Improving the Algorithmic Supply for the Digitalization of Ideological and Political Education

The Proposal of the Central Committee of the Communist Party of China on Formulating the 15th Five-Year Plan for National Economic and Social Development, adopted at the Fourth Plenary Session of the 20th Central Committee of the CPC, points out that deepening the construction of Digital China requires “accelerating innovation in digital-intelligent technologies such as artificial intelligence, breaking through basic theories and core technologies, and strengthening the efficient supply of computing power, algorithms, and data,” providing a fundamental guideline 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 fully integrated into all areas and the entire process of ideological and political education, driving functional transformations in its theoretical paradigms, disciplinary styles, discourse models, and educational forms, and becoming an important focal point for advancing the development of digital ideological and political education.

党的二十届四中全会通过的《中共中央关于制定国民经济和社会发展第十五个五年规划的建议》指出，深入推进数字中国建设，“加快人工智能等数智技术创新，突破基础理论和核心技术，强化算力、算法、数据等高效供给”，为新时代数字思政创新发展提供了基本遵循。当前，数据、算法、算力以其新技术、新场域、新载体、新赛道全面融入思想政治教育各领域和全过程，驱动思想政治教育理论范式、学科样式、话语模式、育人形式发生功能嬗变，成为推动数字思政发展的重要着力点。

Enhancing the Efficiency of Data Element Utilization

提升数据要素利用效度

The utilization of data elements directly determines the fairness of resource allocation in digital ideological and political education. In the process of digital transformation of ideological and political education, by establishing a standardized data system, constructing a resource aggregation mechanism, and activating the transformation of data value, it is possible to effectively enhance the efficiency of data utilization and promote the universal sharing of high-quality ideological and political education resources.

数据要素的利用直接决定了数字思政教育资源配置的公平性。在数字思政转型进程中，通过建立标准化数据体系、构建资源汇聚机制与激活数据价值转化，能够有效提升数据利用效度，推动优质思想政治教育资源的普惠共享。

First, constructing a standardized data system to consolidate the foundation for aggregating digital ideological and political education resources. Data standardization is a prerequisite for breaking down “information silos” and achieving efficient circulation of ideological and political education resources. Traditional ideological and political education data suffer from problems such as inconsistent formats, incomplete dimensions, and unsynchronized updates, which severely constrain the integration efficiency of educational resources. By constructing a standardized data system, through unifying data interface standards and metadata norms, scattered high-quality ideological and political courses, teaching cases, and research results can be effectively aggregated, forming a nationwide data network for the integration of ideological and political education resources across primary, secondary, and higher education, providing data support for the balanced allocation of digital ideological and political education resources.

一是构建标准化数据体系，夯实数字思政教育资源汇聚基础。数据标准化是打破“信息孤岛”，实现思想政治教育资源高效流转的前提。传统思想政治教育数据存在格式不统一、维度不完整、更新不同步等问题，严重制约了教育资源整合效能。构建标准化数据体系，通过统一数据接口标准与元数据规范，使分散的优质思政课程、教学案例、教研成果得以有效聚合，形成覆盖全国大中小学思政课一体化的教育资源数据网络，为数字思政教育资源均衡配置提供了数据支撑。

Second, innovating the resource aggregation mechanism to promote the flow of high-quality digital ideological and political education resources. The activated utilization of data elements can construct a “demand-supply” precisely matched flow mechanism for digital ideological and political education resources. Digital-intelligent technology breaks the temporal and spatial constraints on the sharing of ideological and political education resources. Based on a data-driven resource aggregation mechanism, digital ideological and political education establishes a “classification + gradient” resource supply system, which can both precisely push content according to learners’ individual needs, realizing a shift from “universal supply” to “precise delivery” of ideological and political education resources, and ensure the large-scale supply of high-quality ideological and political education resources, achieving cross-regional sharing of these resources and effectively compensating for gaps in educational resources between regions.

二是创新资源汇聚机制，促进优质数字思政教育资源流动。数据要素的活化利用能够构建“需求—供给”精准匹配的数字思政教育资源流动机制。数智技术打破了思想政治教育资源共享的时空限制，数字思政基于数据的资源汇聚机制，建立“分类+梯度”的资源供给体系，既能根据学习者的个性化需求进行精准推送，实现思想政治教育资源从“普适供给”向“精准配送”的转变，又能保证优质思想政治教育资源的规模化供给，实现优质思想政治教育资源的跨域共享，有效弥补区域间教育资源的差距。

Third, activating the transformation of data value to drive the iterative upgrading of digital ideological and political education resources. The reserve and transformation of data elements can drive the dynamic optimization and iteration of ideological and political education resources. For a long time, the gap in educational resources between regions, urban and rural areas, and schools has been a key factor constraining educational equity. A data-driven iterative model for digital ideological and political education resources can break the limitations of physical space and rapidly deliver high-quality ideological and political education resources to various terminals. On the one hand, by analyzing the learning data of students in underdeveloped areas, it can precisely identify their knowledge gaps and ability shortcomings, and push appropriate teaching resources accordingly; on the other hand, feedback data from learners in various places will continuously flow back to the resource database, becoming an important basis for optimizing data resources, thus forming a virtuous cycle of “universal sharing of high-quality resources—feedback data flow back—resource iteration and upgrading” in digital ideological and political education.

三是激活数据价值转化，推动数字思政教育资源迭代升级。数据要素的储备与转化，能够驱动思想政治教育资源的动态优化与迭代。长期以来，区域、城乡、校际之间的教育资源差距，是制约教育公平的关键因素之一，而数据驱动的数字思政教育资源迭代模式，能够打破物理空间的局限，将优质思想政治教育资源快速输送至各个终端。一方面，通过分析薄弱地区学习者的学习数据，精准定位其知识缺口与能力短板，有针对性地推送适配的教学资源；另一方面，各地学习者的使用反馈数据会持续回流至资源数据库，成为数据资源优化的重要依据，从而形成数字思政“优质资源普惠共享—多元反馈数据回流—资源迭代升级”的良性循环。

Deepening the Management Effect of Algorithm Application

深化算法应用管理效果

Algorithms, as the “intelligent core” of digital-intelligent technology, their scientific application and standardized management are key to achieving precision and personalization in digital ideological and political education. By constructing dedicated algorithm models, innovating educational preference models, and improving the algorithm governance system, socialist advanced culture is embedded into the production model of digital ideological and political education narrative content, building a digital ideological and political education narrative matrix with correct political orientation, authentic information sources, and clear content, thereby enhancing the intelligent level of ideological and political education content generation and deepening the management effect of algorithm application.

算法作为数智技术的“智慧核心”，其科学应用与规范管理是实现数字思政精准化、个性化的关键。通过构建专用算法模型、创新教育偏好模式与完善算法治理体系，为数字思政叙事内容的生产模式植入社会主义先进文化，建设底色正、信源真、内容清的数字思政叙事矩阵，提升思想政治教育内容生成的智能化水平，深化算法应用的管理效果。

First, constructing dedicated algorithm models to enhance the precision of digital ideological and political education narratives. Algorithm models must conform to educational laws and educational goals; dedicated algorithms for ideological and political education need to balance the logicality of knowledge transmission with learners’ cognitive differences. Based on a comprehensive evaluation dimension of all elements of ideological and political education, individual data such as students’ learning behavior, cognitive level, emotional state, and value orientation, teaching content data such as theoretical knowledge, general knowledge, and teaching cases, and teaching process data such as teacher-student interaction frequency and classroom atmosphere are subjected to deep training and reasoning. This enables assessments of learning status, ideological cognition, and teaching effectiveness, using distributed databases and visualization tools for storage and dynamic display of assessment results, facilitating pre-class, in-class, and post-class tracking and analysis for learners, providing personalized educational support, and making the narrative content of ideological and political education highly compatible with learners’ cognitive levels.

一是构建专用算法模型，提升数字思政叙事精准性。算法模型必须贴合教育规律与育人目标，思想政治教育专用算法需兼顾知识传递的逻辑性与学习者的认知差异性。基于思想政治教育全要素测评维度，将学生学习行为、认知水平、情感状况、价值倾向等个体数据，理论知识、通识知识、教学案例等教学内容数据，师生互动频次、课堂氛围等教学过程数据进行深度训练与推理，开展学习状态测评、思想认知测评和教学效果测评，并采用分布式数据库、可视化工具进行测评结果的存储与动态展示，便于为受教育者进行课前—课中—课后的跟踪与分析，为其提供个性化教育支持，使思想政治教育叙事内容与学习者的认知水平高度适配。

Second, innovating educational preference models to enhance the interactivity of digital ideological and political education narratives. Algorithm technology transforms classroom teaching from a traditional teacher-led model to an interactive model of equal participation between teachers and students, driving the ideological and political education narrative from one-way indoctrination to interactive generation. Algorithm technology can realize the dynamic presentation and real-time interaction of ideological and political education teaching content. Students can provide feedback on learning questions and preferences through terminal devices, and the system can instantly analyze and push relevant learning content, achieving personalized adaptation of ideological and political education narrative content. It can automatically adjust the depth of explanation according to students’ learning progress and interests, transforming the educational narrative from standardized content to personalized dialogue, and also cultivate students’ autonomous learning ability, deepening the learning experience of ideological and political education through interaction.

二是创新教育偏好模式，增强数字思政叙事互动性。算法技术使课堂教学从传统的教师主导模式转变为师生平等参与的互动模式，推动思想政治教育叙事从单向灌输转向互动生成。算法技术能够实现思想政治教育教学内容的动态呈现与实时交互，学生可通过终端设备反馈学习疑问和偏好，即时分析并推送相关学习内容，实现思想政治教育叙事内容的个性化适配，根据学生的学习进度和兴趣自动调整讲解深度，使教育叙事从标准化内容转变为个性化对话，也能够培养学生自主学习能力，在互动中深化思想政治教育的学习体验。

Third, improving the algorithm governance system to ensure the normativity of digital ideological and political education narratives. The normativity of algorithm application directly affects the value orientation and fairness of digital ideological and political education narratives. Issues such as algorithm bias and data privacy leakage urgently require the establishment and improvement of an algorithm governance system. Algorithm governance covers the entire chain of algorithm development, application, and evaluation, implementing full-process supervision of “AI + education” evaluation algorithms to avoid algorithms deviating from educational goals and the tendency toward singular evaluation, ensuring that algorithm application meets the educational requirements of ideological and political education, and preventing risks arising from algorithm misuse.

三是完善算法治理体系，保障数字思政叙事规范性。算法应用的规范性直接关系到数字思政叙事的价值导向与公平性。算法偏见、数据隐私泄露等问题，亟须建立健全算法治理体系，算法治理涵盖算法开发、应用、评价等全链条，对“AI+教育”评价算法进行全流程监管，避免算法偏离育人目标和算法评价的单一化倾向，确保算法应用符合思想政治教育育人要求，防范算法滥用带来的风险。

Advancing the Interconnection Efficiency of Computing Power Networks

推进算力网络互联效能

Computing power, as the “infrastructure” for the implementation of digital-intelligent technology, its interconnection efficiency determines the depth and breadth of interaction in the digital ideological and political education field. By constructing distributed computing power hubs, optimizing computing power scheduling mechanisms, and creating immersive interactive scenarios, the value of computing power can be fully released, supporting the systematic construction of digital ideological and political education.

算力作为数智技术落地的“基础设施”，其互联效能决定数字思政场域的交互深度与广度。通过构建分布式算力枢纽、优化算力调度机制与打造沉浸式交互场景，可充分释放算力价值，支撑数字思政的系统性建构。

First, constructing distributed computing power hubs to break down barriers to computing power supply in digital ideological and political education. The equalization of computing power supply is a prerequisite for achieving extensive interaction in digital ideological and political education. The uneven distribution of computing power resources between regions constrains the overall advancement of digital ideological and political education. By constructing a national integrated computing power network, China has effectively compensated for regional computing power gaps, providing a basic guarantee for cross-regional interaction in the ideological and political education field, realizing real-time interaction of educational resources in different regions, and breaking down the temporal and spatial barriers to computing power supply.

一是构建分布式算力枢纽，打破数字思政算力供给壁垒。算力供给的均衡化是实现数字思政广泛交互的前提，区域间算力资源分布不均，制约了数字思政的整体推进。我国通过构建全国一体化算力网络，有效弥补了区域算力差距，为思想政治教育场域的跨域交互提供了基础保障，实现了不同地域教育资源的实时交互，打破了算力供给的时空壁垒。

Second, optimizing the computing power scheduling mechanism to enhance the response efficiency of digital ideological and political education interaction. An intelligent computing power scheduling mechanism can achieve “on-demand allocation” of computing power supply, ensuring efficient interaction in the ideological and political education field. Interactive scenarios in ideological and political education exhibit significant tidal characteristics. By determining the priority of computing power scheduling based on urgency, complexity, and importance, it ensures that high-priority tasks receive sufficient computing resources. An algorithm-optimized computing power scheduling system can realize the dynamic allocation of digital ideological and political education computing power resources, improve resource utilization efficiency, and ensure the smoothness of digital ideological and political education interaction.

二是优化算力调度机制，提升数字思政交互响应效率。智能化的算力调度机制能够实现“按需分配”的算力供给，保障思想政治教育场域的高效交互。思想政治教育交互场景具有显著的潮汐特征，按紧急程度、复杂程度和重要性确定算力调度的优先级，确保高优先级任务得到足够的计算资源。通过算法优化的算力调度系统，能够实现数字思政算力资源的动态调配，提升资源利用效率，确保数字思政交互的流畅性。

Third, create immersive interactive scenarios and build a digital ideological and political education smart system. Sufficient computing power support can construct multi-dimensional, deep-level digital ideological and political education interactive scenarios, promoting the transformation of the ideological and political education ecosystem toward systemic integration. Breakthroughs in computing power technology make new interactive methods such as virtual simulation and holographic teaching possible, breaking the physical limitations of traditional ideological and political education and forming a smart education model that integrates online and offline. Digital ideological and political education, through computing power support, achieves deep integration of teacher-student interaction, student-student interaction, human-machine interaction, and human-technology interaction, driving the relationship between teaching and learning toward a three-dimensional, immersive transformation, and forming an open and collaborative digital ideological and political education field ecosystem.

三是打造沉浸式交互场景，构建数字思政智慧系统。充足的算力支撑能够构建多维度、深层次的数字思政交互场景，推动思想政治教育生态向系统集成转变。算力技术的突破使虚拟仿真、全息教学等新型交互方式成为可能，打破了传统思想政治教育的物理限制，形成了线上线下相融合的智慧教育模式。数字思政通过算力支撑实现了师生交互、生生交互、人机交互、人技交互的深度融合，推动教与学的关系向立体化、沉浸式转变，形成开放协同的数字思政场域生态系统。

Data, algorithms, and computing power, as the core technical elements of digital-intelligent technology, are reshaping the development landscape of digital ideological and political education through their efficient supply and synergistic application. The deep utilization of data elements solves the problem of balanced allocation of high-quality digital ideological and political education resources; the standardized application of algorithm technology achieves the precise upgrading of digital ideological and political education narratives; and the interconnected development of computing power infrastructure supports the all-round interaction of the digital ideological and political education field. As digital-intelligent technology deeply integrates with ideological and political education, data, algorithms, and computing power will further release their enabling effects, promoting digital ideological and political education to achieve higher quality, more efficient, fairer, and more sustainable development, injecting strong momentum into building China into a leading country in education.

数据、算法、算力作为数智技术的核心技术要素，其高效供给与协同应用正在重塑数字思政发展格局。数据要素的深度利用破解了优质数字思政教育资源均衡配置难题，算法技术的规范应用实现了数字思政教育叙事的精准化升级，算力设施的互联发展支撑了数字思政教育场域的全方位交互。随着数智技术与思想政治教育的深度融合，数据、算法、算力将进一步释放赋能效应，推动数字思政实现更高质量、更有效率、更加公平、更可持续的发展，为教育强国建设注入强劲动力。

(The author is a professor at the School of Marxism, Party School of the CPC Heilongjiang Provincial Committee (Heilongjiang Academy of Governance), and a visiting scholar at the School of Marxism, Peking University.)

（作者系中共黑龙江省委党校（黑龙江省行政学院）马克思主义学院教授、北京大学马克思主义学院访问学者）