【作 者】：
杨文正，李小雨，陈选超，杨俊锋
【关 键 词 】：
教育数字化； 生成式人工智能； 数字教育资源； 精准服务； 供需耦合
【栏 目】：
学习环境与资源
【中文摘要】：
数字教育资源精准服务是一种以用户为中心、需求为导向的服务模式。从供需耦合角度分析，当前基础教育数字资源服务还存在资源供需不适配，更新速度与需求变化不同步，服务差异化、个性化程度不足，需求识别与预测能力不佳，以及智能化服务水平有待提升等问题。生成式人工智能凭借其跨模态生成、自适应学习、深度理解与智能交互等强大功能，为数字教育资源服务创新提供了有力的技术支持。基于供需适配、使用与满足和用户体验理论，研究构建了嵌入生成式人工智能技术的数字教育资源精准服务模式，阐释了供需双侧联动的资源服务机制。在此基础上，提出增强资源服务与用户需求“耦合”的路径：基于数据驱动的用户需求识别与预测；通过精准推荐增强资源服务与用户需求的适配度；借助智能交互提升用户参与感和体验感；利用反馈信息实现数字资源的持续进化；以及引入智能评估改进资源服务的质量，以此促进数字教育资源服务向精准化、个性化和智能化方向发展，更好地满足基础教育数字化转型的实践诉求。
【英文摘要】：
Digital educational resource precision service is a user-centered and demand-oriented service model. From the perspective of supply-demand coupling, there are still some problems in current digital resource services in basic education, such as mismatch between resource supply and demand, unsynchronized updating speed and demand changes, insufficient degree of service differentiation and personalisation, poor ability to identify and predict needs, and the need for improvement in the level of intelligent services. Generative artificial intelligence, with its powerful functions such as cross-modal generation, adaptive learning, deep understanding, and intelligent interaction, provides strong technical support for the innovation of digital educational resource services. Based on supply and demand adaptation theory, use and satisfaction theory and user experience theory, this paper constructs a precision service model of digital educational resources embedded in generative artificial intelligence, and explains the resource service mechanism of bilateral linkage between supply and demand. Finally, this paper proposes specific paths to enhance the "coupling" between resource services and user demand, including data-driven user demand identification and prediction, enhancing the adaptability of resource services and user demand through accurate recommendation, improving the sense of user participation and experience through intelligent interaction, realizing the continuous evolution of digital resources by means of feedback information, and introducing intelligent evaluation to improve the quality of resource services. In this way, we can promote the development of digital educational resource services in the direction of precision, personalization and intelligence, so as to better meet the practical demands of the digital transformation of basic education.