The Emergence, Application, and Risk Prevention of the Datafication of Ideological and Political Education

With the rapid development of big data, artificial intelligence, and other technologies, an increasing number of interdisciplinary studies and applications based on new technologies and methods have become a reality. The rapid advancement of technology has brought new opportunities for modernizing educational governance and has also provided new concepts and methods for the research and practice of ideological and political education. General Secretary Xi Jinping has pointed out: “We must use new media and new technologies to make work more dynamic, to deeply integrate the traditional strengths of ideological and political work with information technology, and to enhance its appeal and relevance to the times.” [1]1 Xi Jinping, “Speech at the National Conference on Ideological and Political Work in Colleges and Universities” (December 7, 2016). The advent of the era of big data and artificial intelligence has driven the widespread application of emerging data technologies in ideological and political education, making the datafication of ideological and political education an inevitable trend. Systematically analyzing the innovative development of ideological and political education from a datafication perspective is not only a need of the discipline but also a demand of the practice of ideological and political education.

大数据、人工智能等技术快速发展，越来越多以新技术新方法为基础的跨学科研究和应用成为现实。技术的快速发展，为推动教育治理现代化带来了新契机，也为思想政治教育研究和实践提供了新理念、新方法。习近平总书记指出：“要运用新媒体新技术使工作活起来，推动思想政治工作传统优势同信息技术高度融合，增强时代感和吸引力。”大数据、人工智能时代的到来，推动了新兴数据技术广泛应用于思想政治教育，促使思想政治教育数据化成为必然趋势。以数据化的视角系统分析思想政治教育的创新发展既是学科发展之需要，也是思想政治教育实践之诉求。

I. The Emergence of the Datafication of Ideological and Political Education

一、思想政治教育数据化的产生

The datafication of ideological and political education is a research trend and practical orientation that combines ideological and political education with new technologies such as big data and artificial intelligence, using methods like information processing, data computation, internet experiments, and simulation to explain and predict ideological and political education, thereby promoting the informatization and scientification of its research and practice. Its direct manifestations are the datafication of the subjects, content, methods, and platform carriers of ideological and political education, with the core lying in applying new data computation methods to serve the goal of fostering virtue through education (立德树人) in ideological and political education. [2]2 Fostering virtue through education (立德树人) is a fundamental task of education in China, emphasizing moral cultivation as the root of talent development. The progress of science and technology such as big data and artificial intelligence, the widespread application of data in ideological and political education, and the extension of computational social science methods have made the datafication of ideological and political education a reality.

思想政治教育数据化是思想政治教育结合大数据、人工智能等新技术，以信息处理、数据计算、互联网实验、模拟仿真等方法对思想政治教育进行解释和预测，推动思想政治教育研究和实践信息化、科学化的一种研究趋势和实践取向，其直接表现为思想政治教育主体的数据化、内容的数据化、方法的数据化和平台载体的数据化，核心在于运用新型数据计算的相关方法服务于思想政治教育立德树人的目标。大数据、人工智能等科学技术的进步、思想政治教育数据广泛应用和计算社会科学方法延伸，推动思想政治教育数据化成为现实。

(A) The Big Data Era Provides the External Environment for the Datafication of Ideological and Political Education

（一）大数据时代为思想政治教育数据化提供了外部环境

General Secretary Xi Jinping has pointed out, “Big data is a new stage in the development of informatization.” The advent of the big data era, with the accelerated construction of a digital society, digital economy, and digital government, provides an objective social environment for the application of ultra-large-scale data computation in ideological and political education. On the one hand, in the big data era, everyone can become a node of data. The rapid development of informatization, networking, and intelligence, especially the continuous popularization of mobile internet and smartphones, has made every individual a subject generating data. In this context, each person’s thoughts and behaviors are, to a large extent, expressed through data. For example, the big data of public opinion generated when people browse information and express views on internet platforms can reflect the online behavior preferences, focal points, and value orientations of different netizen groups. On the other hand, the arrival of the big data era has made everyone a target of data influence and data governance. The technological development of the big data era and the extensive and deep application of data in social life provide a reliable practical foundation and external environment for conducting ideological and political education using data computation methods. Through big data analysis of social network public opinion, it is possible to relatively accurately discover the current ideological state, reasonable needs, and existing problems of netizens, and accordingly adopt targeted data-based means to guide netizens’ thinking, offering more possible means to achieve the goal of fostering virtue through education in ideological and political education.

习近平总书记指出，“大数据是信息化发展的新阶段”。大数据时代的来临，数字社会、数字经济、数字政府加快建设，为超大数据计算应用于思想政治教育提供了客观的社会环境。一方面，在大数据时代，人人都可成为数据的节点。信息化、网络化、智能化的快速发展，特别是移动互联网和智能手机的不断普及，更是让每个人成为产生数据的主体。在此情境下，每个人的思想和行为在很大程度上会通过数据的形式表现出来。例如，人们在互联网平台上浏览信息、发表观点的舆情信息大数据，就可以反映不同网民群体的网络使用行为偏好、关注点和价值取向。另一方面，大数据时代来临，促使人人都可成为数据影响和数据治理的对象。大数据时代的技术发展和数据在社会生活中的广泛深度应用，促使以数据计算相关方法开展思想政治教育具有可靠的现实基础和外部环境。通过对社交网络舆情信息的大数据分析，可以较为准确地发现网民的思想现状、合理需求和存在的问题，也可以据此采用针对性的数据手段引导网民思想，为实现思想政治教育立德树人的目标提供更多可能手段。

(B) The Widespread Application of Data in Ideological and Political Education Provides Internal Support for Its Datafication

（二）思想政治教育数据的广泛应用为思想政治教育数据化提供了内部支撑

Data application is the foundation and prerequisite of datafication. The rapid improvement of algorithms and computing power represented by big data and artificial intelligence provides significant impetus for the innovative development of ideological and political education, making the widespread application of data in ideological and political education a prominent feature of the discipline’s development. Under the tide of networking, emerging technologies such as big data and artificial intelligence are constantly appearing in the practice of ideological and political education. The subjects, content, methods, carriers, and other elements of ideological and political education are increasingly presented in datafied forms, and methods of educating and assisting people through data are gradually being promoted.

数据应用是数据化的基础和前提。以大数据、人工智能为代表的算法、算力快速提升，为思想政治教育的创新发展提供了重要推力，促使思想政治教育数据的广泛应用成为学科发展的显著特征。在网络化大潮下，大数据、人工智能等新兴技术不断出现在思想政治教育的实践中，思想政治教育的主体、内容、方法、载体等要素不断以数据化的形态呈现，数据育人、数据助人的方法逐渐推广。

On the one hand, ideological and political education is increasingly expressed in the form of data. It increasingly adopts technical support means such as data collection and computation. Data computation-based learning platforms are continuously being put into use, including both officially developed applications and learning programs built on them. For example, the “Youth Learning” (青年大学习) micro-courses launched by the Central Committee of the Communist Youth League for university students on the WeChat platform, and the online ideological and political education learning platforms established by various universities. [3]3 The “Youth Learning” (青年大学习) is a series of online micro-courses organized by the Central Committee of the Communist Youth League, aimed at guiding young people to study Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era. The application of this series of data technology platforms has accumulated practical experience for the datafication of ideological and political education.

一方面，思想政治教育不断用数据的形式表现出来。思想政治教育越来越多地采用数据收集、计算等技术支持手段。基于数据计算的学习平台不断投入使用，这些平台既有直接开发的官方应用，也有架构其上的学习程序。例如，共青团中央在微信平台上针对大学生开办的“青年大学习”微课、各高校开设的思想政治教育网络学习平台等。这一系列数据技术平台的应用，为思想政治教育数据化积累了实践经验。

On the other hand, disciplinary research on the application of data in ideological and political education is constantly emerging. With the rapid development of big data, combining big data with online ideological and political education and using data application to promote the disciplinary development of ideological and political education has gradually become a consensus in the academic community. The rise of big data and other technologies has driven more and more researchers to engage in interdisciplinary research combining technical means centered on data computation with ideological and political education. Emerging research and cutting-edge explorations in areas such as “big data + ideological and political education,” “artificial intelligence + ideological and political education,” and “blockchain + ideological and political education” are constantly appearing. The academic community’s enthusiasm for research on ideological and political education based on datafication has given rise to a wealth of research results and has generated specialized research fields such as big data ideological and political education, precision ideological and political education, and intelligent ideological and political education. Interdisciplinary research on ideological and political education has promoted the theoretical accumulation of ideological and political education based on data computation, providing solid theoretical support for the emergence of the datafication of ideological and political education.

另一方面，思想政治教育数据应用的学科研究不断涌现。随着大数据的快速发展，运用大数据与网络思想政治教育相结合，以数据应用推动思想政治教育的学科发展逐渐成为学界的共识。大数据等技术的兴起，推动了越来越多的研究者投身于以数据计算为核心的技术手段与思想政治教育结合的跨学科研究。“大数据+思想政治教育”“人工智能+思想政治教育”“区块链+思想政治教育”等方面的新兴研究和前沿探索不断涌现。学界基于数据化基础的思想政治教育研究热情，催生了丰富的研究成果，并产生了大数据思想政治教育、精准思想政治教育、智能思想政治教育等专门研究领域。思想政治教育的跨学科研究，推动了基于数据计算的思想政治教育的理论积累，为思想政治教育数据化的产生提供了坚实的理论支撑。

(C) The Development of Computational Social Science Provides Theoretical and Methodological Reference for the Datafication of Ideological and Political Education

（三）计算社会科学的发展为思想政治教育数据化提供了理论和方法借鉴

With the rapid development of science and technology, emerging technologies mainly based on computer science and internet information science have quickly entered various fields of social life, constantly changing the landscape of social development and social governance. In this context, “computational social science” was jointly proposed by social scientists such as Lazer and natural scientists, and quickly received high attention from various disciplines. [4]4 David Lazer et al., “Computational Social Science,” *Science* 323, no. 5915 (2009): 721-723. Computational social science, driven by data computation, has brought new pathways to social science research from the dimensions of cognitive understanding and practical application. The advent of the era of big data and artificial intelligence has led to the widespread application of computational social science methods in various fields of social science research, such as sociology and political science, and has shown broad prospects in studying people’s thoughts and behaviors, pushing social science research into a new era.

随着科学技术的快速发展，主要基于计算机科学和互联网信息科学的新兴技术迅速进入社会生活的各个领域，不断改变着社会发展和社会治理图景。在此背景下，“计算社会科学”被拉泽尔等社会科学家和自然科学家联合提出，并迅速受到各个学科的高度关注。由数据计算驱动的计算社会科学从认知理解和实践应用的维度为社会科学研究带来了新的路径。大数据、人工智能时代的到来，促使关于计算社会科学的方法广泛应用于社会学、政治学等社会科学研究的各个领域，并在研究人们的思想和行为等方面展现出广阔前景，推动了社会科学研究进入一个新时代。

The leapfrog development of computational social science in theory and method provides a reference path for the innovative development of the datafication of ideological and political education. First, computational social science originates from social research in the big data era, which closely aligns with the external environment of ideological and political education and the datafication of the discipline. The rapid development of information technology is changing social forms and people’s modes of production and life, acting on people’s thoughts and behaviors, and also providing feasibility and necessity for conducting research and practice of ideological and political education through datafication methods. The core of computational social science is the “computation” and “calculation” based on big data, using datafication methods to conduct data analysis and technological application on people and society. This shares commonality with the people-centered value orientation of ideological and political education.

计算社会科学在理论和方法上的跨越式发展，为思想政治教育数据化的创新发展提供了参考路径。首先，计算社会科学来源于大数据时代的社会研究，这与思想政治教育的外部环境和学科的数据化紧密契合。信息技术高速发展，改变着社会形态和人们的生产生活方式，并作用于人的思想和行为，也为通过数据化方法开展思想政治教育研究和实践提供了可行性和必要性。计算社会科学的核心就是基于大数据的“计”和“算”，以数据化的方法开展对人和社会的数据分析和技术应用，这与思想政治教育以人为本的价值取向具有共通性。

Second, computational social science can expand the content and methods of research and practice in ideological and political education. Computational social science breaks the boundaries of quantitative and qualitative approaches in traditional social science research, resolves difficulties in comprehensiveness and systematicity within social science research, and greatly expands the space for theoretical research and practical exploration. Computational social science is a tool-based science; automatic extraction of social information, social network analysis, and social simulation modeling are common pathways in computational social science research. Conducting computation, simulation, and experimentation on the basis of data acquisition to achieve the discovery and explanation of current conditions and the prediction of developmental trends is the main method of computational social science. On the basis of the overall methodological approach of computational social science, branches such as computational education have derived content related to the development and creation of tools based on disciplinary needs, using technologies such as big data and artificial intelligence. As a comprehensive discipline, ideological and political education possesses both educational instrumentality and scientificity. Drawing on the cutting-edge theories and methods of computational social science to promote the innovative development of ideological and political education is not only a powerful support for advancing the datafication of ideological and political education but also an inherent need for promoting disciplinary progress.

其次，计算社会科学能够拓展思想政治教育研究和实践的内容与方法。计算社会科学打破了传统社会科学研究定量定性的边界，破解了社会科学研究中的全面性、系统性等方面的难题，极大拓展了理论研究和实践探索的空间。计算社会科学是一门基于工具的科学，社会信息的自动提取、社会网络分析、社会仿真建模是计算社会科学研究的常见路径。在数据获取的基础上开展计算、模拟和实验，实现对现状的发现、解释和对发展趋势的预测是计算社会科学的主要方法。在总体计算社会科学方法进路的基础上，计算教育学等分支衍生出了基于大数据、人工智能等技术，开展基于学科需要的工具研制与开发等相关内容。思想政治教育作为一门综合性学科，兼具教育工具性和科学性。借鉴计算社会科学的前沿理论和方法，推动思想政治教育创新发展，既是助推思想政治教育数据化的有力支撑，也是推动学科进步的内在需要。

II. The Application of the Datafication of Ideological and Political Education

二、思想政治教育数据化的应用

Based on the new orientation of the New Era, carrying out value guidance through innovation is the proper connotation of using new technologies and new methods to promote the development of ideological and political education. Under the combined effect of the digital social background, the datafication application of the discipline, and computational social science, elements such as the subject, content, methods, and carriers of ideological and political education all have an inherent need for datafication. Combining the disciplinary laws and inherent needs of ideological and political education, big data acquisition and analysis based on data computation, internet experiments, computer simulation modeling, and the development of research and guidance tools constitute the application directions of the datafication of ideological and political education. Among these, the acquisition and analysis of big data can provide support for the datafication of the subject and content of ideological and political education; internet experiments and computer simulation modeling can provide pathways for the datafication of evaluation methods in ideological and political education; and the development of research and guidance tools can provide solutions for the datafication of the carriers of ideological and political education.

立足新时代新方位，在创新中开展价值引领是运用新技术新方法推动思想政治教育发展的应有内涵。在数字社会背景、学科数据化应用、计算社会科学的共同作用下，思想政治教育主体、内容、方法和载体等要素均具有数据化的内在需求。结合思想政治教育的学科规律和内在需求，基于数据计算的大数据获取与分析、互联网实验、计算机仿真模拟、研究与引导工具开发构成了思想政治教育数据化的应用方向。其中，大数据的获取与分析可以为思想政治教育主体和内容的数据化提供支撑，互联网实验、计算机仿真模拟可以为思想政治教育评估方法的数据化提供路径，研究和引导工具的开发可以为思想政治教育载体的数据化提供方案。

(1) Acquisition and Analysis of Big Data in Ideological and Political Education

（一）思想政治教育大数据的获取与分析

The acquisition and analysis of big data is the key to the datafication of the subject and content of ideological and political education. In the practice of ideological and political education, due to reasons such as information asymmetry and differences in understanding, one often encounters the dilemma that the educational subject cannot accurately obtain the ideological state of the receiving subject, nor can it assess the guiding effect produced by the content of ideological and political education based on the state of the receiving subject. The datafication of ideological and political education can both provide support for escaping such dilemmas and may also create more severe information barriers, even forming "data silos." Facing the trend of datafication, how to leverage its advantages while preventing it from creating new barriers lies in doing a good job of data acquisition and analysis. Therefore, the datafication of ideological and political education needs to pay more attention to the acquisition and analysis of big data itself, build a bridge of information between the subjects of ideological and political education, achieve precise evaluation of educational content, and promote the datafication of the subject and content of ideological and political education.

大数据的获取与分析是思想政治教育主体和内容数据化的关键所在。在思想政治教育实践中，由于信息不对称、理解差异等方面的原因，经常遇到教育主体无法准确获得接受主体的思想状况，也难以根据接受主体的状况评估思想政治教育内容产生的引导效果的困境。思想政治教育数据化既能够为摆脱这类困境提供支撑，也可能会造成更加严重的信息壁垒，甚至造成“数据孤岛”。面对数据化趋势，如何发挥其优势，防止其产生新的壁垒，关键在于做好数据的获取与分析。因此，思想政治教育数据化更加需要注重大数据本身的获取与分析，搭建思想政治教育主体之间信息的桥梁，实现精准评估教育内容，推动思想政治教育的主体和内容数据化。

First, the acquisition of big data information in ideological and political education. The arrival of the big data era and the data application of ideological and political education have produced many direct or indirect, different types of big data in ideological and political education. Big data related to ideological and political education on social networks can be subdivided into categorical data reflecting people's values, civic awareness, national consciousness, sense of fairness and justice, and so on. These data can already be screened and extracted through various data platforms such as social networks. On the one hand, existing relevant data platforms can be used for data acquisition. While achieving public opinion monitoring, existing big data analysis platforms can also, based on the inherent needs of ideological and political education, carry out the acquisition and analysis of information big data. For example, from the big data generated by college students' library entries and exits, borrowed books, campus card consumption, forum information, etc., relevant data reflecting students' ideological states can be extracted. On the other hand, big data information tools developed by research institutions and researchers can be used for data collection, or data collection systems can be developed according to research and practice needs. The rapid development of computer science and technology has made big data acquisition technically easy. For example, various existing qualitative big data analysis research software can achieve data scraping functions for social networking site systems and form the ideological and political education datasets needed by researchers. Emerging technologies such as web crawlers can also acquire network big data based on the themes needed for ideological and political education. At the same time, the development of technology continuously lowers the difficulty and cost for researchers to design and develop data systems themselves; developing systems and collecting data according to research and practice needs is no longer difficult. For example, universities can develop specialized data collection systems based on the characteristics of students' ideological and political education.

首先，思想政治教育的大数据信息获取。大数据时代的到来和思想政治教育的数据应用，产生了许多直接或者间接的、不同类型的思想政治教育大数据。社交网络上的思想政治教育相关的大数据，能够细分为反映人们的价值观、公民意识、国家意识、公平正义感等方面的类别数据。这些数据已经能够通过社交网络等各类数据平台，进行相关数据的筛查和提取。一方面，可以运用已有的相关数据平台进行数据获取。现有的大数据分析平台在实现舆情监测的同时，也可以有针对性地根据思想政治教育的内在需要，进行信息大数据的获取和分析。例如，从在校大学生出入图书馆、借阅书目、校园卡消费、论坛信息等生成的大数据中，提取反映学生思想状况的相关数据。另一方面，可以运用研究机构和研究者开发出的大数据信息工具进行数据收集，或者根据研究和实践需要开发数据收集系统。计算机科学技术的快速发展，促使大数据获取在技术上变得容易。例如，已有的多种大数据质性分析研究软件，就可以实现对社交网站系统数据抓取功能，并形成研究者需要的思想政治教育数据集。新兴的网络爬虫等技术，也能够根据思想政治教育需要的主题进行网络大数据的获取。同时，技术的发展不断降低研究者自行设计和开发数据系统的难度和成本，根据研究和实践需要开发系统、收集数据已不再困难。例如，高校可以根据学生思想政治教育的特点，进行专门的数据收集系统开发。

Second, the big data analysis of ideological and political education. Raw data obtained directly through the internet, rather than through designed acquisition, is often unstructured or semi-structured data. Only through further data mining and analysis can the structuring of raw data in ideological and political education be achieved, thereby transforming it into valuable data that can be used for research and application. The content of ideological and political education delivered through online platforms is usually presented in datafied forms such as text information, video and audio, and images. The usage of this content by educational objects and its effects will also be manifested in a datafied manner. Through the analysis of these data, carrying out data classification and mining from different groups and different levels can analyze the correlations and regularities of relevant factors in ideological and political education, carry out precise evaluation of the content and effects of ideological and political education, and thus achieve precise guidance and precise policy implementation in ideological and political education. For example, using data visualization technology to display real-time classroom teaching interactions in ideological and political courses, and using cloud computing technology for precise analysis of big data in ideological and political education, can achieve rapid analysis of the teaching effects of ideological and political courses and targeted identification of improvement strategies.

其次，思想政治教育的大数据分析。通过互联网直接获取，而非设计获取的原始数据往往是非结构化或者半结构化的数据。只有通过进一步的数据挖掘和分析，才能够实现思想政治教育原始数据的结构化，进而转化为可用于研究和应用的有价值数据。通过网络平台投放的思想政治教育内容，通常以文字信息、视频音频、图片等数据化的形式呈现，教育对象对这些内容的使用情况及其效果也会以数据化的方式表现出来。通过对这些数据的分析，从不同群体、不同层次开展数据分类挖掘，能够分析出思想政治教育相关因素的关联性和规律性，开展对思想政治教育内容和效果的精准评价，从而实现思想政治教育的精准引导、精准施策。例如，通过数据可视化技术实时展现思政课课堂教学互动情况，以云计算技术进行思想政治教育大数据精准分析，从而实现快速分析思政课课堂的教学效果，针对性地寻找改进策略。

(2) Internet Experiments in Ideological and Political Education

（二）思想政治教育的互联网实验

For a long time, the evaluation of the effects of the content and methods of ideological and political education has easily faced dilemmas such as small evaluation samples, interference from the environment in evaluations, and inaccurate evaluations. Conducting ideological and political education using experimental methods conforms to the trend of digital empowerment and can provide support for enhancing the explanatory power of ideological and political education. Online internet experiments can provide solutions for the datafication of evaluation methods in ideological and political education, thereby providing pathways for optimizing the content, methods, and other internal elements of ideological and political education. Large-scale online internet experiments solve problems such as traditional experiments being based on ideal environments, limited external validity, and insufficient tolerance for heterogeneity, providing a more realistic explanation for analyzing people's thoughts and behaviors. Designing experiments based on internet big data platforms can precisely analyze the influencing factors of people's ideological concepts, value preferences, and behaviors. For example, some researchers, based on experimental designs on social networking platforms, analyzed the impact of informational messages and social messages on political mobilization (participation in the U.S. presidential election), finding that social messages have a greater impact on people's political behavioral tendencies.

长期以来，针对思想政治教育内容、教育方式的效果评估，容易面临评估样本较小、评估受到环境干扰、评估不准确等困境。以实验的方法开展思想政治教育顺应数字赋能的变革趋势，能够为增强思想政治教育解释力提供支撑。在线互联网实验可以为思想政治教育评估方法的数据化提供解决方案，从而为优化思想政治教育的内容、方式等内在要素提供路径。互联网在线大规模实验解决了传统实验基于理想环境、外部效度受到局限和异质性包容度不够等问题，为分析人们的思想和行为提供了更加真实的解释。基于互联网大数据平台进行实验设计，可以精准分析人们的思想观念、价值偏好和行为的影响因素。例如，有研究者基于在社交网络平台的实验设计，分析信息类消息和社交类消息对政治动员（参加美国大选）的影响，结果发现社交类消息对人们的政治行为倾向具有更大的影响。

Since internet experiments can analyze the influencing factors and effects of human thought and behavior, ideological and political education, which takes humans as its core research object, should logically regard internet experiments as an important component of its datafication. Internet experiments have clear advantages in comparative research and practice in ideological and political education, particularly in providing large-sample measurements of the influencing factors and effects of such education. Internet experiments can precisely assess the implementation effects of different ideological and political education contents and methods on different target groups in different regions, thereby exploring factors that influence the effectiveness of ideological and political education and analyzing improvement strategies. There are two main research pathways for internet experiments in ideological and political education: first, online experiments based on existing internet platforms; second, building virtual internet laboratories through network platforms. The former takes unspecified internet user groups as experimental subjects, while the latter recruits eligible experimental subjects through the network. Both pathways serve the specific goals of research and practice based on the intrinsic needs of ideological and political education.

既然互联网实验可以对人的思想和行为进行影响因素和效果分析，作为以人为核心研究对象的思想政治教育，理应将互联网实验作为其数据化的重要内容。互联网实验在思想政治教育比较研究和实践中具有明显优势，特别是能够对思想政治教育影响因素和效果提供大样本的测量。互联网实验可以精准评估不同思想政治教育内容和方法对于不同思想政治教育对象群体在不同区域的实施效果，并由此探寻影响思想政治教育成效的因素，分析改进策略。思想政治教育的互联网实验主要有两种研究路径：一是根据已有互联网平台的在线实验，二是通过网络平台搭建虚拟互联网实验室。前者以不特定的互联网使用群体为实验对象，后者通过网络招募符合条件的实验对象。两种路径都是根据思想政治教育的内在需求，服务于研究和实践的具体目标。

For example, on existing social networking platforms or news dissemination platforms, online experiments can analyze the dissemination effects of the same ideological and political education content—delivered via online video, text, or image formats—among different groups. The specific experimental method is as follows: first, the ideological and political education content (for instance, the spirit of an important meeting) is simultaneously released in image, video, and text formats during the same time period. By analyzing the reading volume, click-through rate, dwell time, number and quality of comments, and age of the reading audience for these images, videos, and texts, one can discover the dissemination effects of the ideological and political education content across different carriers. Through further comparative experimental analysis, it is possible to obtain the dissemination effects of different ideological and political education contents on different carrier formats. For example, a comparative internet experiment on dissemination effects could be conducted between two types of content: the spirit of a meeting and the promotion of moral models. Alternatively, the subjects of ideological and political education can be divided into an experimental group and a control group. One group, as the experimental group, receives the ideological and political education content carrier, while the other group, as the control group, receives no such content push. Finally, by measuring the cognitive level of both groups regarding the educational content online, the specific effect of the ideological and political education can be tested. For instance, in the "Internet Micro-Class" platform implemented in higher education ideological and political education, the entire student body is divided into an experimental group and a control group. After the experimental group studies the "micro-class" content pushed through the learning platform, comparing the cognition and attitudes of the two groups toward the corresponding "micro-class" content allows for testing the educational effect of the "Internet Micro-Class."

例如，在已有社交网络平台或者新闻传播平台中，可以通过在线实验分析同样的思想政治教育内容以网络视频传播、文字传播和图片传播形式在不同群体中的传播效果。具体实验方法如下：先将思想政治教育内容（比如某次重要会议精神），在同一时段分别以图片、视频、文字的形式进行投放，分析这些图片、视频、文字的阅读量、点击量、停留时间、评论数和评论质量、阅读人群的年龄等信息，就可以发现思想政治教育内容在不同载体中的传播效果。通过更进一步比较实验分析，能够获取不同的思想政治教育内容在不同的载体形式上的传播效果。例如，将会议精神、道德模范宣传两种不同的思想政治教育内容，进行传播效果的互联网实验比较。也可以将思想政治教育对象分为实验组和控制组，一组群体作为实验组通过投入思想政治教育的内容载体，另一组群体作为控制组不作任何思想政治教育内容推送，最后在线测量两组群体对该教育内容的认知水平，即可以检测思想政治教育的具体效果。例如，在高校思想政治教育中实行的“互联网微课”平 台，将整体学生分为实验组和控制组，实验组学生通过学习平台推送“微课”之后，对比两组学生对相应“微课”内容的认知和态度，即可测试“互联网微课”的教育效果。

(3) Computer Simulation of Ideological and Political Education

（三）思想政治教育的计算机仿真模拟

Similar to internet experiments, computer simulation can methodologically achieve the simulation and prediction of research objects, thereby providing support for the datafication of assessment methods in ideological and political education. Computer simulation is based on complexity science, employing big data, simulation algorithms, and simulation programming to conduct simulation research on set research targets. Its core lies in using strategies that approximate reality and consider social complexity to simulate the actions of agents through computers. Computer simulation has produced many significant research results in social science fields such as competition and cooperation, policy impact, and the dissemination of ideas. Simulation technologies such as agent-based modeling and multi-agent models can incorporate elements like knowledge, reasoning, expression, and emotion into the simulation. With the help of algorithms, it is also possible to simulate the adaptive actions of agents during learning.

与互联网实验类似，计算机仿真模拟可以在方法上实现对研究对象的模拟和预测，从而为思想政治教育评估方法的数据化提供支撑。计算机仿真模拟是基于复杂性科学，以大数据、仿真算法、仿真编程等方式，开展对设定研究目标的仿真研究。其核心在于以贴近真实和考虑社会复杂性的策略，通过计算机实现对主体行动的模拟。计算机仿真模拟在社会科学研究竞争与合作、政策影响、思想传播等领域已经产生了许多成效显著的研究成果。基于行动者的建模、多行动者的模型等仿真模拟技术，能够将知识、推理、表达、情感等要素纳入仿真模拟。借助算法，还可以模拟主体在学习中的适应性行动。

The application of computer simulation in ideological and political education can be explored from two aspects. First, by leveraging existing simulation platforms and programs, design simulation strategies around research objectives. Computational social science, by introducing data into simulation platforms, has found that simulation has significant advantages in revealing dissemination effects; simulation can accurately assess the dissemination effects of different communication methods. The datafication of ideological and political education can adopt the same method, conducting simulations of the effects of different educational methods on internet platforms, thereby achieving the evaluation and prediction of the effects of ideological and political education strategies. For example, based on a simulation environment, one could evaluate and predict the different effects of "cramming" and "heuristic" ideological and political education.

计算机仿真模拟在思想政治教育中的应用可以从两个方面进行探索。一是借助已有的仿真模拟平台和程序，围绕研究目标设计模拟仿真策略。计算社会科学通过将数据引入仿真模拟平台，发现仿真模拟在揭示传播效果方面具有显著优势，仿真模拟能够准确地评估不同传播方式的传播效果。思想政治教育数据化可以采用同样的方法，在互联网平台上对不同教育方式的效果进行仿真模拟，从而实现对思想政治教育策略效果的评估预测。例如，同样基于仿真环境，就可以评估预测“填鸭式”和“启发式”思想政治教育的不同效果。

Second, conduct exploratory simulations based on research needs. With the rapid development and deep application of technologies such as big data, artificial intelligence, and machine algorithms, an increasing number of simulation platforms have emerged, also providing researchers with the possibility to construct their own simulation frameworks. Researchers can use existing simulation platforms to explore simulations of ideological and political education, or, based on research needs and starting from set goals, achieve simulations of set targets by designing simulation algorithms and simulation programming. For example, combining social networks with simulation modeling to analyze the mechanisms of knowledge dissemination and learning utility in online Q&A communities can provide references for using online Q&A communities to conduct ideological and political education.

二是根据研究需要探索性地开展仿真模拟。随着大数据、人工智能、机器算法等技术的快速发展和深度应用，越来越多的仿真模拟平台问世，同时也为研究人员自行进行仿真架构提供了可能。研究者既可以利用现有仿真模拟平台开展思想政治教育的仿真模拟探索，也可以根据研究需要，以设定的目标为出发点，通过设计仿真算法、仿真编程实现对设定目标的仿真模拟。例如，运用社交网络和仿真建模结合的方式，分析网络问答社区中知识传播和学习效用的机制，就能够为运用网络问答社区开展思想政治教育提供参考。

(4) Development of Research and Guidance Tools for Ideological and Political Education

（四）思想政治教育研究和引导工具开发

Research and guidance tools are important supports for the practice of ideological and political education, and the datafication of ideological and political education is inseparable from the datafication of carriers. In the areas of big data text information analysis, social network information data processing, and video, image, and text information processing, computational social science has made many beneficial attempts at developing research and guidance tools, using them to analyze issues such as people's social network relationships, value preferences, and psychological tendencies. The same applies to the datafication of ideological and political education: it is not only necessary to acquire data, analyze data, and study data, but also, based on research and practice needs, to develop and create research and educational guidance tools, thereby achieving the datafication of research and guidance tools for ideological and political education. First, the development of research tools for ideological and political education. The datafication of ideological and political education is cutting-edge and exploratory; existing research tools may not necessarily meet the target needs of researchers. Therefore, researchers can attempt to develop research tools based on research objectives and provide references for subsequent similar research. Second, the development of guidance tools for ideological and political education. Based on extensive big data acquisition and analysis, internet experiments, and computer simulations, it is theoretically possible to construct universal influencing factors of ideological and political education, application scenarios for different dissemination carriers, the needs of different educational subjects, and differentiated educational guidance effects. Therefore, based on the results of data research, the datafication of ideological and political education can, in practice, develop guidance tools for ideological and political education. Targeting different educatees such as university students, the general public, and Party members and cadres, differentiated ideological and political education guidance tools can be developed. These tools can either be mounted on existing social media platforms like WeChat, Weibo, and short video platforms, or can involve attempts at standalone program applications. For example, developing virtual simulation classrooms that are not limited by time or location and are built on smart terminals.

研究和引导工具是思想政治教育实践的重要支撑，思想政治教育数据化离不开载体的数据化。在大数据文本信息分析、社交网络信息数据处理、视频图片文字信息处理方面，计算社会科学已对研究和引导工具开发进行了许多有益尝试，并用于分析人们的社会网络关系、价值偏好、心理倾向等议题。思想政治教育数据化亦是如此，不仅要能够获取数据、分析数据、研究数据，还有必要根据研究和实践需要，对研究和教育引导工具进行研制和开发，从而实现思想政治教育研究和引导工具的数据化。一是思想政治教育研究工具的开发。思想政治教育数据化具有前沿性和探索性，现有的研究工具不一定能够满足研究者的目标需要。因此，研究者可以尝试根据研究目标进行研究工具开发，并为后来类似研究提供参照。二是思想政治教育引导工具的开发。在开展大量大数据获取与分析、互联网实验、计算机仿真模拟的基础上，理论上能够构建出思想政治教育的普遍影响因素、不同传播载体的应用场景、不同教育对象的需要和差异化的教育引导效果等内容。因此，思想政治教育数据化可以根据数据研究的结果，在实践上开发思想政治教育的引导工具。针对大学生、社会公众、党员干部等不同受教育者的情况，开发出差异化的思想政治教育引导工具。这些工具既可以搭载已有的微信、微博、短视频等社交媒体平台，也可以开展单独的程序应用尝试。例如，开发不受时间地点限制，搭建在智能终端上的虚拟仿真课堂等。

III. Risk Prevention in the Datafication of Ideological and Political Education

三、思想政治教育数据化的风险防范

The application of new technologies and methods is exploratory and risky. Facing the rapid development of emerging data technologies such as artificial intelligence, General Secretary Xi Jinping emphasizes, "promote the healthy development of China's new generation of artificial intelligence," and "strengthen the research and prevention of potential risks in the development of artificial intelligence." [6]6 Xi Jinping, "Strengthen the Leadership of the Party and Ensure the Healthy Development of China's New Generation of Artificial Intelligence," *Qiushi*, 2019, no. 14. The datafication of ideological and political education, driven by technologies such as big data and artificial intelligence, must both fully leverage its advantages in promoting disciplinary innovation and development and guard against its potential risks.

新技术新方法的应用具有探索性和风险性。面对人工智能等新兴数据技术的飞速发展，习近平总书记强调，“推动我国新一代人工智能健康发展”，“要加强人工智能发展的潜在风险研判和防范”。以大数据、人工智能等技术驱动的思想政治教育数据化，既要充分发挥其促进学科创新发展的优势，也要防范其潜在风险。

(1) Preventing the Risk of Deviating from the Laws of Ideological and Political Education

（一）防范偏离思想政治教育规律的风险

Ideological and political education has its own laws and characteristics. The datafication of ideological and political education has its boundaries of use; research and practice on the datafication of ideological and political education must not deviate from the inherent laws of ideological and political education. First, it is necessary to guard against the risk of excessive reliance on data technology in ideological and political education. The prerequisite for the datafication of ideological and political education is adherence to the laws of ideological and political education itself; its essence is an expansion and supplement to ideological and political education, not a direct abandonment of existing methods of ideological and political education. Ideological and political education is a long-term process of fostering virtue through education [7]7 The phrase “fostering virtue through education” (立德树人) is a central concept in Chinese educational policy, emphasizing the cultivation of moral character as the fundamental task of education., which cannot simply rely on a single datafication to achieve long-term evaluation, explanation, and guidance. Instead, it requires a combination of technological and non-technological, online and offline approaches, with dynamic, continuous analysis, evaluation, and targeted policy implementation. The objects of ideological and political education also have their own growth laws and characteristics. The datafication of ideological and political education must avoid pursuing data for data’s sake, one-sidedly emphasizing the application and promotion of data while neglecting the laws that ideological and political education itself should follow. For example, for objects of ideological and political education at different stages, such as university students, secondary school students, and primary school students, it is necessary to achieve precise identification, precise education, and precise guidance by category and level, based on their stage-specific characteristics and degree of datafication, combined with the laws of ideological and political education itself.

思想政治教育有其自身的规律和特点。思想政治教育数据化具有其使用的边界，对思想政治教育数据化的研究和实践不能偏离思想政治教育的内在规律。首先，要防范思想政治教育对数据技术过度依赖的风险。思想政治教育数据化的前提是对思想政治教育本身规律的遵循，其本质是对思想政治教育的拓展和补充，而不是将已有的思想政治教育方法直接抛弃。思想政治教育是一个长期的立德树人过程，并不能简单地依靠单一数据化实现思想政治教育的长期评估、解释和引导，而是需要结合技术的和非技术的、线上的和线下的，动态地、持续地分析评估和精准施策。思想政治教育对象也具有自身的成长规律和特征，思想政治教育数据化要避免为数据而数据，片面强调数据的应用和推广，而忽略思想政治教育本身应当遵循的规律。例如，针对大学生、中学生、小学生等不同阶段的思想政治教育对象，应当根据其阶段性特征和数据化程度，结合思想政治教育自身规律，分类别分层次地实现精准识别、精准教育、精准引导。

Second, it is necessary to avoid the risk of instrumental alienation in the datafication of ideological and political education. The rapid development of computational social science has led some theoretical research and practice to develop a “technological superstition,” even believing that technology is omnipotent. In this context, it is necessary to prevent technology from failing to serve the value goals of ideological and political education and falling into the instrumental alienation of “technology dissolving goals.” For example, in carrying out ideological and political education work, some universities develop a wide variety of online platform tools for ideological and political education in a fragmented manner, leading to an endless proliferation of forms such as online learning, online quizzes, and ideological theory competitions. Some online platform-based learning and quizzes often overlap and repeat, requiring objects to download specific applications and register, while also linking them to assessments. These datafied ideological and political education platforms may appear to meet the needs of the times and the objects, but in reality, they consume a significant amount of the objects’ time and may even provoke their resentment. The actual effectiveness of ideological and political education achieved is also debatable. In practice, it is not uncommon for some objects to merely go through the motions formally for the sake of assessment, without genuinely engaging in ideological learning. This not only fails to achieve the effects of ideological and political education but also erodes the seriousness of ideological and political education work, deviating from the original intention of technology serving the goals of ideological and political education. Therefore, it is necessary to guard against the “technological superstition” and instrumental alienation generated by the datafication of ideological and political education.

其次，要规避思想政治教育数据化的工具异化风险。计算社会科学的快速发展，致使部分理论研究和实践产生了“技术迷信”，甚至认为技术无所不能。在此情境下，需要防止技术并没有服务于思想政治教育的价值目标，陷入“技术消解目标”的工具异化。例如，有些高校在开展思想政治教育工作中，各自为政开发种类繁多的思想政治教育网络平台工具，导致网络在线学习、网络答题、思想理论比赛等形式层出不穷。部分基于网络平台的学习、答题常常交叉重复，并要求对象必须下载专门的应用程序并注册登记，同时与考核挂钩。这些数据化的思想政治教育平台看似结合了时代和对象的需要，实际上会占用教育对象大量时间，甚至引起教育对象的反感，具体取得了多少思想政治教育的实效也值得商榷。在实践中，部分教育对象仅仅是为了考核而在形式上应付，思想上并未真正学习的情况时有发生。这样不仅无法取得思想政治教育的效果，还侵蚀了思想政治教育工作的严肃性，偏离技术服务于思想政治教育目标的初衷。因此，要防范思想政治教育数据化产生“技术迷信”和工具异化。

(II) Guarding Against the Risk of Violating Privacy and Weakening Subjectivity

（二）防范侵犯隐私和削弱主体性的风险

Adhering to research and practice ethics and guarding against possible ethical risks is a basic prerequisite for the healthy development of the datafication of ideological and political education. First, it is necessary to guard against the risk of violating privacy rights in the datafication of ideological and political education. The datafication of ideological and political education usually takes the overall form of a dataset as its carrier, yet the source of this dataset is the individual information of single persons. Although the data as a whole does not directly infringe upon individual rights, if researchers obtain the information of single individuals without the informed consent of the data-generating subjects, or even through technological means, they may face the risk of violating privacy and data rights. For example, in university ideological and political education research, using data on students’ use of libraries, campus cards, and campus networks as the basic data for research would face the problem of violating the privacy rights of the research subjects, posing ethical risks. However, if informed consent is set up, clearly informing subjects that the data they generate will be used for ideological and political education research, this would break the natural state of the data and harm its validity. Therefore, the datafication of ideological and political education requires balancing data validity with data use ethics, guarding against the risk of violating privacy rights.

遵守研究和实践伦理，防范可能出现的伦理风险是思想政治教育数据化健康发展的基本前提。首先，要防范思想政治数据化侵犯隐私权利的风险。思想政治教育数据化通常是以数据集的整体形态作为载体，这种数据集的来源却是单个人的个体信息。虽然作为整体的数据不会对个体权利产生直接侵犯，但是如果研究者未经数据产生主体的知情同意，甚至通过技术手段获取单个人的信息，则可能面临侵犯隐私和数据权利的风险。例如，在高校思想政治教育研究中，以学生在图书馆、校园卡、校园网络等方面的使用数据作为研究的基础数据，则会面临侵犯研究对象隐私权利的问题，具有道德伦理风险。然而，如果设置知情同意，明确告知对象其产生的数据将用于思想政治教育研究，则又会打破数据的自然形态，损害数据的有效性。因此，思想政治教育数据化需要做好数据有效性与数据使用伦理的平衡，防范侵犯隐私权利的风险。

Second, it is necessary to guard against the risk of the datafication of ideological and political education weakening human subjectivity. Facing development trends such as big data and artificial intelligence, we should, on the premise of firmly establishing our own subjective position, rationally develop and apply emerging technologies, and use them as an important means to promote the free and comprehensive development of human beings. Datafication easily shifts the mode of human life existence from “social existence” and “meaningful existence” to “digital existence,” thereby challenging human subjectivity. The datafication of ideological and political education, as a means, easily strengthens the control of data and technology over people, thereby weakening the subjectivity of both educators and education objects. The objects of ideological and political education are people with subjective consciousness and rich emotions, but the datafication of ideological and political education has obvious instrumental characteristics, using data symbols to identify, calculate, and guide people with subjective consciousness, easily neglecting people’s true feelings and subjective consciousness. Therefore, it is necessary to guard against the risk of weakening the subjectivity of the objects of ideological and political education.

其次，要防范思想政治教育数据化削弱人的主体性的风险。面对大数据、人工智能等发展趋势，我们应该在坚定自身主体性地位的前提下，合理发展和应用新兴技术，并将其作为重要方式促进人的自由全面发展。数据化容易使人的生命存在方式从“社会化存在”和“意义性存在”走向“数字化存在”，从而挑战人的主体性。作为手段的思想政治教育数据化，容易强化数据和技术对人的控制，从而削弱教育者和教育对象的主体性。思想政治教育的对象是具有主体意识和丰富情感的人，但是思想政治教育数据化具有明显的工具性特征，以数据符号标识、计算、引导具有主体意识的人，容易忽视人的真情实感和主体意识。因此，防范削弱思想政治教育对象的主体性风险具有必要性。

(III) Guarding Against the Risk of Loss of Reliability and Validity in Datafication

（三）防范数据化的信度与效度缺失的风险

The datafication of ideological and political education requires the deep integration of data technology with ideological and political education itself, and is inseparable from the basic support of the data literacy of the subjects and objects of ideological and political education. The truthfulness and credibility of the data itself, and the reliability and effectiveness of research and guidance methods, are the basic prerequisites for the datafication of ideological and political education. The core of the datafication of ideological and political education is the acquisition and analysis of data. Guarding against the risk of loss of reliability and validity in the data itself is key to ensuring the representativeness and truthfulness of the data, and safeguarding its reliability and validity. First, it is necessary to guard against the risk of data collection and calculation deviating from the real situation. Raw data obtained from the internet may be biased data, representing only the opinions of a partial group. If researchers do not screen it, they may face the risk of loss of data reliability and validity. Computer simulation and internet experiments, if they cannot accurately measure the true intentions of the objects, may lead to a lack of generalizability in predictions and evaluations. At the same time, ideological and political education itself has complexity, which has a certain tension with some assumptions of internet experiments and computer simulations. Moving from theoretical construction to practical promotion requires fully prudent argumentation and practical testing. Second, it is necessary to guard against the risk that researchers’ interdisciplinary literacy cannot meet the requirements. Using the methods of datafication of ideological and political education to conduct research requires building on an accurate grasp of both ideological and political education and computer science methods. Therefore, conducting in-depth research and practice on the datafication of ideological and political education requires guarding against the risk of loss of reliability and validity caused by insufficient researcher capacity.

思想政治教育数据化要求数据技术与思想政治教育本身的深度融合，离不开思想政治教育主客体数据素养的基础支撑。数据本身的真实可信、研究和引导方法的可靠有效是思想政治教育数据化的基本前提。思想政治教育数据化的核心是数据的获取与分析。防范数据本身信度和效度缺失的风险，是确保数据的代表性和真实性、保障其信度和效度的关键。首先，要防范数据收集和计算偏离真实情况的风险。互联网上获取的原始数据，可能是有偏数据，只能代表部分群体的意见。如果研究者不加以甄别，则可能面临数据信度和效度损失的风险。计算机仿真模拟和互联网实验，如果不能准确测度对象的真实意图，则可能导致预测和评估的推广性缺失。与此同时，思想政治教育本身具有复杂性，与互联网实验、计算机仿真模拟的部分假定具有一定的张力。由理论构建走向现实推广，需要进行充分审慎的论证和实践检验。其次，要防范研究者的跨学科素养无法满足要求的风险。运用思想政治教育数据化的方法开展研究，需要建构在对思想政治教育和计算机科学方法准确掌握的基础之上。因此，开展思想政治教育数据化的深度研究和实践，需要防范研究者能力不足所导致的信度和效度缺失风险。

IV. Conclusion

四、结 语

General Secretary Xi Jinping has pointed out, “Promote the reform and innovation of ideological and political theory courses, and continuously enhance the ideological nature, theoretical nature, affinity, and targetedness of ideological and political courses.” [8]8 This quotation is from Xi Jinping’s speech at a national conference on ideological and political work in colleges and universities, held in Beijing on December 7-8, 2016. The datafication of ideological and political education provides new research paradigms and practical methods for the online content, online dissemination, and online management of ideological and political education. It is of great significance for addressing the contemporary challenges faced by ideological and political education in terms of targetedness, affinity, interactivity, and personalization, and for promoting the innovative development of the discipline. The datafication of ideological and political education can enhance two-way interaction in ideological and political education, improve its precision, expand its space, and facilitate new breakthroughs in its content and methods. At the same time, the datafication of ideological and political education requires guarding against risks such as deviating from the laws of ideological and political education, violating privacy rights and weakening subjectivity, and loss of reliability and validity in datafication. When conducting research and practical exploration on the datafication of ideological and political education, it is necessary to fully tap its potential value while also paying attention to its boundaries.

习近平总书记指出，“推动思想政治理论课改革创新，不断增强思政课的思想性、理论性和亲和力、针对性”。思想政治教育数据化为思想政治教育网络内容、网络传播、网络管理方面提供了新的研究范式和实践方法，对于应对当前思想政治教育面临的针对性、亲和力、互动性、个性化等方面的时代挑战，推动学科的创新发展具有重要意义。思想政治教育数据化能够增强思想政治教育的双向互动，提升思想政治教育的精准性，拓展思想政治教育空间，有利于推动思想政治教育在内容和方法上取得新的突破。同时，思想政治教育数据化需要防范偏离思想政治教育规律、侵犯隐私权利和削弱主体性、数据化信度和效度缺失等方面的风险。在开展思想政治教育数据化研究和实践探索时，既要充分挖掘其潜在价值，也要注意其边界。

On the one hand, we must adopt a positive attitude and strengthen the exploration and application of the datafication of ideological and political education. The datafication of ideological and political education is a product of the development of technological progress and disciplinary evolution to a certain stage, and it holds significant value in guiding the innovative development of ideological and political education. Intensifying the exploration and practice of new technologies and methods for the datafication of ideological and political education can provide new ideas for expanding the research and application of ideological and political education, and offer support for achieving a high degree of integration between ideological and political education and new technologies. At the same time, the new technologies and methods of datafication are themselves exploratory and innovative. Promoting the datafication of ideological and political education with a positive attitude can better develop its potential and value, maximizing the role of emerging technologies in supporting the development of ideological and political education. On the other hand, the datafication of ideological and political education needs to adhere to the principles of cautious optimism and steady advancement. The datafication of ideological and political education is not a panacea; research and application of datafication still carry potential risks. Because ideological and political education possesses general characteristics such as timeliness, complexity, practicality, comprehensiveness, long-term nature, and diversified scenarios, in the future use of datafication emerging technologies to carry out data acquisition, data analysis, and data application in ideological and political education, it is also necessary to strengthen the demonstration of consistency with the discipline's value orientation and target needs, strengthen the constraints of disciplinary ethical norms, and prepare for fully leveraging the positive functions of datafication technology while avoiding its potential risks.

一方面，要以积极的态度，加强对思想政治教育数据化的探索与应用。思想政治教育数据化是技术进步、学科发展到一定阶段的产物，对引领思想政治教育创新发展具有重要价值。加大对思想政治教育数据化新技术新方法的探索实践，可以为拓展思想政治教育的研究与应用提供新思路，为实现思想政治教育与新技术的高度融合给予支撑。同时，数据化的新技术新方法，本身具有探索性和创新性。以积极的态度推动思想政治教育数据化，能够更好地开发其潜力和价值，最大程度发挥新兴技术支撑思想政治教育发展的作用。另一方面，思想政治教育数据化需要秉持审慎乐观、稳步推进的原则。思想政治教育数据化并非万能，数据化的研究和应用仍然具有潜在风险。由于思想政治教育具有时效性、复杂性、实践性、综合性、长期性、场景多元化等一般特征，在未来运用数据化新兴技术开展思想政治教育的数据获取、数据分析、数据应用方面，还需要加强与学科价值定位、目标需求一致性的论证，加强学科伦理规范的约束，为充分发挥数据化技术的正向功能，规避数据化的潜在风险作好准备。

In summary, the datafication of ideological and political education is a new research paradigm and practical orientation emerging from the widespread application of data in ideological and political education in the era of big data and artificial intelligence, and from the interdisciplinary development of computational social science. Although it has certain limitations, it remains an important trend guiding the research and practical development of ideological and political education, warranting deeper exploration in subsequent research.

综上，思想政治教育数据化是在大数据和人工智能时代思想政治教育数据广泛应用，计算社会科学跨学科发展下产生的新的研究范式和实践取向。尽管具有一定程度的局限性，但仍然是引领思想政治教育研究和实践发展的重要趋势，值得后续研究进行更加深入的探索。

(About the authors: Xie Yu, Vice Dean, Professor, and Doctoral Supervisor of the School of Marxism, Southwest Jiaotong University; Xie Yi, Doctoral Candidate of the School of Marxism, Southwest Jiaotong University, and Lecturer at Yibin Institute of Administration)

（作者简介：谢瑜，西南交通大学马克思主义学院副院长、教授、博士生导师；谢熠，西南交通大学马克思主义学院博士研究生，宜宾行政学院讲师）

(Fund project: This article is a phased outcome of the National Social Science Fund general project "Research on Ethical Risks of Artificial Intelligence Emotions from the Perspective of Confucian Emotionalism" (Project No. 23BZX078), the Sichuan Provincial Social Science Planning project "Research on the Current Situation, Trends, and Risks of Artificial Intelligence Emotions" (Project No. SC22B038), and the Sichuan Provincial Key Philosophy and Social Science Laboratory "Artificial Intelligence and Social Consciousness Laboratory".)

（基金项目：本文系国家社会科学基金一般项目“儒家情感主义视阈下人工智能情感的伦理风险研究”（项目号：23BZX078）、四川省社会科学规划项目“人工智能情感的现状、趋势与风险研究”（项目号：SC22B038）、四川省级哲学社会科学重点实验室“人工智能与社会意识实验室”的阶段性成果）

Online editor: Tongxin

网络编辑：同心