AI-Driven Case Teaching in College Political Education

Abstract
With the rapid development of artificial intelligence, large language models (LL Ms) are increasingly applied in education. Ideological and political theory courses in universities, which are crucial for moral education, face challenges such as outdated cases, weak interaction, and uniform content. This study explores the intelligent generation of cases and interactive teaching empowered by LL Ms, proposing a "triad" paradigm: intelligent generation, multi-dimensional interaction, and value guidance. An ELM-based case-generation system and an interactive platform were constructed to realize real-time content updates, precise case matching, enhanced classroom interaction, and data-driven assessment. Results show that LL Ms significantly improve the attractiveness and pertinence of ideological education, providing technical support for the goal of "educating students in accordance with the times and trends". Finally, strategies for addressing ethical, security, and teacher-role issues are discussed.
Keywords: Large Language Model; Ideological; Political Course; Case Teaching; Intelligent Generation; Interactive Teaching; AI+Education

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