Yu Dongchao: Improving the Algorithmic Supply of Digitalized Ideological and Political Education
The "Recommendations of the CPC Central Committee on Formulating the Fifteenth Five-Year Plan for National Economic and Social Development" [1], adopted at the Fourth Plenary Session of the 20th CPC Central Committee, points out the need to further advance the construction of Digital China, to "accelerate the innovation of digital-intelligent technologies such as artificial intelligence, achieve breakthroughs in basic theories and core technologies, and strengthen the efficient supply of computing power, algorithms, and data." This provides the fundamental guidance for the innovative development of digital ideological and political education in the New Era. Currently, data, algorithms, and computing power—with their new technologies, new fields, new carriers, and new tracks—are comprehensively integrating into all areas and the entire process of ideological and political education. They are driving a functional transmutation of theoretical paradigms, disciplinary styles, discourse patterns, and pedagogical forms, becoming critical leverage points for promoting the development of digital ideological and political education.
Enhancing the effectiveness of data factor utilization
The utilization of data factors directly determines the fairness of resource allocation in digital ideological and political education. In the process of digital transformation, the effectiveness of data utilization can be improved by establishing standardized data systems, constructing resource aggregation mechanisms, and activating the transformation of data value, thereby promoting the inclusive sharing of high-quality ideological and political education resources.
First, constructing standardized data systems to solidify the foundation for aggregating digital ideological and political education resources. Data standardization is the prerequisite for breaking "information silos" [2] and achieving the efficient flow of ideological and political education resources. Traditional data in this field suffers from inconsistent formats, incomplete dimensions, and asynchronous updates, which severely restrict the efficiency of resource integration. By constructing a standardized data system and unifying data interface standards and metadata specifications, scattered high-quality courses, teaching cases, and research achievements can be effectively aggregated. This forms a data network for educational resources that integrates ideological and political courses across universities, primary, and secondary schools nationwide, providing data support for the balanced allocation of digital ideological and political education resources.
Second, innovating resource aggregation mechanisms to promote the flow of high-quality digital ideological and political education resources. The active utilization of data factors can build a flow mechanism for digital ideological and political education resources that precisely matches "demand and supply." Digital-intelligent technology breaks the temporal and spatial constraints of resource sharing. Based on data-driven aggregation mechanisms, digital ideological and political education establishes a "classified + gradient" resource supply system. This not only allows for precision pushing based on the individual needs of learners—realizing a shift from "universal supply" to "precision delivery"—but also ensures the large-scale supply of high-quality resources, achieving cross-regional sharing and effectively narrowing the gap in educational resources between regions.
Third, activating the transformation of data value to drive the iterative upgrading of digital ideological and political education resources. The storage and transformation of data factors can drive the dynamic optimization and iteration of resources. For a long time, resource gaps between regions, urban and rural areas, and different schools have been key factors restricting educational equity. A data-driven iteration model for digital ideological and political education resources can break physical limitations and rapidly deliver high-quality resources to all terminals. On one hand, by analyzing learning data from learners in underdeveloped areas, one can precisely locate knowledge gaps and competency shortfalls to push adapted teaching resources. On the other hand, feedback data from learners everywhere flows back into the resource database, becoming an important basis for optimization, thus forming a virtuous cycle of "inclusive sharing of high-quality resources—multi-dimensional feedback data backflow—iterative resource upgrading."
Deepening the management effects of algorithm application
As the "intelligent core" of digital-intelligent technology, the scientific application and regulated management of algorithms are key to achieving precision and personalization in digital ideological and political education. By constructing specialized algorithmic models, innovating educational preference patterns, and improving algorithm governance systems, advanced socialist culture can be embedded into the production modes of digital ideological and political narrative content. This builds a digital narrative matrix with a "correct background color" [3], authentic sources, and clean content, enhancing the intelligence level of content generation and deepening the management effects of algorithm application.
First, constructing specialized algorithmic models to enhance the precision of digital ideological and political narratives. Algorithmic models must align with educational laws and pedagogical goals. Specialized algorithms for ideological and political education must balance the logic of knowledge transmission with the cognitive differences of learners. Based on a full-element evaluation dimension, deep training and inference should be conducted on individual data (learning behavior, cognitive levels, emotional states, value orientations), teaching content data (theoretical knowledge, general knowledge, teaching cases), and teaching process data (teacher-student interaction frequency, classroom atmosphere). This allows for assessments of learning states, ideological cognition, and teaching effectiveness. Using distributed databases and visualization tools for storage and display facilitates tracking and analysis of learners before, during, and after class, providing personalized educational support and ensuring narrative content is highly adapted to the learner’s cognitive level.
Second, innovating educational preference patterns to enhance the interactivity of digital ideological and political narratives. Algorithmic technology transforms classroom teaching from a traditional teacher-led model to an interactive model where teachers and students participate as equals, driving the narrative from one-way indoctrination toward interactive generation. Algorithms enable the dynamic presentation and real-time interaction of teaching content. Students can provide feedback on learning difficulties and preferences via terminal devices; the system can instantaneously analyze this and push relevant content, realizing personalized adaptation. It can automatically adjust the depth of explanation based on the student's progress and interests, transforming standardized content into a personalized dialogue, while also cultivating students' autonomous learning abilities and deepening the learning experience through interaction.
Third, improving algorithm governance systems to ensure the normativity of digital ideological and political narratives. The normativity of algorithm application directly relates to the value orientation and fairness of the narrative. Issues such as algorithmic bias and data privacy leaks necessitate the establishment of a sound algorithm governance system. This system covers the entire chain of development, application, and evaluation, providing full-process supervision of "AI + Education" evaluation algorithms. This avoids algorithms deviating from pedagogical goals or trending toward oversimplified evaluations, ensuring that algorithm applications meet the requirements of ideological and political education and guarding against risks brought by algorithmic abuse.
Promoting the interconnection efficiency of computing power networks
As the "infrastructure" for the implementation of digital-intelligent technology, the interconnection efficiency of computing power determines the depth and breadth of interaction in the digital ideological and political field. By constructing distributed computing hubs, optimizing scheduling mechanisms, and creating immersive interactive scenes, the value of computing power can be fully released to support the systemic construction of digital ideological and political education.
First, constructing distributed computing hubs to break supply barriers. Balanced supply of computing power is a prerequisite for wide-ranging interaction. The uneven regional distribution of computing resources has restricted overall progress. By constructing a national integrated computing power network, China has effectively compensated for regional gaps, providing fundamental security for cross-regional interaction in ideological and political education and breaking temporal and spatial barriers.
Second, optimizing computing power scheduling mechanisms to improve response efficiency. Intelligent scheduling can achieve "distribution according to need," ensuring efficient interaction. Since interaction scenes in ideological and political education exhibit significant "tidal" characteristics (peak usage periods), the priority of scheduling should be determined by urgency, complexity, and importance. An algorithm-optimized scheduling system can achieve dynamic allocation, improving resource utilization efficiency and ensuring the fluidity of interaction.
Third, creating immersive interactive scenes to build a digital ideological and political wisdom system. Sufficient computing power support can build multi-dimensional, deep-level interactive scenes, driving the educational ecosystem toward systemic integration. Breakthroughs in computing power make new interactive modes such as virtual simulation and holographic teaching possible, breaking the physical limits of traditional education and forming a "wisdom education" model that integrates online and offline elements. Digital ideological and political education uses computing power to achieve the deep fusion of teacher-student, student-student, human-machine, and human-technology interactions, transforming the relationship between teaching and learning into a multi-dimensional, immersive experience and forming an open, collaborative field ecosystem.
As core technical elements, the efficient supply and collaborative application of data, algorithms, and computing power are reshaping the landscape of digital ideological and political education. The deep utilization of data factors has solved the problem of balancing high-quality resources; the regulated application of algorithms has achieved the precision upgrading of educational narratives; and the interconnected development of computing power facilities supports all-around interaction. As digital-intelligent technology and ideological and political education undergo deeper fusion, these elements will further release their empowering effects, pushing digital ideological and political education toward higher quality, greater efficiency, more fairness, and more sustainable development, thereby injecting strong momentum into the construction of a leading country in education.