From Macro Warnings to Micro Risks: Identifying Generative AI Risks in University Ideological and Political Education
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The rapid integration of Generative Artificial Intelligence (GAI) into university ideological and political education has created new pedagogical opportunities while simultaneously introducing significant risks. While existing research has produced valuable macro level warnings concerning ideological security, discursive ethics, and teacher authority erosion, these warnings remain largely at the level of principle based alerts. They lack the granularity needed for frontline teachers to recognize and respond to specific risks in actual teaching scenarios. This study addresses this gap by shifting the analytical focus from macro warnings to micro risks. Employing a mixed methods approach that includes a survey of 500 ideological course instructors, semi structured interviews with teachers and students, and classroom observations across multiple universities in Jiangxi Province, the study systematically identifies risk manifestations across four teaching scenarios: lesson preparation, classroom instruction, interactive sessions, and assessment activities. The findings reveal concrete, observable, and intervenable micro risks, such as GAI generated case studies that subtly weaken the Party's leadership narrative, students uncritically copying AI generated answers, and automated grading systems that fail to detect ideologically problematic statements. A preliminary micro risk taxonomy is developed to make these risks visible, nameable, and actionable. The study further identifies three causal logics underlying these risks: technological bias embedded in training data, institutional absence of review mechanisms, and cognitive blind spots among teachers who mistakenly trust GAI neutrality. Theoretically, this study shifts the field from macro warnings to micro identification. Practically, the micro risk taxonomy provides a diagnostic tool that frontline teachers and administrators can use to recognize and respond to GAI risks in their daily work.
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