This study presents an AI Agent framework for intelligently matching and opti-mizing ideological and political elements in computer science (CS) curricula based on Outcome-Based Education (OBE) principles. The proposed system em-ploys a hybrid recommendation approach, combining knowledge graph-based semantic reasoning (with 86+ validated ideological elements) and collaborative filtering (leveraging 320+ instructor profiles), achieving 40% higher matching accuracy (F1-score=0.79) than conventional methods. Key innovations include: (1) a three-dimensional taxonomy of CS ideological elements (ethical/legal, technological narratives, professional competencies), (2) an explainable rec-ommendation engine providing justification paths (e.g., linking "sorting algo-rithms" to "engineering ethics"), and (3) continuous improvement mechanisms via real-time regulatory updates and longitudinal graduate tracking. Experi-mental results across five core CS courses demonstrate significant improve-ments in instructor satisfaction (4.1/5 vs 3.2/5 baseline) and student ideologi-cal awareness (+42% post-test scores). The framework addresses critical gaps in curriculum design by balancing technical rigor
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