the new ecosystem of college sports science popularization education in the digital age.

Artificial intelligence; Big data; College sports; Scientific fitness literacy; Precision science popularization; Data sources