Yuan Zhonghai: Digital and Intelligent Technology Empowers the Connotative Development of Ideological and Political Courses in Universities
Digital intelligence technology is accelerating its evolution from "content generation" toward agentic forms capable of "planning, execution, and coordination." This transformation provides a brand-new technological foundation and innovative possibilities for ideological and political theory courses (hereafter "思政课" (sizhengke) [1]) to transcend the traditional "irrigation" [2] model and strengthen the efficacy of value leadership. Empowered by these new technologies, we urgently need to consider how to introduce and effectively utilize AI educational agents, ensuring that teaching and learning penetrate the superficial layer of technological accumulation to allow "digital intelligence empowerment" to drive deep-seated transformations in pedagogical concepts, pathways, and the learning ecosystem.
The Bottlenecks in the Connotative Development of University Ideological and Political Courses and the Empowerment Logic of AI Agents
As university ideological and political courses enter a new stage of quality enhancement, the focus of construction is shifting from "teaching more" to "guiding more precisely" and "implementing more effectively." This aims to achieve the organic unity of ideological depth, theoretical rigor, approachability, and targeted relevance, helping to forge a powerful "ideological and political leadership force." However, in the practice of transformation, several key sticking points remain to be resolved. First, real-world issues update rapidly; lesson preparation and research often fall into the predicament of "information overload—scattered materials—thin evidence chains." Second, classroom interaction frequently exhibits "sufficient heat but insufficient depth," characterized by a surplus of expressed opinions but a deficit of argumentation and discernment. Third, while practical resources are becoming increasingly abundant, "having visited" does not equate to "having learned." The mechanism for the "Great Social Classroom" [3] to flow back into the "Small Ideological and Political Classroom" for review and improvement is unstable, resulting in experiences failing to crystallize into rational cognition and practical literacy.
AI agents solve these problems simultaneously from both the "teacher's instruction" and "student's learning" ends, thereby supporting the connotative construction of ideological and political courses. For teachers, the focus is on the systematic improvement of teaching efficacy: solving the problem of "what to teach that is more reliable and up-to-date" in terms of content supply, and "how to teach more deeply and generatively" in terms of classroom operation. For students, the focus is on the complete construction of the learning closed-loop: solving the problem of "how to integrate more effectively and allow for better feedback" in the fusion of theory and practice, and "how to improve more precisely and sustainably" in quality enhancement. This drives social experience to transcend perceptual cognition and transform into systematic theoretical understanding and operational practical ability.
The Triple Leap in Teaching Ability
The "difficulty" of the connotative development of ideological and political courses is, in essence, not a lack of resources, but rather the fact that it is often constrained by these bottlenecks. An AI agent is not merely "adding a tool" to the classroom, but rather systematically embedding "data chains," "problem chains," and "evidence chains" into the entire pedagogical process. It builds a solid digital intelligence core across three dimensions: content production, cognitive interaction, and the transformation of knowledge into action.
First, using "data chains" to enhance "experience chains" achieves a precise upgrade of instructional preparation. AI agents do not simply find materials for teachers; instead, they revolve around major themes and real-world concerns to complete issue aggregation, material verification, evidence comparison, and conceptual sorting. They automatically generate logically rigorous "theory-evidence-case" teaching resource packages. This liberates teachers from low-level retrieval and repetitive organization, shifting lesson preparation from "piling up materials" to "building evidence" and from "piecing together viewpoints" to "constructing frameworks," thereby providing a sustainable mechanism for precise content production to "teach deeply, thoroughly, and vividly."
Second, using "problem chains" to drive "thought chains" achieves a dialogic reconstruction of the teaching process. The key to classroom efficacy lies not in the frequency of interaction, but in whether the interaction enhances understanding. Addressing the problem of superficial classroom interaction, AI agents serve as "deep dialogue incubators." They analyze discussion semantics in real-time, present the threads of clashing viewpoints from multiple angles, and instantly push key follow-up questions, expanded resources, and theoretical benchmarks, constructing a layer-by-layer guided chain of problems. Consequently, classroom discussion gradually advances from simple "expressions of position" to "logical testing" centered on facts and "dialectical thinking" based on values. This promotes the teacher's transformation from a "lecturer of knowledge" to a "guide of thinking."
Third, using "evidence chains" to close the "learning chain" achieves an evidence-based transformation of pedagogical evaluation. Addressing the disconnection between social practice and theoretical teaching, AI agents act as "cross-domain learning connectors." They perform concomitant multi-modal recording and structured analysis of students' practical processes, forming a "behavior-reflection" process evidence chain. The personalized reports on the development of ideological and political literacy generated therefrom transform pedagogical evaluation from a simple grading of activity results into "evidence-based guidance" that promotes cognitive and value internalization. Teaching evaluation is no longer the end point of management, but the starting point of development, building a spiral learning closed-loop of "practice—reflection—theory—action."
Systematic Construction of the "Two Classrooms" Learning Ecosystem
The ultimate goal of "upholding the fundamentals and breaking new ground to promote the connotative development of ideological and political courses" is to build a new learning ecosystem that takes the student as the subject and connects the school with the social classroom, solidifying a deep foundation for value leadership and ideological enlightenment. AI agents build a specialized framework around the three educational dimensions of "theory-history-culture" and social expansion to support integration, deepen connotation, and improve effectiveness.
On one hand, in terms of deep content integration, AI agents establish cross-dimensional knowledge graphs to achieve the internalization and sublimation of immersive learning experiences. Addressing the fragmentation and hollowness of the three dimensions of theory, history, and culture, AI agents achieve deep integration and contextualized activation of teaching resources. When learning theory, students can call up cases, policies, and theoretical databases linked in real-time by the agent, autonomously constructing cognition through comparative analysis. When exploring history, students engage in role-based simulations of key decision-making in high-fidelity scenarios, understanding historical logic through embodied experience. When understanding culture, they utilize a traceable "cultural gene pool" to independently explore the associations between symbolic systems and contemporary value significance, completing the internalization from perception to identification.
On the other hand, in the systematic fusion of the "two classrooms," AI agents construct a mechanism of "intelligent matching—concomitant guidance—evidence-based feedback." This transforms society into a "living laboratory" and students into researchers carrying personalized task packages. The system matches practical goals and inquiry clues based on individual learning characteristics; through intelligent push notifications, it transforms on-site scenes into learning interfaces for task interaction, guiding observation and reflection to generate structured "inquiry logs." Upon returning to the classroom, the individual cognitive maps generated by the system clearly present the students' ideological trajectories and issues requiring further depth. This drives them to invest in deeper reflection and action with real social experiences and questions, completing the identity shift from "participant in social practice" to "reflective learning constructor." Taking "Rural Revitalization" [4] as an example: at the classroom end, the AI agent pushes personalized research plans and virtual tasks based on student interest tags (such as governance or culture); at the social end, it links with community platforms to match interactive exploration and deep questioning in real-world scenarios; and at the post-class feedback end, it analyzes student practice data in real-time to generate a "literacy development heat map," guiding them back to the classroom with real problems for deep theoretical discernment.
In short, when AI agents become "accelerators of mechanisms" rather than "replacements for judgment," and when the social classroom becomes the "site of methodological training" rather than a "space for experience consumption," the "head-up rate" [5] of ideological and political courses will truly be transformed into an explanatory and active force that "enters the brain and enters the heart."