摘要
在高校“双一流”建设与“健康中国2030”战略背景下，大学生科学健身素养已成为体育教育的核心议题。传统体育科普模式存在同质化与单向度灌输两大缺陷，严重制约了育人效能。本研究主要以大数据与人工智能为锚点，系统探讨了走出高校体育科普困境的逻辑理路与实践策略。研究指出，大数据通过立体化分析多维学生数据源，实现从“广谱式”宣传到“靶向性”供给范式转变，为科普内容个性化定制提供数据决策依据，能够为学生“精准画像”。人工智能通过构建智能体育科普交互情境，将抽象的运动科学知识转化为可感知、可反馈的实践性参照，形成了体育科普评估“数据驱动认知、智能优化体验”的动态闭环系统，并剖析了该范式在数据伦理与内容科学性等方面面临的现实挑战，以期为数智化时代高校体育科普教育新生态提供系统的理论参照与实践指引。
关键词： 人工智能；大数据；高校体育；科学健身素养；精准科普；数据来源
Abstract
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.
Key words： Artificial intelligence; Big data; College sports; Scientific fitness literacy; Precision science popularization; Data sources
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