Empowering the Integrated Construction of Ideological and Political Courses across Large, Medium, and Small Schools with Generative Artificial Intelligence

As a key course for implementing the fundamental task of fostering virtue through education [1]1 “Fostering virtue through education” (立德树人) is a fundamental task of Chinese education, emphasizing moral character cultivation as the foundation of talent development., ideological and political courses play an irreplaceable role in cultivating socialist builders and successors. General Secretary Xi Jinping pointed out: “The integrated construction of ideological and political courses across large, medium, and small schools should be advanced as an important project, promoting the connotative development of ideological and political course construction.” [2]2 “Connotative development” (内涵式发展) refers to a development model focused on quality improvement, structural optimization, and internal capacity building rather than quantitative expansion. Currently, artificial intelligence technology is rapidly evolving, profoundly reshaping the mode of knowledge supply and the paradigm of teaching research. Facing the trend of educational digitalization, relying on generative artificial intelligence technology to empower the integration of teaching resources, the creation of practical fields, and the construction of evaluation systems can provide strong support for promoting the reform and innovation of the integrated construction of ideological and political courses across large, medium, and small schools, further enhancing the quality and effectiveness of ideological and political education.

思政课作为落实立德树人根本任务的关键课程，在培育社会主义建设者和接班人过程中发挥着不可替代的作用。习近平总书记指出：“要把统筹推进大中小学思政课一体化建设作为一项重要工程，推动思政课建设内涵式发展。”当前，人工智能技术快速演进，正在深刻重塑知识供给模式和教学研究范式。面对教育数字化发展趋势，依托生成式人工智能技术，赋能教学资源整合、实践场域创设、评价体系构建等，可以为推进大中小学思政课一体化改革创新提供有力支撑，进一步提升思政教育质效。

Constructing a full-educational-stage ideological and political education resource library. The deep development and efficient utilization of cross-stage and cross-disciplinary curriculum resources are the foundation for the continuous deepening of the integrated construction of ideological and political courses across large, medium, and small schools. Currently, the integrated construction of ideological and political courses across large, medium, and small schools still faces multiple challenges in the integration of teaching resources. In terms of vertical articulation, there is to some extent content repetition and poor articulation between textbooks at different stages, and the spiral ascent of the knowledge system still needs further improvement; in terms of resource updating, some traditional teaching cases struggle to adapt to the rapid update trend of social hotspots, weakening the realistic explanatory power and value-leading function of ideological and political courses; in terms of resource allocation, the regional concentration of high-quality resources and the relative weakness of grassroots teaching supply constrain the balanced and high-quality development of ideological and political education to varying degrees. To meet the educational needs of the integrated construction of ideological and political courses across large, medium, and small schools in the New Era, one can rely on generative artificial intelligence technology to construct a full-educational-stage ideological and political education resource library, achieving intelligent integration and innovative supply of teaching resources. In terms of technological empowerment, through generative artificial intelligence, automatically parse the internal logic of textbooks, dynamically generate stage-articulation suggestions and gap warnings, forming a progressive knowledge chain of “enlightenment—experience—inquiry.” In terms of resource production, establish a teaching resource generation system that responds in real time to social hotspots, based on multimodal large models, intelligently transform current political events into case resources suitable for each educational stage, and automatically integrate campus-specific ideological and political education elements from each school. In terms of application ecology, form a dynamically updated teaching resource library, develop a layered resource supply platform, create immersive enlightenment scenarios for primary schools to cultivate students’ basic ideological and political awareness, construct issue-based practice workshops for middle schools to enhance students’ ideological and political practical abilities, and customize critical-thinking study matrices for universities to improve students’ ideological and political theoretical level.

构建全学段思政教育资源库。跨学段、融学科的课程资源深度开发与高效利用，是大中小学思政课一体化建设得以持续深化的基础。当前，大中小学思政课一体化建设在教学资源整合上仍面临多重考验。在纵向衔接方面，各学段教材一定程度上存在内容重复与衔接不畅的情况，知识体系螺旋上升仍需进一步完善；在资源更新方面，部分传统教学案例难以适配社会热点快速更新趋势，弱化了思政课的现实解释力与价值引领力；在资源配置方面，优质资源区域性集中与基层教学供给相对薄弱，不同程度制约思政教育优质均衡发展。满足新时代大中小学思政课一体化建设的育人需求，可依托生成式人工智能技术，构建全学段思政教育资源库，实现教学资源的智能整合与创新供给。在技术赋能方面，通过生成式人工智能自动解析教材内在逻辑，动态生成学段衔接建议与断层预警，形成“启蒙—体验—探究”的进阶式知识链。在资源生产方面，建立实时响应社会热点的教学资源生成系统，基于多模态大模型，将时政事件智能转化为适配各学段的案例资源，并自动融入各学校的校园特色思政育人元素。在应用生态方面，形成动态更新的教学资源库，开发分层资源供给平台，为小学打造沉浸式启蒙场景，培养学生的基本思政意识；为中学构建议题式实践工坊，增强学生的思政实践能力；为大学定制思辨型研习矩阵，提升学生的思政理论水平。

Creating a virtual-real integrated ideological and political education space. A multi-scenario, all-round practical education system is the practical foundation and innovation engine for the in-depth advancement of the integrated construction of ideological and political courses across large, medium, and small schools. The practical education link currently faces some real difficulties. For example, a stepwise ascending path has not yet been fully formed between primary school enlightenment practice, middle school inquiry practice, and university innovative practice; offline practice scenarios are limited by temporal and spatial resources, making it difficult to meet large-scale and personalized educational needs, and students in remote areas face the structural shortage of practice opportunities; traditional practical teaching has a tendency to emphasize process over feedback and form over internalization, and the closed-loop mechanism for shaping students’ values and enhancing their innovation ability needs further improvement; and so on. To solve the difficulties faced by practical education, one can rely on generative artificial intelligence to construct a “virtual-real symbiosis, stage-through” intelligent practice ecosystem. In terms of technological empowerment, through generative artificial intelligence, dynamically construct historical scene simulation models and social problem deduction sandboxes, achieving a stepped practice scenario of immersive perception in primary school, interactive inquiry in middle school, and innovative problem-solving in university. In terms of spatial reconstruction, comprehensively utilize technologies such as AR (augmented reality), VR (virtual reality), and MR (mixed reality) to create a practice matrix with multi-party linkage of “family—campus—society” and virtual-real integration of “theory—scenario—emotion.” In terms of effectiveness transformation, construct an intelligent analysis system for ideological and political course practice outcomes, based on large language models to automatically evaluate students’ practice outcomes and generate personalized improvement plans, track students’ value growth trajectories through ideological and political knowledge graphs, provide teachers with precise analysis of students’ ideological and political literacy development, and form a full-educational-stage ideological and political practice intelligent agent with progressive goals, global data sharing, and continuous effectiveness transformation, to comprehensively support the implementation of the educational goals of the integrated construction of ideological and political courses across large, medium, and small schools.

创设虚实融合的思政育人空间。多场景、全方位的实践育人体系是大中小学思政课一体化建设纵深推进的实践基础与创新引擎。实践育人环节当前面临一些现实困境。比如，小学启蒙性实践、中学探究性实践与大学创新性实践之间尚未完全形成阶梯式上升路径；线下实践场景受时空资源限制，难以满足规模化、个性化的育人需求，偏远地区学生更面临实践机会结构性短缺的困境；传统实践教学存在重过程轻反馈、重形式轻内化的倾向，学生价值观塑造与创新能力提升的闭环机制有待进一步完善；等等。破解实践育人面临的困境，可依托生成式人工智能构建“虚实共生、学段贯通”的智慧实践生态系统。在技术赋能方面，通过生成式人工智能动态构建历史场景仿真模型与社会问题推演沙盘，实现小学阶段沉浸式感知、中学阶段交互式探究、大学阶段创新式求解的阶梯化实践场景。在空间重构层面，综合运用AR（增强现实）、VR（虚拟现实）、MR（混合现实）等技术，打造“家庭—校园—社会”多方联动、“理论—场景—情感”虚实融合的实践矩阵。在成效转化层面，构建思政课实践成果智能分析系统，基于大语言模型自动评估学生的实践成果并生成个性化改进方案，通过思政知识图谱追踪学生价值观成长轨迹，为教师提供精准的学生思政素养发展情况分析，形成目标分层递进、数据全域共享、成效持续转化的全学段思政实践智能体，以全面支撑大中小学思政课一体化建设育人目标落地。

Building a whole-process ideological and political education evaluation ecosystem. A scientific and integrated teaching evaluation system is an important support and quality guarantee for the implementation of the integrated construction of ideological and political courses across large, medium, and small schools. Currently, the integrated evaluation system for ideological and political courses across large, medium, and small schools still suffers from fragmentation and simplification. In terms of evaluation orientation, evaluation dominated by examination scores still prevails, which can easily lead to the simplification of the educational goals of value shaping and ability cultivation in ideological and political courses into score representations. In terms of evaluation dimensions, values and emotional attitudes to some extent lack effective quantitative tools, and traditional paper-and-pencil tests struggle to capture the dynamic trajectory of students’ ideological growth. In terms of stage articulation, evaluation standards relatively lack vertical coherence, making it impossible to fully depict the progressive process of students’ ideological and political literacy, weakening the integrated educational effect of gradual progress and spiral ascent. To build a whole-process ideological and political education evaluation ecosystem, one can rely on generative artificial intelligence technology to create a “data-driven—intelligent diagnosis—continuous improvement” intelligent evaluation system. In terms of technical support, through a multimodal learning analysis engine, collect data such as classroom speech micro-expressions and online ideological and political learning trajectories in real time, use generative artificial intelligence to construct an emotional attitude calculation model, and transform the value formation process into a visual growth curve. In terms of algorithmic innovation, develop a dynamic assessment system for ideological and political knowledge mastery, based on large language models to automatically generate personalized diagnostic reports, construct a cross-stage evaluation standard converter, achieving intelligent articulation between primary school ideological and political learning behavior observation records, middle school project-based evaluation, and university academic assessment. In terms of system integration, create a full-educational-stage cycle ideological and political literacy digital education portrait, through the predictive analysis function of generative artificial intelligence, provide ideological and political course teachers at each stage with information such as each student’s ideological and political education background, thereby providing guidance for personalized and differentiated teaching, forming a multi-dimensional intelligent evaluation system covering cognitive development, practical participation, and value shaping.

打造全过程思政教育评估生态。科学化、一体化的教学评价体系是大中小学思政课一体化建设落地落实的重要支撑与质量保障。当前，大中小学思政课一体化评价体系仍存在碎片化、单一化问题。在评价导向层面，以考试成绩为主的评价仍占主导，容易导致思政课的价值塑造与能力培养的育人目标被简化为分数表征。在评价维度层面，价值观与情感态度等在一定程度上缺乏有效的量化工具，传统纸笔测试难以捕捉学生思想成长的动态轨迹。在学段衔接层面，评价标准相对缺乏纵向连贯性，无法完整刻画学生思政素养的进阶过程，弱化了循序渐进、螺旋上升的一体化育人效果。构建全过程思政教育评估生态，可依托生成式人工智能技术，打造“数据驱动—智能诊断—持续改进”的智慧评价系统。在技术支撑层面，通过多模态学习分析引擎，实时采集课堂语音微表情、在线思政学习轨迹等数据，利用生成式人工智能构建情感态度计算模型，将价值观形成过程转化为可视化成长曲线。在算法创新层面，开发思政知识掌握度动态评估系统，基于大语言模型自动生成个性化诊断报告，构建跨学段评价标准转换器，实现小学思政学习行为观察记录、中学项目化评价与大学学术性评估的智能衔接。在系统集成层面，创建全学段周期思政素养数字育人画像，通过生成式人工智能的预测性分析功能，为各学段思政课教师提供每个学生的思政教育背景等信息，从而为个性化、差异化教学提供指导意见，形成覆盖认知发展、实践参与、价值塑造的多维度智能评价系统。

Online editor: Tongxin

网络编辑：同心