Empowering the Connotative Development of Ideological and Political Courses in Higher Education with Digital and Intelligent Technology

Digital and intelligent technology is rapidly evolving from “generating content” toward an intelligent-agent form capable of “planning, executing, and collaborating.” This transformation provides a new technological foundation and innovative possibilities for ideological and political courses to transcend the traditional “flood-irrigation” model and strengthen the effectiveness of value guidance. With the support of new technology, we urgently need to consider how to introduce and make good use of AI educational agents, so that teaching and learning penetrate the surface layer of technological overlay, allowing “digital and intelligent empowerment” to drive deep changes in teaching concepts, pathways, and learning ecosystems.

数智技术从“生成内容”加速走向“能规划、能执行、能协同”的智能体形态。这一变革为思政课堂超越传统“漫灌”模式，强化价值引领效能，提供了全新的技术基座与创新可能。在新技术的加持下，我们亟须思考如何引入并善用AI教育智能体，使教与学穿透技术叠加的表层，让“数智赋能”驱动教学理念、路径与学习生态的深层变革。

Bottlenecks in the Connotative Development of Ideological and Political Courses in Higher Education and the Empowering Logic of AI Agents

高校思政课内涵式发展的瓶颈与AI智能体的赋能逻辑

As ideological and political courses in higher education enter a new stage of quality improvement, the focus of construction is shifting from “teaching more” to “guiding more precisely” and “implementing more solidly,” aiming to achieve an organic unity of ideological and theoretical content with affinity and relevance, thereby helping to forge a strong “ideological and political guiding force.” [1]1 “Ideological and political guiding force” (思政引领力) is a term used in Chinese education policy discourse to denote the capacity of ideological and political education to guide students’ values and thinking. However, in the practice of transformation, several key bottlenecks still urgently need to be resolved: First, real-world issues are updated rapidly, and lesson preparation and in-depth study easily fall into the predicament of “information overload—scattered materials—thin chain of evidence.” Second, classroom interaction often exhibits “abundant enthusiasm but insufficient depth,” with many expressions of opinion but little argumentation and analysis. Third, practical resources are increasingly abundant, but “having been there” does not equal “having learned”; the mechanism for the “big classroom of society” to flow back into the “small classroom” of ideological and political courses for review and improvement is unstable, making it difficult for experiences to precipitate into rational cognition and practical competence.

迈入质量提升新阶段的高校思政课，建设重点正从“讲得更多”转向“引得更准”“落得更实”，旨在实现思想性、理论性与亲和力、针对性的有机统一，助力锻造强大的“思政引领力”。但在转型实践中，仍有一些关键症结亟待破解：其一，现实议题更新快，备课与研读很容易陷入“信息过载—材料分散—证据链薄”的困境；其二，课堂互动常出现“热度有余、深度不足”，观点表达多、论证与辨析少；其三，实践资源越来越丰富，但“去过”不等于“学到”，社会大课堂回流思政小课堂复盘提升机制不稳，导致体验难沉淀为理性认知与实践素养。

AI agents simultaneously address the problem from both the “teacher teaching” and “student learning” ends, thereby supporting the connotative construction of ideological and political courses. For teachers, they focus on the systematic improvement of teaching effectiveness, solving the problem of “what to teach that is more reliable and more updatable” in content supply, and “how to teach more deeply and more generative” in classroom operation. For students, they focus on the complete construction of a learning loop, solving the problem of “how to integrate more stably and more recyclable” in the integration of learning and application, and “how to improve more accurately and more sustainably” in quality improvement, promoting the transformation of social experience beyond perceptual cognition into systematic theoretical cognition and actionable practical abilities.

AI智能体从“教师教学”与“学生学习”两端同步破题，以此支撑思政课的内涵式建设。对教师而言，它聚焦于教学效能的系统提升，在内容供给上解决“讲什么更可靠、更能更新”，在课堂运行上解决“怎么讲更深、更能生成”。对学生而言，它侧重于学习闭环的完整构建，在学用融合上解决“怎么融更稳、更可回流”，在质量改进上解决“怎么改更准、更可持续”，推动社会体验超越感性认知，转化为系统的理论认知与可操作的实践能力。

Three Levels of Leap in Teacher Teaching Capability

教师教学能力的三重跃迁

The “difficulty” of the connotative development of ideological and political courses is essentially not a lack of resources, but rather being frequently constrained by these bottlenecks. AI agents do not simply “add tools” to the classroom; they systematically embed the “data chain,” “problem chain,” and “evidence chain” into the entire teaching process, constructing a solid digital and intelligent core across the three dimensions of content production, thinking interaction, and the transformation of knowledge into action.

思政课内涵式发展的“难”，本质上不是资源不足，而是常常被这些瓶颈所牵制。AI智能体不是给课堂“加工具”，而是将 “数据链”“问题链”“证据链” 系统性嵌入教学全流程，在内容生产、思维互动与知行转化三个维度，构建起坚实的数智化内核。

First, using the “data chain” to strengthen the “experience chain,” achieving a precise upgrade in teaching preparation. AI agents do not simply find materials for teachers; rather, they complete topic aggregation, material verification, evidence comparison, and concept sorting around major themes and real-world concerns, automatically generating a logically rigorous “theory—evidence—case” teaching resource package. This liberates teachers from low-level retrieval and repetitive organization, transforming lesson preparation from “piling up materials” to “building evidence” and from “piecing together opinions” to “constructing frameworks,” providing a sustainable mechanism for precise content production to “explain deeply, thoroughly, and vividly.”

其一，以“数据链”增强“经验链”，实现教学准备的精准化升维。AI智能体不是简单地替教师找材料，而是围绕重大主题与现实关切，完成议题聚合、材料核验、证据对照与概念梳理，自动生成逻辑严密的“理论—证据—案例”教学资源包，把教师从低水平检索与重复整理中解放出来，使备课从“堆材料”转向“建证据”、从“拼观点”转向“搭框架”，为“讲深讲透讲活”提供可持续的内容精准生产机制。

Second, using the “problem chain” to drive the “thinking chain,” achieving a dialogical reconstruction of the teaching process. The key to classroom effectiveness lies not in the frequency of interaction, but in whether interaction enhances understanding. To address the problem of superficial classroom interaction, AI agents act as “deep-dialogue incubators,” analyzing discussion semantics in real time, presenting the context of opinion confrontation from multiple angles, and instantly pushing key follow-up questions, expanded resources, and theoretical mirrors, constructing a progressively deepening chain of problem guidance. Thus, classroom discussion gradually advances from simple “position expression” to “logical testing” based on facts and “dialectical thinking” based on values, promoting the transformation of teachers from “knowledge lecturers” to “thinking guides.”

其二，以“问题链”驱动“思维链”，实现教学过程的对话性重构。课堂效能的关键不在于互动频次，而在于互动是否能增进理解。针对课堂互动浅表化问题，AI智能体作为“深度对话孵化器”，实时解析讨论语义，多角度呈现观点交锋脉络，即时推送关键追问、拓展资源和理论镜鉴，构建层层深入的问题导引链。由此，课堂讨论逐渐从简单的“立场表达”进阶到围绕事实的“逻辑检验”和基于价值的“辩证思考”，推动教师从“知识讲授者”向“思维引导者”转型。

Third, using the “evidence chain” to close the “learning chain,” achieving an evidence-based transformation of teaching evaluation. To address the disconnection between social practice and theoretical teaching, AI agents act as “cross-field learning connectors,” conducting accompanying multimodal recording and structured analysis of students’ practice processes, forming a “behavior—reflection” process evidence chain. The personalized ideological and political literacy development reports generated on this basis transform teaching evaluation from a simple scoring of activity outcomes into “evidence-based guidance” that promotes the internalization of cognition and values. Teaching evaluation is no longer the endpoint of management but the starting point of development, constructing a spiral learning loop of “practice—reflection—theory—action.”

其三，以“证据链”闭环“学习链”，实现教学评价的循证化转型。针对社会实践与理论教学的脱节现象，AI智能体作为“跨场域学习连接器”，对学生的实践过程进行伴随式的多模态记录与结构化分析，形成“行为—反思”过程证据链。基于此生成的个性化思想政治素养发展报告，使教学评价从对活动结果的简单评分，转变为促进认知与价值内化的“循证引导”，教学评价不再是管理的终点，而是发展的起点，构建起“实践—反思—理论—行动”的螺旋式学习闭环。

Systematic Construction of a “Two-Classroom” Learning Ecosystem

“两个课堂”学习生态的系统性建构

The ultimate goal of “upholding fundamental principles and breaking new ground to promote the connotative development of ideological and political courses” [2]2 “Upholding fundamental principles and breaking new ground” (守正创新) is a key principle in Xi Jinping Thought, meaning to uphold the fundamental tenets of socialism while innovating in practice. is to construct a new learning ecosystem with students as the main body, connecting school and society classrooms, and to build a solid foundation for value guidance and ideological enlightenment. AI agents, centered on the three educational dimensions of “theory—history—culture” and social expansion, construct an intelligent framework that supports integration, deepens connotation, and enhances effectiveness.

“守正创新推动思政课内涵式发展”的终极目标是构建以学生为主体，贯通学校与社会课堂学习新生态，筑牢价值引领与思想启迪的深厚根基。AI智能体围绕“理论—历史—文化”三大教育维度与社会化拓展，构建起一个支撑融合、深化内涵、提升实效的智慧化框架。

On the 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. To address the fragmentation and emptiness of the three educational dimensions of theory, history, and culture, AI agents achieve deep integration and contextual activation of teaching resources. When learning theory, students can access cases, policies, and theory libraries linked in real time by the agent, autonomously constructing cognition through comparative analysis. When exploring history, students conduct role-based key decision-making simulations in highly realistic scenarios, understanding historical logic through embodied experience. When understanding culture, with the help of a traceable “cultural gene bank,” students autonomously explore the relationship between symbolic lineages and contemporary value significance, completing the internalization from perception to identification.

一方面，在内容深度整合上，AI智能体建立跨维度知识图谱，实现沉浸式学习体验的内化与升华。针对理论、历史、文化三大教育维度的割裂与空泛问题，AI智能体实现教学资源的深度整合与情境化激活。学习理论时，学生可调阅由智能体实时关联的案例、政策与理论库，在对照分析中自主建构认知；探究历史时，学生在高仿真场景中进行角色化关键决策推演，以具身体悟理解历史逻辑；理解文化时，借助可溯源的“文化基因库”，自主探究符号谱系与当代价值意义关联，完成从感知到认同的内化。

On the other hand, in terms of the systematic integration of the “two classrooms,” AI agents construct a mechanism of “intelligent matching—accompanying guidance—evidence-based feedback,” transforming society into a “living laboratory” and students into researchers carrying personalized task packages. The system matches practice goals and inquiry clues based on individual learning characteristics; through intelligent push, it transforms on-site scenes into task-interactive learning interfaces, guiding observation and reflection, and generating structured “inquiry logs.” Back in the classroom, the system-generated personal cognitive map clearly presents students’ thought trajectories and topics requiring deeper exploration, driving them to engage in deeper reflection and action with real social experiences and problems, completing the identity transformation from “participant in social practice” to “reflective learning constructor.” Taking the study of “rural revitalization” as an example: on the classroom side, the AI agent pushes personalized research plans and virtual tasks based on students’ interest tags (e.g., governance, culture); on the social side, it connects with community platforms to match interactive exploration and deep questioning in real scenarios; on the post-class feedback side, it analyzes student practice data in real time, generates a “literacy development heat map,” and guides them to return to the classroom with real problems for deep theoretical analysis.

另一方面，在“两个课堂”系统融合上，AI智能体构建“智能匹配—伴随引导—循证回流”机制，将社会转化为“活态实验室”，将学生转化为带着个性化任务包的研究者。系统根据个体学习特征匹配实践目标与探究线索；智能推送，将现场场景转化为任务交互的学习界面，引导观察与反思，生成结构化“探究日志”；回到课堂，系统生成的个人认知图谱清晰呈现学生的思想轨迹与待深化议题，驱动其带着真实的社会经验和问题，投入更深层的反思与行动，完成从“社会实践参与者”到“反思性学习建构者”的身份转变。以“乡村振兴”学习为例，课堂端，AI智能体依据学生兴趣标签（如治理、文化）推送个性化调研方案与虚拟任务；社会端，联动社区平台匹配真实场景的交互探索与深度追问；课后反馈端，实时分析学生实践数据，生成“素养发展热力图”，引导其带着真问题回归课堂进行深度理论辨析。

In summary, when AI agents become “accelerators of mechanisms” rather than “substitutes for judgment,” and when the social classroom becomes a “site for method training” rather than a “space for experiential consumption,” the “head-raising rate” [3]3 “Head-raising rate” (抬头率) is a colloquial metric in Chinese classrooms referring to the proportion of students who look up from their desks or phones to engage with the teacher, used as a proxy for student attention and engagement. of ideological and political courses will truly be transformed into the explanatory power and action capacity of “entering the mind and heart.”

总之，当AI智能体成为“机制的加速器”而不是“判断的替代者”，当社会课堂成为“方法训练的现场”而不是“体验消费的空间”，思政课的“抬头率”才会真正转化为“入脑入心”的解释力与行动力。

(The author is a Distinguished Research Fellow at the Innovation and Development Center for Ideological and Political Work of the Ministry of Education (Zhejiang Shuren University).)

（作者系教育部思想政治工作创新发展中心（浙江树人学院）特聘研究员）