Yuan Ke and Huang Tenglong: Artificial Intelligence Empowers the Paradigm Shift in Ideological and Political Education and Teaching
The development of artificial intelligence (AI) technology is reshaping social operational logic and people's modes of production and life through an all-encompassing technological revolution; it also provides an opportunity for the transformation of the pedagogical paradigm of ideological and political education, currently in a critical period of modernization transition. General Secretary Xi Jinping has emphasized: "China attaches great importance to the profound impact of AI on education, actively promoting the deep integration of AI and education to facilitate educational transformation and innovation." The recommendations for the "15th Five-Year Plan" [1] propose the comprehensive implementation of the "AI+" action, to "seize the commanding heights of AI industrial application and provide all-around empowerment across all sectors." Promoting the deep integration of AI with ideological and political education and empowering the systemic transformation of the pedagogical paradigm is an important path for fulfilling the fundamental task of "fostering virtue through education" [2] in the New Era. The Outline of the Plan for Building a Leading Country in Education (2024–2035), issued by the CPC Central Committee and the State Council, proposes promoting the deep integration of ideological and political work with information technology to create featured brands of network-based ideological and political education. This clarifies the practical direction for combining AI with ideological and political education: no longer confining technical empowerment to scattered tool applications, but moving toward deep integration across the entire chain. Since the 18th CPC National Congress, China has continuously advanced the digital transformation of education, integrating AI into all elements and processes of education and teaching, thereby accelerating the implementation of technology in the field of ideological and political education. Through measures such as building intelligent teaching platforms, developing virtual simulation scenarios, and constructing ideological and political case libraries, problems in traditional teaching—such as spatial-temporal limitations, homogenized supply, and insufficient interaction—have been gradually resolved. "Intelligent agents" for ideological and political education have been implemented and promoted in many universities, covering application scenarios such as freshman orientation and Party/Youth League building [3], significantly enhancing the contemporary feel and effectiveness of ideological and political education, and injecting strong new momentum into the construction of a leading country in education.
During the "15th Five-Year Plan" period, with the comprehensive implementation of the "AI+" action, ideological and political education is encountering a developmental environment characterized by deeper technical integration, higher educational requirements, and more complex security challenges; the urgency of pedagogical paradigm transformation has become increasingly prominent. Any innovative educational system or measure will encounter new problems and challenges. Technical empowerment makes human-machine collaboration a norm in ideological and political teaching, while also placing higher demands on the rationality and normativity of value leadership. New phenomena, such as cross-platform data flow and autonomous decision-making by algorithmic models, further highlight risks including data leaks, algorithmic bias, and content compliance, presenting new security tests for ideological and political education. To this end, we must support basic theoretical research and key technology R&D in AI, and advance the construction of infrastructure such as training data resources and computing power; simultaneously, we must improve ethical norms, strengthen risk monitoring and assessment, and ensure security supervision to promote the healthy development of AI applications. We must strictly implement requirements for hierarchical and classified data management, exercising full-cycle control over data regarding students' ideological trends and learning trajectories, and standardizing the entire process of data collection, storage, and use. Pre-audit and dynamic calibration mechanisms should be established for algorithmic applications to avoid value deviations driven by algorithms, ensuring that technical applications always revolve around the core orientation of "fostering virtue through education."
Building an integrated system of "technology, content, and mechanism" is an inherent requirement for AI to empower the transformation of the ideological and political teaching paradigm; it is also the fundamental strategy for enhancing the quality and effectiveness of education. Currently, the integration of AI and ideological and political education is moving from preliminary exploration—characterized by scattered pilots and tool adaptation—toward a critical stage of large-scale application and standardized advancement. While some universities have formed replicable cases, overall problems remain, such as fragmented technical applications, misalignment between content and demand, and poor mechanism connection. These cannot be solved by simply stacking intelligent tools or supplementing ideological materials. Instead, we must adhere to a systems perspective [4], coordinating core elements such as technology, resources, and governance, and smoothing the logic of supply-demand docking and full-process operation. Only then can we break through the misunderstanding of "valuing form over effectiveness" in technical applications, allowing AI to truly integrate into ideological and political teaching practice and serve the well-rounded development of the person. On the new journey of the "15th Five-Year Plan," to promote the transformation of the pedagogical paradigm of ideological and political education, we must persist in the unification of dominance and subjectivity. This means firmly grasping the core direction of value leadership while docking with the actual needs of teachers and students across different educational stages and disciplines, optimizing technical supply and content design, and improving platform adaptability and operability to avoid the problem of "separation between substance and application" [5] at its source. We must persist in taking national strategy as the guide, policy texts as the handle, norms and standards as the guarantee, and educational effectiveness as the goal. We should improve the top-level design, resource supply, and security prevention systems for the application of intelligent technology in ideological and political education, clarifying technical application boundaries, content review standards, and mechanism operation processes so that all work follows established rules. We must adhere to the principle of "technology for good," focus on the difficulties of technical implementation, optimize the functional design of smart platforms, and break down data barriers between different teaching scenarios and administrative departments to achieve intelligence in student condition analysis, resource pushing, and evaluation feedback.
Relying on the systemic integration advantages of the "smart education" stage to build an all-domain ecosystem for ideological and political teaching is the core direction for AI-driven pedagogical paradigm transformation. During the "14th Five-Year Plan" period, the development of educational digitalization in China moved from single-point exploration and local application toward a stage of systemic integration and comprehensive deepening known as "smart education." Empowerment during the "15th Five-Year Plan" period will focus more on all-domain synergy. By integrating the National Smart Education Platform, local university intelligent systems, and social ideological and political resource libraries, we will construct a smart ideological and political ecosystem characterized by "data interconnection, resource sharing, and scenario linkage," effectively merging the "small classroom" of ideological and political education with the "grand classroom" of society. First, we must build a layered and classified resource supply system. Based on the needs of different educational stages and disciplines, we should organize high-quality courses from national platforms, local university materials, "red resources" [6], and corporate cases to form a dynamically updated resource pool, ensuring both the downward sharing of high-quality resources and the fulfillment of personalized teaching needs. Second, we must fortify the security bottom line of data interconnection by building a unified data middle-platform, clarifying interface standards, and integrating academic performance.
Innovating the new model of human-machine collaborative teaching is the key to empowerment. The Opinions on the Deep Implementation of the "AI+" Action, issued by the State Council in 2025, proposed integrating AI into all elements and processes of education and teaching, and innovating new human-machine collaborative models such as intelligent study partners and intelligent teachers. Whether it is the "Qing Xiaoda" intelligent study partner created by Tsinghua University, the "Ai Huadao" ideological and political agent developed by Huazhong University of Science and Technology, or the "UESTC Chip Partner" AI comprehensive agent launched by the University of Electronic Science and Technology of China, a paradigm shift has been realized—from "one-way lecturing by teachers" to "human-machine collaborative guidance and autonomous inquiry by students." "Human-machine collaboration" has become a new hallmark of quality and efficiency improvement in ideological and political teaching. However, it must also be noted that this new model faces challenges such as inaccurate alignment of core values, superficial integration of technology and teaching, and uneven regional resource allocation. We must use specific linguistic corpora for ideological and political education as a support, optimize model training logic, and screen high-quality materials that fit the needs of value leadership. We must establish pre-audit and dynamic calibration mechanisms for content to eliminate value deviations led by algorithms, ensuring that intelligent tools always serve as auxiliary carriers for transmitting the correct orientation. We must deepen the integration of technology and teaching, promoting the connection between intelligent tools and the key/difficult points and practical links of ideological and political courses, guiding teachers to transform from "tool users" to "designers of teaching integration." We should give full play to the "leading goose" effect [7] of AI's strong spillover drive, build cross-university sharing platforms for high-quality resources, and establish specialized ideological and political corpora to optimize model training. Furthermore, human-machine collaborative teaching ability should be incorporated into the teacher training system, with practical exercises and case studies conducted based on the characteristics of ideological and political teaching, thereby enhancing teachers' ability to use AI to optimize instructional design and judge students' ideological trends, achieving a resonance between technical empowerment and value leadership.