Redesigning Ideological and Political Education Classrooms in the Generative AI Era: Innovation Pathways, Risk Boundaries, and Governance Principles
TL;DR
The main contribution is to argue that generative AI should not be positioned as an autonomous educational subject, but as a conditional medium that supports interpretation, deliberation, and responsible practice under curricular purpose, teacher judgment, transparent rules, critical AI literacy, and institutional safeguards.
Abstract
Generative artificial intelligence is reshaping the organization of knowledge, classroom interaction, assessment evidence, and institutional arrangements in ideological and political education (IPE). In this article, IPE refers to a form of higher education that integrates theoretical learning, civic responsibility, and social practice. This conceptual article examines how generative AI changes the conditions under which educational judgment is formed in IPE classrooms. Drawing on educational technology studies, human agency theory, responsible AI governance, and critical AI literacy, it adopts conceptual analysis and theoretical synthesis to develop a four-stage pedagogical redesign model for the generative AI era. The model contains problem generation, negotiated interpretation, evidence verification, and practice transfer. The article identifies four innovation pathways: issue-based knowledge organization, human-AI collaborative dialogue, situated learning environments, and process-based assessment. It also specifies four risk boundaries: knowledge compression, cognitive dependence, relational weakening, and excessive datafication. The main contribution is to argue that generative AI should not be positioned as an autonomous educational subject, but as a conditional medium that supports interpretation, deliberation, and responsible practice under curricular purpose, teacher judgment, transparent rules, critical AI literacy, and institutional safeguards.