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<p>Ideological and political education is facing increasing challenges in the digital age, as the rapid spread of online public opinion and the growing influence of social media often overwhelm traditional teaching models. Current approaches struggle with low accuracy in risk identification and lack clear standards for evaluating educators’ core competencies. To address these issues, this study introduces a dual-core framework. First, the Multi-modal Decision Support System (MADF-SE) combines classroom behavior recognition, semantic analysis, and blockchain-based traceability to improve the accuracy and timeliness of risk detection. Second, the Dynamic Capability Evolution Model (DCEM) quantitatively tracks and enhances educators’ cognitive, operational, and innovative abilities over time. In addition, a hierarchical federated learning framework is applied to balance privacy protection with cross-campus collaboration. Experimental validation and case studies in more than 30 universities show that the proposed system reduces the public opinion processing cycle by half, increases educators’ participation in strategic decision-making from 20% to 60%, and enhances their overall core competencies by over 25% annually. These results demonstrate a practical and scalable path for the digital transformation of ideological and political education.</p>

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Research on the role reconstruction and core competency evolution of ideological and political educators under AI-assisted decision-making

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月14日

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  • Hui He

摘要

Ideological and political education is facing increasing challenges in the digital age, as the rapid spread of online public opinion and the growing influence of social media often overwhelm traditional teaching models. Current approaches struggle with low accuracy in risk identification and lack clear standards for evaluating educators’ core competencies. To address these issues, this study introduces a dual-core framework. First, the Multi-modal Decision Support System (MADF-SE) combines classroom behavior recognition, semantic analysis, and blockchain-based traceability to improve the accuracy and timeliness of risk detection. Second, the Dynamic Capability Evolution Model (DCEM) quantitatively tracks and enhances educators’ cognitive, operational, and innovative abilities over time. In addition, a hierarchical federated learning framework is applied to balance privacy protection with cross-campus collaboration. Experimental validation and case studies in more than 30 universities show that the proposed system reduces the public opinion processing cycle by half, increases educators’ participation in strategic decision-making from 20% to 60%, and enhances their overall core competencies by over 25% annually. These results demonstrate a practical and scalable path for the digital transformation of ideological and political education.