The Teacher as Gatekeeper: Identifying and Governing Generative AI Risks in Chinese University Ideological and Political Courses

Abstract

The rapid adoption of Generative Artificial Intelligence (GAI) in Chinese university ideological and political courses has placed frontline teachers in a challenging position. While existing research has focused on macro-level risk warnings, little is known about how teachers can actively govern these risks in their daily practice. This study addresses this gap by conceptualizing the teacher as a "gatekeeper" who filters, critiques, and guides GAI use in value-laden educational contexts. Drawing on semi-structured interviews with 20 ideological course instructors and classroom observations across five universities in Jiangxi Province, the study identifies four gatekeeping strategies that teachers spontaneously develop: AI output review, critical questioning in class, boundary setting, and peer consultation. The findings also reveal three governance dilemmas faced by teachers: the lack of standards, time constraints, and institutional policy gaps. Based on these findings, the paper proposes a three-level governance framework operating at the teacher, course, and institution levels. Unlike existing studies that emphasize technological risks or teacher replacement, this framework empowers teachers to become effective gatekeepers without being overwhelmed by technological demands. The study contributes a teacher-centered perspective to GAI governance in politically sensitive educational settings and offers practical tools, including a risk review checklist and teaching scripts.