Risk Governance of Algorithmic Recommendation in Education for Fostering a Strong Sense of Community for the Chinese Nation: A Conceptual Analysis in Chinese Higher Education
Algorithmic recommendation increasingly shapes how educational platforms organize learning resources, distribute attention, and mediate students’ encounters with knowledge, values, and cultural narratives. This article offers a conceptual analysis of algorithmic recommendation in education for fostering a strong sense of community for the Chinese nation in Chinese higher education. It develops the concept of educational visibility mediation to explain how recommendation systems shape what educational content becomes visible, what is repeatedly encountered, and how content is interpreted. Unlike general accounts of algorithmic curation or information filtering, this concept foregrounds the pedagogical conditions through which algorithmic systems influence meaning-making and identity formation. The article links three theoretical tensions—personalization versus common identity formation, platform visibility versus educational meaning-making, and data-driven optimization versus value-oriented formation—to five risk dimensions: subjectivity, content diversity, pedagogical mediation, experiential embodiment, and evaluative distortion. It then proposes a governance framework connecting these risks with value-oriented regulation, diversity-sensitive design, teacher-led mediation, student algorithmic literacy, online–offline integration, and multidimensional evaluation. The article contributes to AI-in-education and algorithmic governance debates by showing that recommendation systems should be assessed not only by technical performance, but also by their capacity to sustain educational purpose, cultural breadth, reflective agency, and collective belonging.