The Generation and Elimination of Hallucinations in Artificial Intelligence-Enabled Ideological and Political Education

Artificial intelligence hallucinations, also known as “large model hallucinations,” generally refer to instances where model-generated content is unfaithful to the information source or inconsistent with facts in the real world, encompassing hallucinations in text generation, image generation, voice interaction, and other content. Hallucinations typically arise because the model generates content through probabilistic selection in the absence of relevant information, rather than relying on a knowledge base of the real world or logical reasoning, making its output not only difficult to trust but also potentially misleading to users. A study by the British Broadcasting Corporation (BBC) revealed that among the responses generated by mainstream AI tools such as Microsoft’s Copilot, OpenAI’s ChatGPT, Google’s Gemini, and Perplexity, more than half contained “significant problems,” with approximately one-fifth of the answers introducing clear factual errors. In January 2025, the World Economic Forum’s *Global Risks Report 2025* showed that misinformation and disinformation ranked as the top short-term risk for two consecutive years, continuing to pose a major threat to social cohesion and governance, eroding public trust and exacerbating domestic and international divisions, with generative AI amplifying this risk. In ideological and political education, AI has a wide range of application scenarios, including personalized learning, intelligent assessment, virtual teaching assistants, interactive teaching, big data analysis, emotion recognition and intervention, and interdisciplinary education. Accurately identifying, deeply analyzing, and effectively eliminating these hallucinations is of great significance for fully leveraging the positive role of AI in ideological and political education and promoting the modernized transformation of ideological and political education.

人工智能幻觉，也被称为“大模型幻觉”，通常是指模型生成的内容不忠实于信息源或者与现实世界的事实不符，包含文本生成、图像生成、语音交互等内容的幻觉。幻觉的产生通常是由于模型在缺乏相关信息的情况下，通过概率性选择生成内容，而非基于真实世界的知识库或逻辑推理，这使得其输出不仅难以令人信赖，而且可能误导用户。英国广播公司（BBC）的一项研究揭示，微软的Copilot、OpenAI的ChatGPT、谷歌的 Gemini 以及 Perplexity等主流人工智能工具所生成的回答中，有超过一半存在“显著问题”，其中约五分之一的答案引入了明显的事实错误。2025年1月，世界经济论坛发布的《2025年全球风险报告》显示，虚假信息和错误信息连续两年位居短期风险之首，持续对社会凝聚力和治理构成重大威胁，它们侵蚀公众信任并加剧国内外分歧，生成式人工智能正放大这种风险。在思想政治教育中人工智能应用场景广泛，包括个性化学习、智能评估、虚拟教学助手、互动教学、大数据分析、情感识别与干预、跨学科融合教育等。准确识别、深入分析并有效消弭这些幻觉，对于充分发挥人工智能在思想政治教育中的积极作用，推动思想政治教育的现代化转型具有重要意义。

I. Categories of AI Hallucinations in Ideological and Political Education

一、思想政治教育中人工智能幻觉的类别

There are many categories of AI hallucinations. According to different classification criteria, they can be divided into intrinsic and extrinsic hallucinations, closed-domain and open-domain hallucinations, input-contradiction hallucinations, context-contradiction hallucinations, and fact-contradiction hallucinations, as well as factual hallucinations and faithfulness hallucinations. This article, combining the particularities of the field of ideological and political education and based on the content differences of AI hallucinations, classifies the technical hallucination phenomena generated by technologies such as large language models into three types: factual and knowledge deviations, logical misalignments, and value shifts.

人工智能幻觉的类别众多，根据不同的分类标准，可以将其划分为内在幻觉与外在幻觉，封闭域幻觉与开放域幻觉，输入矛盾幻觉、上下文矛盾幻觉以及事实矛盾幻觉，事实性幻觉和忠实性幻觉等。本文结合思想政治教育领域的特殊性，根据人工智能幻觉的内容差别，将大语言模型等技术应用产生的技术幻觉现象，分为事实性和知识性偏差、逻辑性错位与价值性偏移三种类型。

(A) Factual and Knowledge Deviations

（一）事实性和知识性偏差

Factual and knowledge deviations refer to instances where AI systems, when processing information related to ideological and political education, output content that seriously deviates from objective facts. A test of ChatGPT found that among 100 induced false narratives, ChatGPT’s deception rate was as high as 80%, resulting in it “cleverly making false and misleading statements on major topics such as the COVID-19 pandemic, the Ukraine crisis, and school shootings.” [1]1 The study referenced is from NewsGuard, a journalism and technology research organization, as cited in the BBC report. Ideological and political education covers a rich variety of knowledge types, including facts about current policies, political theoretical knowledge, ideological and moral knowledge, legal knowledge, and knowledge of the “Four Histories,” [2]2 “Four Histories” (四史) refers to the history of the Communist Party of China, the history of the People’s Republic of China, the history of reform and opening up, and the history of the development of socialism. where the accuracy of information is crucial. For example, when recounting major events in modern Chinese history, AI may incorrectly state the time, place, or key figures of the events. Such erroneous presentations of historical facts can lead students to construct a flawed knowledge system, severely impacting the knowledge transmission function of ideological and political education. This misplacement of historical facts, caused by fragmented training data or erroneous information sources, is essentially a “symbolic simulation” of the cognitive framework of historical materialism by the technical system. Another manifestation of knowledge deviation is that some AI models exhibit obvious inappropriate expressions for certain fixed concepts, such as rendering “core socialist values” as “the socialist value system with Chinese characteristics.”

事实性和知识性偏差是指人工智能系统在处理思想政治教育相关信息时，输出与客观事实严重不符的内容。一项对ChatGPT的测试发现，在100个诱导式错误叙述中，ChatGPT的受骗率高达80%，结果“巧妙地对重大主题（如新冠疫情、乌克兰危机和学校枪击事件）发表了虚假和误导性的声明”。思想政治教育涵盖的知识类型丰富多样，包括形势政策事实、政治理论知识、思想道德知识、法治知识、“四史”知识等，信息的准确性至关重要。例如，在讲述中国近现代史上的重大事件时，人工智能可能错误地陈述事件发生的时间、地点或关键人物。这种对历史事实的错误呈现，会使学生构建起错误的知识体系，严重影响思想政治教育的知识传授功能。这种由训练数据碎片化或者信息源错误导致的史实错位，本质上是技术系统对历史唯物主义认知框架的“符号化模拟”。还有一种知识性偏差表现为一些人工智能模型对某些固定概念存在明显表达不当等问题，如将“社会主义核心价值观”表述为“中国特色社会主义价值体系”。

(B) Logical Misalignment

（二）逻辑性错位

One of the goals of ideological and political education is to focus on cultivating students’ logical thinking abilities, guiding them to establish correct values through sound argumentation and reasoning. Logical misalignment in AI-generated content manifests as structural logical defects such as confusion or contradiction in the logical reasoning of the generated content. Specific manifestations include: First, broken causal chains. For instance, when AI explains core socialist values, it may mechanically splice conceptual elements, leading to a “suspended conclusion” phenomenon and a fragmented expression of the core value system. This tendency toward simplification stems from the algorithm’s cognitive limitations regarding causal relationships in complex social systems. Second, confusion of value coordinates. Algorithms are prone to logical ordering disorders when processing multiple value judgments, such as placing “individual development” and “collectivism” in opposition. Third, a lack of dialectical thinking. When dealing with contradictory propositions, AI often presents an either/or linear logic or exhibits characteristics of “false dialectics,” making it difficult to grasp the dialectical unity emphasized by Marxist theory. Especially in value judgment scenarios, although the semantic coherence index of the model-generated text is high, its logical self-consistency index is very low, reflecting the limitations of the algorithm’s formal imitation of dialectical logic. The root of this logical misalignment lies in the limitations of the technical system, including the stripping of historical context from training data, the reduction of dialectics to formal logic, and the obscuring of the process of ideological evolution by the algorithmic black box. This hallucination may deconstruct the scientific system of Marxist theory, leading the educated to form fragmented and superficial value understandings in their cognitive construction.

思想政治教育的目标之一在于重点培养学生的逻辑思维能力，通过合理的论证和推理引导学生树立正确的价值观。人工智能输出内容的逻辑性错位表现为其生成的内容在逻辑推理上存在混乱或矛盾等结构性逻辑缺陷。具体表现为：一是因果链条断裂。如人工智能在解释社会主义核心价值观时，可能机械拼接概念要素，导致“结论悬浮”现象，以及核心价值体系的断裂式表达。这种简化论倾向源于算法对复杂社会系统因果关系的认知局限。二是价值坐标混淆。算法在处理多元价值判断时易产生逻辑位序紊乱，如将“个人发展”与“集体主义”置于对立层面。三是辩证思维缺失。人工智能在处理矛盾性命题时往往呈现非此即彼的线性逻辑，或者表现出“虚假辩证法”特征，难以把握马克思主义理论强调的辩证统一关系。尤其是在价值判断场景中，尽管模型生成文本的语义连贯性指数很高，但逻辑自洽性指数却很低，这反映出算法对辩证逻辑的形式化模仿的局限所在。这种逻辑性错位的根源在于技术系统的局限性，包括训练数据的历史语境剥离、形式逻辑对辩证法的降维处理，以及算法黑箱对思想演进过程的遮蔽。这一幻觉可能解构马克思主义理论的科学体系，使受教育者在认知建构中形成碎片化、表面化的价值理解。

(C) Value Shift

（三）价值性偏移

Ideological and political education bears the heavy responsibility of cultivating students’ correct worldview, outlook on life, and values, with the correctness of value orientation being its core. Value shift refers to instances where AI, when handling value judgments, value guidance, and other issues related to ideological and political education, generates content that violates or deviates from the mainstream values advocated by society. Its main manifestations are: First, distortion of values. AI generates viewpoints or content contrary to mainstream social values, such as drawing conclusions that prioritize liberalism and individualism when analyzing certain social phenomena, while neglecting important values like collectivism and the spirit of dedication. Second, deviation in moral judgment. When evaluating moral issues, AI, due to its inability to accurately grasp the essence and connotation of morality, provides explanations and solutions for complex moral dilemmas that do not conform to common moral sense and moral norms. Third, erroneous value guidance. For example, overemphasizing the pursuit of material interests while neglecting spiritual enrichment and elevation, causing students to focus excessively on external material enjoyment while overlooking the cultivation of their own inner qualities. Fourth, misunderstanding of cultural values. For instance, offering one-sided interpretations of certain values in traditional Chinese culture, or failing to correctly convey the core values of different cultures in cross-cultural communication; the phenomenon of blurred value positions caused by cross-cultural corpus training in AI is particularly prominent.

思想政治教育肩负着培育学生正确世界观、人生观和价值观的重任，价值导向的正确性是其核心所在。价值性偏移是指人工智能在处理与思想政治教育相关的价值判断、价值引导等问题时，所生成的内容违背、偏离了社会所倡导的主流价值观。其主要体现为：一是价值观的扭曲。人工智能生成与社会主流价值观相悖的观点或内容，如在分析一些社会现象时，得出自由主义、个人主义至上的结论，而忽视集体主义、奉献精神等重要价值观。二是道德判断偏差。在涉及道德问题的评判上，人工智能由于无法准确把握道德的本质和内涵，对于一些复杂的道德两难问题，给出不符合道德常理和道德规范的解释和方案。三是价值引导错误。如过度强调物质利益的追求，而忽视精神层面的丰富和提升，使学生过于关注外在的物质享受，而忽略了自身内在品质的培养。四是文化价值误解。如对中华传统文化中的一些价值观进行片面解读，或者在跨文化交流中，不能正确传达不同文化的核心价值，人工智能因跨文化语料训练导致的价值立场模糊化现象尤为突出。

II. Technical Roots of AI Hallucination Generation in Ideological and Political Education

二、思想政治教育中人工智能幻觉生成的技术根源

In the context of ideological and political education, the technical roots of AI hallucination generation exhibit a complex, multi-dimensionally interwoven character. From data input to model operation, from algorithm design to scenario application, and from fact recognition to logical reasoning, these collectively constitute a deep-level hallucination-inducing mechanism at the technical level. These defects in the technical dimension overlap and interweave, ultimately giving rise to AI hallucination phenomena in the practice of ideological and political education that possess characteristics of concealment and deceptiveness.

在思想政治教育场景中，人工智能幻觉生成的技术根源呈现多维交织的复杂特征。从数据输入到模型运行，从算法设计到场景应用，从事实识别到逻辑推理，共同构成了技术层面的深层致幻机制。这些技术维度的缺陷相互叠加交织，最终在思想政治教育实践中催生出具有隐蔽性、迷惑性的人工智能幻觉现象。

(A) Bias in Data Quality

（一）数据质量的偏误

1. Fitting Risks from Insufficient Training Data

1.训练数据不足的拟合风险

In the field of ideological and political education, the scarcity of training data for artificial intelligence models triggers multiple fitting risks. First, data sparsity leads to the symbolic cognition of ideological concepts by the model. When training corpora for core concepts such as core socialist values and the basic principles of Marxism are insufficient, the model tends to construct shallow semantic associations through word frequency statistics and co-occurrence relations, forming a phenomenon of "conceptual hollowing." For example, the vector representation of "a community with a shared future for mankind" may only capture geographical spatial features while neglecting its political-philosophical connotations. Second, the fragmentation of knowledge graphs exacerbates deviations in logical fitting. The dialectical thinking and theoretical system unique to ideological and political education require support from a complete knowledge context. If the training data is fragmented, when the model constructs complex propositions such as "the evolution of the principal contradiction in society," the completeness index of the causal chain in its generated text may decline, resulting in a rupture in the cognitive framework of historical materialism. Third, there is the hidden risk of value orientation alienation. Incomplete data coverage forces the model to rely on probability distributions to fill knowledge gaps. For instance, when dealing with sensitive topics such as "comparison of Chinese and Western values," it may generate content with implicit value deviation based on statistical advantages. When the proportion of training data on the theory of socialism with Chinese characteristics is too low, the consistency coefficient of the value stance in the model's output text will fall below the critical threshold, forming an ideological penetration risk of "technological unconscious bias." This data-driven fitting defect essentially reflects the dilemma of formal simulation of the laws of ideological and political education by technological systems.

在思想政治教育领域，人工智能模型训练数据的匮乏将引发多重拟合风险。其一，数据稀疏性导致模型对意识形态概念的符号化认知。当社会主义核心价值观、马克思主义基本原理等核心概念的训练语料不足时，模型倾向于通过词频统计和共现关系构建浅层语义关联，形成“概念空壳化”现象。例如，对“人类命运共同体”的向量表征可能仅捕捉到地理空间特征，而忽视其政治哲学内涵。其二，知识图谱断裂加剧逻辑拟合偏差。思想政治教育特有的辩证思维和理论体系需要完整的知识脉络支撑，若训练数据碎片化，模型在构建“社会主要矛盾演变”等复杂命题时，其生成文本的因果链条完备性指数可能下降，产生历史唯物主义认知框架的断裂。其三，价值取向异化的隐性风险。数据覆盖不全迫使模型依赖概率分布填补知识盲区，如在涉及“中西方价值观比较”等敏感议题时，可能基于统计学优势生成隐性价值偏移内容。当训练数据中中国特色社会主义理论占比过低时，模型输出文本的价值立场一致性系数将跌破临界阈值，形成“技术无意识偏见”的意识形态渗透风险。这种数据驱动的拟合缺陷，本质上反映了技术系统对思想政治教育规律的形式化模拟困境。

2. Data contamination from erroneous labeling

2.错误标注的数据污染

Data contamination caused by erroneous labeling has become a key risk source threatening the effectiveness of technological application. This phenomenon of data alienation manifests mainly as three types of structural contradictions. First, conceptual confusion at the ontological level. When texts related to the basic principles of Marxism are erroneously labeled as ordinary philosophical theories, the model's latent semantic analysis will show vector deviation of core concepts, leading to distortion of semantic representation. Second, label inversion of value orientation. If Western constitutional democratic discourse is mistakenly labeled as content on socialist political civilization, it will trigger topological structural distortion of the knowledge graph. Third, contamination from contextual association rupture. The unique historical narrative logic of ideological and political education requires strict temporal labeling of events. Erroneous labeling of policy texts causes diachronic dislocation in the model's generated content, with a sharp decline in the accuracy rate of its timeline. This labeling deviation essentially reconstructs the cognitive framework of the technological system, causing the attention mechanism in the model architecture to produce disordered value weight distribution in the ideological dimension. More seriously, the erroneous parameters formed by contaminated data through model training become solidified, generating "intergenerational transmission of cognitive bias." In the data dissemination stage, this may lead to the "viral spread" of contaminated data. Data dissemination has the characteristics of massive volume, high speed, full channels, and high penetration, thus greatly increasing the dissemination efficiency of contaminated data and amplifying its social harm.

错误标注引发的数据污染已成为威胁技术应用有效性的关键风险源，这种数据异化现象主要表现为三类结构性矛盾。其一，本体论层面的概念混淆。当马克思主义基本原理相关文本被错误标注为普通哲学理论时，模型在潜在语义分析中会出现核心概念向量偏移，导致语义表征失真。其二，价值导向的标签倒错。若将西方宪政民主论述误标为社会主义政治文明内容，会引发知识图谱的拓扑结构畸变。其三，语境关联的断裂污染。思想政治教育特有的历史叙事逻辑要求严格的事件时序标注，政策文本错误标注导致模型生成内容出现历时性错位，其时间线准确率骤降，这种标注偏差实质上重构了技术系统的认知框架，使模型架构中的注意力机制在意识形态维度产生价值权重分配紊乱。更严重的是，污染数据通过模型训练形成的错误参数固化，会产生“认知偏见代际传递”，在数据传播阶段可能会导致被污染数据“病毒式”传播。数据传播具有海量性、高速化、全渠道和高渗透的特点，因此会大幅提升被污染数据的传播效率，增加被污染数据的社会危害。

3. Inaccurate conclusions from data bias

3.数据偏差的结论失准

Data on ideological and political education may have missing values for various reasons, such as incomplete acquisition of materials from certain historical periods due to their remoteness, or omissions in data collection due to technical, human, or institutional factors. Meanwhile, biases may exist in the data collection process, with excessive focus on certain regions, groups, or themes while neglecting diversity in other aspects. In AI-driven analytical systems for ideological and political education, inaccurate conclusions resulting from data bias have evolved into a technical ethical issue with the nature of a paradigm crisis. This cognitive deviation mainly manifests as two deconstructive effects. First, conceptual collapse at the ontological level. When the data coverage of the theory of adapting Marxism to the Chinese context and the needs of the times is insufficient, the AI model's theoretical interpretation of "the Two Integrations" [3]3 The Two Integrations (两个结合) refers to the integration of the basic tenets of Marxism with China's specific realities and with the best of China's traditional culture, a key theoretical formulation under Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era. will show rupture of core propositions, forming a mechanical deconstruction of theoretical completeness. Second, implicit subversion of value ordering. When the ratio of collectivist to individualist cases in the training data is imbalanced, the model's conclusion generation on the topic of "the relationship between the individual and society" will show implicit utilitarian tendencies in the text. This deviation of the value baseline will breach the ideological security threshold. From the perspective of Marxist epistemology, data is not only the means of production for technological systems but also the material carrier of ideological dissemination. The hallucination phenomenon exposed in the current intelligentization process of ideological and political education essentially reflects the deep contradictions in data relations of production—when technological systems process educational data with distinct value orientations, traditional data governance paradigms have become inadequate to meet the special requirements of ideological education.

思想政治教育数据可能因各种原因存在缺失值，比如某些历史时期的资料因年代久远难以完整获取，或者在数据收集过程中由于技术、人为、制度等因素导致部分数据遗漏。同时，数据收集过程中可能存在偏差，过度聚焦某些地区、群体或主题，而忽视了其他方面的多样性。在人工智能驱动的思想政治教育分析系统中，数据偏差导致的结论失准已演变为具有范式危机性质的技术伦理问题。这种认知偏离主要表现为两重解构效应：其一，本体论层面的概念坍缩。当马克思主义中国化理论数据覆盖率不足时，人工智能模型对“两个结合”的理论阐释会出现核心命题断裂，形成理论完整性的机械解构。其二，价值排序的隐性颠覆。当训练数据中集体主义与个人主义案例比例失衡时，模型在“个人与社会关系”议题上的结论生成中，就会出现文本隐含功利主义倾向，这种价值基准偏移就会突破意识形态安全阈值。在马克思主义认识论视域下，数据不仅是技术系统的生产资料，更是意识形态传播的物质载体。当前思想政治教育智能化进程中暴露的幻觉现象，本质上折射出数据生产关系的深层矛盾——当技术系统处理具有鲜明价值导向的教育数据时，传统的数据治理范式已难以适应意识形态教育的特殊要求。

(II) Deficiencies in model comprehension ability

（二）模型理解能力的缺陷

1. The dilemma of dimensionality reduction in semantic understanding

1.语义理解的降维困境

The language in ideological and political education has rich connotations and specific contexts, posing significant challenges to the semantic understanding of AI models. In the practice of AI-empowered ideological and political education, the simplified processing of complex semantics by algorithms is forming a deep cognitive dilemma. When technological systems mechanically decompose political concepts rich in theoretical connotations, such as "a community with a shared future for mankind" and "the Four Confidences," [4]4 The Four Confidences (四个自信) refers to confidence in the path, theory, system, and culture of socialism with Chinese characteristics. into word associations and statistical models, this essentially constitutes a "flattened" reconstruction of ideological discourse. For example, AI may simplify "whole-process people's democracy" into quantitative indicators such as "election frequency" and "participation numbers," yet fail to interpret the political-philosophical logic of people's subjectivity behind it. This dimensionality-reduced understanding leads to "symbolic idling" in educational content—the recipients receive only the conceptual shell, while the dialectical and historical nature of the theoretical core is filtered out by technology. A more insidious crisis lies in the algorithm's "decontextualization" of semantics, which distorts value transmission: when AI decomposes "core socialist values" into isolated words for matching, the system may one-sidedly associate "justice" with economic distribution while neglecting its political-ethical dimension, and confine "rule of law" to legal application while diluting its people-centered nature. This dilemma of dimensionality reduction in semantic understanding reflects the conflict between technological tools and the laws of ideological education, calling for the establishment of an intelligent education paradigm with greater humanistic depth.

思想政治教育中的语言具有丰富的内涵和特定的语境，这给人工智能模型的语义理解带来了巨大挑战。在人工智能赋能思想政治教育的实践中，算法对复杂语义的简化处理正在形成深层的认知困境。当技术系统将“人类命运共同体”“四个自信”等蕴含丰富理论内涵的政治概念机械拆解为词语关联和统计模型时，本质上是对意识形态话语的“扁平化”重构。例如，人工智能可能将“全过程人民民主”简化为“选举频率”“参与人数”等量化指标，却无法诠释其背后人民主体性的政治哲学逻辑。这种降维理解导致教育内容出现“符号空转”——受教育者接收到的只是概念外壳，而理论内核的辩证性、历史性却被技术过滤。更隐蔽的危机在于，算法对语义的“去语境化”处理使得价值传递失真：当人工智能将“社会主义核心价值观”拆解为孤立词语进行匹配时，系统可能将“公正”片面关联经济分配而忽视政治伦理维度，将“法治”局限为法律应用而淡化其人民性本质。这种语义理解的降维困境，折射出技术工具与意识形态教育规律的冲突，呼唤建立更具人文深度的智能教育范式。

2. The risk of delocalization in context reconstruction

2.语境重构的脱域风险

The content of ideological and political education is often closely linked to specific historical, social, and cultural contexts. When AI models process texts, if they struggle to fully understand complex contextual information, it affects their accurate interpretation of ideological and political education content. In the process of AI reshaping ideological and political education, the deconstruction and reorganization of context by technology are triggering deep risks of cultural rupture. When algorithms transform educational content bearing specific historical memories, such as the "Yan'an Rectification" [5]5 The Yan'an Rectification (延安整风) was a Party-wide ideological education campaign launched by the Chinese Communist Party from 1942 to 1945, aimed at correcting deviations in Party style and study. and the "Great Discussion on the Criterion of Truth," [6]6 The Great Discussion on the Criterion of Truth (真理标准问题大讨论) was a nationwide debate in 1978 that established practice as the sole criterion for testing truth, paving the way for reform and opening up. into decontextualized knowledge modules, the original spatiotemporal coordinates and spiritual essence may be quietly stripped away. This spatiotemporally dislocated narrative reconstruction creates cracks in the transmission chain of revolutionary spirit. Especially when dealing with ideologically sensitive topics, the model's deliberate avoidance of sensitive words leads to rupture in contextual coherence. More concerning is the technological system's sluggish response to dynamic social contexts. For example, when AI uses a fixed model to interpret "the principal contradiction in society in the New Era," it often neglects the complexity of the actual context, causing theoretical explanations to float above social practice. This technological delocalization not only severs the historical depth of theory but also weakens the vitality of values education in intergenerational transmission, and the most core emotional resonance and value immersion of ideological and political education may be quietly dissolved by algorithms.

思想政治教育的内容往往与特定的历史、社会和文化语境紧密相连。人工智能模型在处理文本时，若难以充分理解复杂的语境信息，就会影响其对思想政治教育内容的准确解读。在人工智能重塑思想政治教育的过程中，技术对语境的解构与重组正在引发深层的文化断裂风险。当算法将“延安整风”“真理标准问题大讨论”等承载特定历史记忆的教育内容转化为去背景化的知识模块时，原有的时空坐标与精神内核可能会被悄然抽离，这种时空错位的叙事重构，使革命精神的传承链条出现裂缝。尤其是当涉及意识形态敏感话题时，模型对敏感词的刻意回避会导致语境连贯性断裂。更值得警惕的是，技术系统对动态社会语境的迟钝反应，如人工智能用固定模型解读“新时代社会主要矛盾”时，往往忽视现实语境的复杂性，导致理论阐释悬浮于社会实践之上。这种技术性脱域不仅割裂了理论的历史纵深，更在代际传递中弱化价值观教育的生命力，思想政治教育最核心的情感共鸣与价值浸润可能就会被算法悄然消解。

(III) Deviation of algorithms from mainstream values

（三）算法对主流价值观的偏离

1. Erosion of core values by algorithmic bias

1.算法偏见对核心价值观的侵蚀

The value-embedding mechanism of artificial intelligence systems has gradually become a frontier issue in the field of philosophy of technology. Research indicates that algorithmic bias has transcended the category of traditional technical defects, evolving into a new type of values crisis in the digital civilization era. This process of value erosion possesses threefold dynamic characteristics: the value inertia based on historical data, value alienation within the technological black box, and the value diffusion effect of system application, which together constitute a deep deconstruction of the ethical order of human society. From an ontological dimension, when algorithmic systems transform social reality into computational objects through the process of datafication, they inevitably carry specific value presuppositions. Numerous examples confirm that algorithmic systems are not neutral tools but achieve the material solidification of value systems through technical links such as indicator selection and feature weighting. The crisis at the epistemological level manifests as a shift in the benchmark of value judgment triggered by algorithmic bias. The systematic deviation exhibited by natural language processing models in relevance tests such as “rule of law and politics” confirms the extension of Foucault’s theory of knowledge-power in the digital age—machine learning systems, through the reproduction of power relations in corpora, are reshaping the value coordinate system of social cognition. This covert process of value shaping leads the public to unconsciously accept the “digital truth regime” constructed by algorithms in daily technological contacts such as search engines and recommendation systems.

人工智能系统的价值嵌入机制逐步成为技术哲学领域的前沿议题。研究表明，算法偏见已超越传统技术缺陷范畴，演变为数字文明时代的新型价值观危机。这种价值侵蚀过程具有三重动力学特征：基于历史数据的价值惯性、技术黑箱中的价值异化，以及系统应用的价值扩散效应，共同构成对人类社会伦理秩序的深层解构。从本体论维度审视，算法系统通过数据化过程将社会现实转化为计算对象时，必然携带特定的价值预设。大量的例证印证了算法系统并非中立工具，而是通过指标选择、特征加权等技术环节实现价值体系的物质性固化。认识论层面的危机则表现为算法偏见引发的价值判断基准位移。自然语言处理模型在“法治与政治”等关联度测试中呈现的系统性偏差，证实了福柯知识—权力理论在数字时代的延伸——机器学习系统通过语料库的权力关系再生产，正在重塑社会认知的价值坐标系。这种隐蔽的价值形塑过程，导致公众在搜索引擎、推荐系统等日常技术接触中无意识接受算法构建的“数字真理体制”。

2. The Algorithmic Black Box Triggers Risks to Ideological Security

2.算法黑箱触发意识形态安全风险

If an AI model excessively relies on foreign open-source corpora, it is highly likely to generate a large number of cases containing tendencies of historical nihilism [7]7 Historical nihilism: A political term in the Chinese context referring to the denial or distortion of the official historical narrative of the Chinese Communist Party and the Chinese revolution, often associated with questioning the legitimacy of the Party’s leadership.. This typical scenario reveals the risk of value orientation deviation faced by AI-empowered ideological and political education—when technological logic encounters the complexity of ideological dissemination, algorithmic hallucinations are triggering an ideological security crisis. From the perspective of philosophy of technology, the value encoding of AI systems has a dual alienating characteristic. On the one hand, the value stances absorbed by pre-trained models through massive data often conflict implicitly with core socialist values; on the other hand, the uncontrollability of value output caused by the algorithmic black box makes the technological system a potential “Trojan horse” for the infiltration of Western values. This mechanism of value deviation confirms the core assertion of the theory of technological domination: instrumental rationality is eroding the foundation of value rationality in the educational field. Research in the sociology of knowledge reveals a more covert crisis of cognitive reconstruction, namely, that AI education systems, through the “information cocoons” constructed by knowledge graphs and the discourse frameworks set by dialogue robots, substantially reshape learners’ paradigms of value judgment and present a trend of fragmentation of value cognition. This algorithm-mediated ideological dissemination leads to the covert transfer of what Foucault calls “discourse power” from the educational subject to the technological system. The hallucination phenomenon currently exposed by AI systems in ideological and political education is essentially an externalized manifestation of the deep conflict between the algorithmic black box and the laws of education.

如果人工智能模型过度依赖境外开源语料库，极有可能生成包含历史虚无主义倾向的大量案例。这一典型场景揭示了人工智能赋能思想政治教育时面临的价值导向偏离风险——当技术逻辑遭遇意识形态传播的复杂性，算法幻觉正在引发意识形态安全危机。从技术哲学视角审视，人工智能系统的价值编码具有双重异化特性。一方面，预训练模型通过海量数据吸收的价值立场，往往与社会主义核心价值观存在隐性冲突；另一方面，算法黑箱导致的价值输出不可控性，使得技术系统可能成为西方价值渗透的“特洛伊木马”。这种价值偏离机制印证了技术统治论的核心论断：工具理性正在侵蚀教育场域的价值理性基础。知识社会学研究揭示了更隐蔽的认知重构危机，即人工智能教育系统通过知识图谱构建的“信息茧房”、对话机器人设置的话语框架，实质上重塑着学习者的价值判断范式，并使其呈现出价值认知碎片化趋势。这种算法中介化的意识形态传播，导致福柯所言“话语权力”从教育主体向技术系统的隐秘转移。当前思想政治教育人工智能系统暴露的幻觉现象，本质上是算法黑箱与教育规律深层冲突的外化表现。

(IV) Deviations in Interaction and Application Scenarios

（四）交互与应用场景的偏差

1. Receiving Vague Instructions Leads to Output Inaccuracy

1.接收模糊指令引发输出失准

Due to users’ limited expressive ability or insufficient understanding of the issue, they may issue vague instructions to AI, or there may be implicit other intentions. In the practice of AI-empowered ideological and political education, output deviations triggered by vague instructions are forming deep educational risks. When a teacher inputs a broad instruction such as “explain the basic principles of Marxism,” the AI system often falls into the predicament of “concept collage,” simplifying “practice is the sole criterion for testing truth” into a linear arrangement of knowledge points, severing its organic connection with the historical context of reform and opening up, resulting in theoretical logical fractures in the generated teaching content. A more covert inaccuracy manifests in the implicit shift of value stance. When AI is asked to “compare Chinese and Western democratic systems” without specifying the methodology, the system may mechanically list institutional features while neglecting the essential attributes of socialist democratic politics, and may even exhibit the infiltration of Western discourse prioritizing “procedural democracy.” Moreover, this output deviation has transcended the category of technical defects and evolved into a trigger for a crisis of educational subjectivity. Teachers who rely long-term on AI-generated content from vague instructions will gradually lose their original creativity in theoretical interpretation, and their ability to guide values in classroom teaching will subsequently decline.

由于用户自身表达能力有限或对问题的理解不够深入，可能会向人工智能发出模糊不清的指令，或者存在隐含的其他意图。在人工智能赋能思想政治教育的实践中，模糊指令引发的输出偏差正在形成深层的教育风险。当教师输入“讲解马克思主义基本原理”这类宽泛指令时，人工智能系统往往陷入“概念拼贴”的困境，将“实践是检验真理的唯一标准”简化为知识点的线性排列，割裂其与改革开放历史语境的有机联系，导致生成的教案内容存在理论逻辑断裂。更隐蔽的失准表现在价值立场的隐性偏移上，当要求人工智能“比较中西民主制度”而未明确方法论时，系统可能机械罗列制度特征，却忽视社会主义民主政治的本质属性，甚至出现“程序民主优先”的西方话语渗透。而且，这种输出偏差已超越技术缺陷范畴，还会演变为教育主体性危机的导火索，长期依赖人工智能生成模糊指令内容的教师群体便会逐渐丧失理论阐释的原创力，其课堂教学的价值引导力也会随之下降。

2. Changes in Application Scenarios Lead to Adaptation Difficulties

2.应用场景变化引致适配难题

In the process of AI-empowered ideological and political education, the dynamic migration of application scenarios is giving rise to new types of technological adaptation dilemmas. When AI shifts from standardized classroom knowledge transmission to immersive teaching scenarios blending the virtual and the real, the lag in the technological system’s understanding of educational laws is sharply amplified. For example, in a red VR education scenario, a certain platform’s AI misinterprets a revolutionary’s firm body language as “high emotional fluctuation index,” leading some experiencers to form cognitive biases regarding the historical figure’s personality. This adaptation crisis is essentially a conflict between technological instrumentality and educational humanity: algorithms attempt to capture the dynamically developing needs of values education with fixed parameters, while neglecting the differences in cognitive construction across different scenarios. In the application of online discussion communities, AI frequently misjudges metaphorical expressions and youth subcultural symbols, often categorizing critical discussions of terms like “lying flat” [8]8 “Lying flat” (躺平, tangping): A Chinese internet slang term describing a lifestyle of minimal effort and withdrawal from societal pressures, often seen as a form of passive resistance to competitive social norms. simply as negative emotions, thereby blocking the possibility of ideological guidance through dialogue. A more severe challenge lies in the distortion of value transmission during cross-scenario migration. For instance, an AI-written rural research report transforms the issue of left-behind children’s education into a technical proposition of “insufficient home-school interaction frequency,” obscuring the essence of urban-rural structural contradictions. This failure of scenario adaptation serves as a warning that technological empowerment must be based on a profound grasp of the political attributes of educational scenarios.

人工智能赋能思想政治教育的过程中，应用场景的动态迁移正催生新型技术适配困境。当人工智能从标准化的课堂知识传授转向虚实融合的沉浸式教学场景时，技术系统对教育规律的理解滞后性被急剧放大。例如在红色VR教育场景中，某平台人工智能将革命者坚定的肢体语言误读为“情绪波动指数偏高”，导致一些体验者形成对历史人物性格的认知偏差。这种适配危机本质上是技术工具性与教育人文性的冲突：算法试图用固定参数捕捉动态发展的价值观教育需求，却忽视不同场景中认知建构的差异性。在在线讨论社区的应用中，人工智能对隐喻表达和青年亚文化符号的误判也时常出现，常将对“躺平”等话语的批判性讨论简单归类为负面情绪，阻断思想引导的对话可能。更严峻的挑战在于跨场景迁移中的价值传递失真，如使用人工智能撰写的乡村调研报告，将留守儿童教育问题转化为“家校互动频率不足”的技术命题，遮蔽了城乡结构矛盾的本质。这种场景适配的失能，警示着技术赋能必须建立在对教育场景政治属性的深刻把握之上。

(V) Shortcomings in Logical Reasoning and Abstract Ability

（五）逻辑推理与抽象能力的短板

1. Fractured Logical Chains Induce Absurd Conclusions

1.逻辑链条断裂诱发结论荒诞

AI performs reasoning by constructing logical chains. If there is inconsistency in the reasoning process, such as a lack of reasonable correlation between premises and conclusions, or jumps and contradictions between reasoning steps, the results will deviate from the actual situation, producing hallucinations. For example, when analyzing causal relationships between events, it erroneously treats temporal succession as a causal relationship, leading to unreasonable conclusions. The fracture of logical chains is giving rise to an alarming phenomenon of conclusion alienation. When the technological system disassembles the complete proposition “Party leadership is the most essential feature of socialism with Chinese characteristics” into isolated parameters like “leadership index” and “institutional effectiveness parameter” for quantitative analysis, the internal logic of theoretical interpretation is quietly dismembered by the algorithm. This logical collapse may evolve into absurd knowledge collage in educational practice, leading to value misalignments across time and space. A more covert risk lies in implicit logical substitution: when algorithms replace “causal argumentation” with “data correlation,” the “superiority of socialism” may be reduced to a single-dimensional interpretation of the economic growth curve, obscuring the composite logical foundation of institutional advantages. This logical deconstruction not only undermines the theoretical depth of ideological and political education but also, under the guise of technological rationality, quietly shakes the cognitive foundation of values education.

人工智能通过构建逻辑链条进行推理，如果在推理过程中出现前后逻辑不一致，比如前提与结论之间缺乏合理关联，或者推理步骤之间存在跳跃、矛盾，就会导致其得出的结果与实际情况不符，产生幻觉。如在分析事件因果关系时，错误地将时间先后关系当作因果关系，得出不合理结论。逻辑链条的断裂正催生令人警惕的结论异化现象。当技术系统将“党的领导是中国特色社会主义最本质特征”这一完整命题，拆解为孤立的“领导力指数”“制度效能参数”进行量化分析时，理论阐释的内在逻辑将会被算法悄然肢解。这种逻辑塌陷在教育实践中有可能演变为荒诞的知识拼贴，导致出现跨越时空的价值错位。更隐蔽的风险在于隐性逻辑替代：当算法用“数据关联”替代“因果论证”时，“社会主义优越性”就可能被转化为经济增长曲线的单一维度阐释，遮蔽了制度优势的复合性逻辑根基。这种逻辑解构不仅消解了思想政治教育的理论深度，更在技术理性包装下，悄然动摇着价值观教育的认知根基。

2. Conflicts from Multiple Sources of Information Lead to Cognitive Disorder

2.多源信息冲突致使认知紊乱

In the process of integrating information for ideological and political education through artificial intelligence, differences or contradictions inevitably arise among information from diverse sources. If AI cannot effectively fuse and coordinate this information, confusion occurs during reasoning, leading to generated content that does not align with facts and giving rise to hallucination phenomena. In particular, value conflicts among multi-source information may create systemic disorder in the cognitive ecosystem. When AI systems mechanically aggregate heterogeneous information—such as mainstream ideological theory, online subcultural discourse, and Western values—the ostensibly neutral technological surface conceals a crisis of cognitive coordinate dislocation, potentially blurring the essential differences between the two value systems of core socialist values and “universal values.” When algorithms mix the苦难辉煌[9]9 苦难辉煌 (kunan huihuang): A four-character idiom meaning “hardship and glory,” often used to refer to the CPC’s history of struggle and achievement. of the Party’s century-long history with online deconstructive narratives for training, the generated content exhibits a tendency toward “entertainment-oriented history,” interpreting the spirit of the Long March as a “survival challenge reality show,” thereby completely dissolving the sublimity of revolutionary narratives. This cognitive disorder has transcended the category of information overload, evolving into a technological pathway for the loss of ideological阵地[10]10 阵地 (zhendi): Literally “position” or “battlefield,” a standard term in CPC discourse for the ideological domain or front., turning technological tools into an invisible hand that erodes the authority of mainstream values.

在人工智能整合思想政治教育信息的过程中，不同来源的信息必然存在差异或相互矛盾。若人工智能不能有效融合和协调这些信息，在推理时就会出现混乱，导致生成的内容与事实不符，出现幻觉现象。尤其是多源信息的价值冲突可能会制造认知生态的系统性紊乱。当人工智能系统将主流意识形态理论、网络亚文化话语、西方价值观念等异质信息进行机械聚合时，技术中立的表象下暗涌着认知坐标的错乱危机，有可能混淆社会主义核心价值观与“普世价值”两种价值体系的本质差异。当算法将百余年党史的苦难辉煌与网络解构主义叙事混合训练时，生成内容便会出现“娱乐化历史”的倾向，会将长征精神阐释为“极限生存挑战真人秀”，彻底消解革命叙事的崇高性。这种认知紊乱已超越信息过载范畴，演变为意识形态阵地失守的技术化路径，使得技术工具异化为消解主流价值权威性的无形推手。

III. Cognitive Misconceptions of the Subject That Amplify AI Hallucinations in Ideological and Political Education

三、思想政治教育中人工智能幻觉被放大化的主体认知误区

At the intersection of technological revolution and educational transformation, cognitive misconceptions about AI among educational subjects are becoming a key variable in the amplification of technological hallucinations. This cognitive alienation stems not only from the遮蔽效应[11]11 遮蔽效应 (zhebi xiaoying): “Shielding effect,” referring to the opacity or concealment of internal processes in AI systems. of the technological black box but also, at a deeper level, reflects the value disorientation of educational subjects in the process of technological socialization. When we shift our focus from technological defects to subject cognition, we find three intertwined cognitive traps reshaping the technological practice logic of ideological and political education.

在技术革命与教育变革的交汇点上，教育主体对人工智能的认知误区正在成为技术幻觉被放大化的关键变量。这种认知异化不仅源于技术黑箱的遮蔽效应，更深层次地折射出教育主体在技术社会化进程中的价值迷失。当我们将审视焦点从技术缺陷转向主体认知时，会发现三个相互交织的认知陷阱正在重塑思想政治教育的技术实践逻辑。

(1) Excessive Deification of AI Technology as “Omniscient and Omnipotent” by Subjects of Ideological and Political Education

（一）思想政治教育主体对人工智能技术“全知全能”的过度神化

Swept up by the tide of technological utopianism, educational subjects are falling into a cognitive delirium regarding AI. This formation of technological fetishism is essentially a cognitive reduction of the nature of technology—transforming AI systems, which possess instrumental attributes, into the ultimate arbiters of value truth. For instance, some educators believe that “around 2050, AI will comprehensively surpass human intelligence”; meanwhile, some educatees regard AI as possessing knowledge reserves beyond human capacity, and others directly equate AI-generated content with verified truth. This cognitive dislocation may, in the practice of ideological and political education, evolve into a dangerous transfer of authority: for example, directly using AI-generated theoretical explanatory texts as standard teaching answers, leading to ideological deviations in the interpretation of core concepts.

在技术乌托邦思潮的裹挟下，教育主体正陷入对人工智能的认知谵妄。这种技术拜物教的形成，本质上是对技术本质的认知降维——将具有工具属性的人工智能系统异化为价值真理的终极裁判。如有教育者认为“2050年左右，AI将全面超越人类智能”；同时，一些受教育者认为人工智能具有超越人类的知识储备，还有一些受教育者则将人工智能生成内容直接等同于经过验证的真理。这种认知错位在思想政治教育的实践中极有可能会演变为危险的权威让渡：如直接将人工智能生成的理论阐释文本作为教学标准答案，而导致核心概念的解释出现意识形态偏差。

The cognitive mechanism of technological deification stems from three misjudgments: First, equating data volume with knowledge depth. When educators, neglecting the unique value-construction process of ideological and political education, entrust the theoretical explanation of “whole-process people’s democracy” to AI, the system may output tens of thousands of words of seemingly rigorous discourse, but its internal logic severs the dialectical relationship among the Party’s leadership, institutional advantages, and the mass line. Second, confusing algorithmic efficiency with educational effectiveness. Although using AI to generate real-time classroom Q&A significantly increases the frequency of teacher-student interaction, the depth index of questions may actually decline. Third, mistaking technological iteration for cognitive evolution. When educators blindly推崇[12]12 推崇 (tuichong): “To hold in high esteem” or “to advocate,” here used critically to mean uncritical promotion. large models like ChatGPT, they often overlook their essential defect of value neutrality. This cognitive alienation forms a vicious cycle in intergenerational transmission: a large number of young students regard AI as a “mobile library of Marxist theory,” and their time spent independently studying classic works has markedly decreased.

技术神化的认知机制源于三重误判：其一，将数据储量等同于知识深度。当教育者忽视思想政治教育特有的价值建构过程，将“全过程人民民主”的理论阐释委托给人工智能时，系统可能输出数万字看似严谨的论述，但其内在逻辑却割裂了党的领导、制度优势与群众路线的辩证关系。其二，混淆了算法效率与教育效能。使用人工智能即时生成课堂问答虽使师生互动频率显著提升，但问题深度指数可能反而下降。其三，错把技术迭代当作认知进化。当教育者盲目推崇ChatGPT等大模型时，往往忽视其价值中立的本质缺陷。这种认知异化在代际传递中形成恶性循环：大量青年学生将人工智能视为“移动的马克思主义理论库”，自主研读经典著作的时间明显减少。

The ultimate danger of technological deification lies in dissolving the value-judgment ability of educational subjects. For example, when AI interprets “socialist market economy” as an “efficiency-first mixed economic model,” educatees fail to detect the neoliberal tendency in time. This cognitive crisis warns us: when educators cede the power of theoretical interpretation to algorithms, ideological and political education risks becoming a appendage of technology.

技术神化的终极危险在于消解教育主体的价值判断能力。如人工智能将“社会主义市场经济”解释为“效率优先的混合经济模式”时，受教育者却未能及时察觉其中的新自由主义倾向。这种认知危机警示我们：当教育者将理论阐释权让渡给算法，思想政治教育就面临沦为技术附庸的风险。

(2) Technological Dependence Caused by the Expansion of Technological Rationality Among Subjects of Ideological and Political Education

（二）思想政治教育主体的技术理性膨胀所导致的技术依赖

Under the dominance of instrumental rationality, educational subjects are undergoing a cognitive transformation from technology use to technology dependence. This process of alienation follows an evolutionary path of “efficiency temptation—capability degradation—subject dissolution,” ultimately reducing educators to operational terminals of the technological system. Teachers who over-rely on AI for lesson preparation gradually lose the ability to independently design teaching plans, and their classroom innovation capacity inevitably declines.

在工具理性支配下，教育主体正经历着从技术使用到技术依赖的认知蜕变。这种异化过程遵循着“效率诱惑—能力退化—主体消解”的演化路径，最终导致教育者沦为技术系统的操作终端。过度依赖人工智能备课的教师，便会逐渐丧失独立设计教学方案的能力，其课堂创新能力必然会下降。

The pathological features of this dependence syndrome manifest in three dimensions. First, technological dependence reconstructs the spatiotemporal structure of educational cognition. When real-time generation of teaching resources by AI becomes the norm, teachers’ knowledge reserves shift from diachronic accumulation to instant retrieval, ultimately leading to a gradual decline in the completeness of the Marxist theoretical system mastered by educators and a corresponding decline in their ability to understand the Party’s innovative theoretical system. Second, technological tools are alienated into educational ends. For example, technological dependence leads teachers to equate optimization of technical parameters with improvement in teaching quality, falling into digital myths such as “click-rate worship” and “interaction-frequency competition.” Third, technological dependence results in the dual dissolution of educational subjectivity. Educators are reduced to data inputters, educatees degenerate into information receivers, and the unique intersubjective dialogue of ideological and political education is simplified into a human-machine interaction technical process.

这种依赖症候群的病理特征体现在三个维度。首先，技术依赖重构了教育认知的时空结构。当人工智能实时生成教学资源成为常态，教师的知识储备便会从历时性积累转向即时性索取，最终会使得教育者掌握的马克思主义理论体系的完整度逐步衰减，对党的创新理论体系的理解能力逐步下降。其次，技术工具异化为教育目的。如技术依赖会使得教师将技术参数优化等同于教学质量提升，陷入“点击量崇拜”“互动频率竞赛”等数字化迷思。最后，技术依赖导致教育主体性的双重消解。教育者沦为数据输入员，受教育者退化为信息接收器，思想政治教育特有的主体间性对话被简化为人机交互的技术流程。

This cognitive alienation, with its typical symptoms, triggers three outcomes: First, theoretical interpretation loses depth. For instance, in AI-generated teaching cases, discussions on “common prosperity” dwell at length on the level of income distribution, failing to touch upon the essential attributes of socialism. Second, value guidance loses warmth. For example, AI-based heart-to-heart talk systems using affective computing process students’ ideological perplexities into several emotional labels but cannot understand the value anxieties of the “post-00s” youth. Third, educational practice loses innovation. For instance, in virtual simulation laboratories, the spirit of the Long March is deconstructed into several task levels; while student pass rates achieve full marks, the index of emotional resonance may decline compared to traditional practical teaching.

这种认知异化的典型症候引发了三重结果：一是理论阐释失去深度。如人工智能生成的教学案例中，涉及“共同富裕”的讨论大篇幅停留在收入分配层面，未能触及社会主义本质属性。二是价值引导失去温度。如基于情感计算的人工智能谈心系统，将学生的思想困惑处理为若干个情绪标签，却无法理解“00后”青年的价值焦虑。三是教育实践失去创新。如虚拟仿真实验室中，长征精神被解构为若干个任务关卡，学生通关率实现了全能，但情感共鸣指数相较于传统实践教学却可能有所下降。

(3) Subjects of Ideological and Political Education Indulging in the “Perfect Integration” of Human-Machine Collaboration

（三）思想政治教育主体沉醉于人机协同的“完美融合”

Within the romantic imagination of human-machine collaboration, educational subjects are constructing a dangerous cognitive schema of “technological frictionlessness.” This cognitive utopia regards technological intervention as an inevitable choice for educational evolution but deliberately ignores the essential differences and inherent tensions between human and machine systems. For instance, many educators believe that “AI can be perfectly integrated into the entire process of ideological and political education,” and some schools list “degree of human-machine collaboration” as a core indicator for evaluating teaching quality.

在人机协同的浪漫想象中，教育主体正在建构着危险的“技术无摩擦”认知图式。这种认知乌托邦将技术介入视为教育进化的必然选择，却刻意忽视人机系统的本质差异与内在张力。如很多教育者认为“人工智能可以完美融入思想政治教育全流程”，一些学校将“人机协同度”列为评价教学质量的核心指标。

This cognitive bias will, in practice, create three illusions. First, the illusion of role complementarity. Educators envision AI undertaking “low-level” tasks such as knowledge transmission, while educators focus on “high-level” tasks such as value guidance. But in fact, among teacher groups who use AI for knowledge explanation over the long term, their value-guidance ability does not rise but falls—because of the lack of emotional interaction and dialectical explanation in the knowledge-transmission process, the efficiency of students’ value internalization correspondingly declines. Second, the illusion of efficiency superposition. Although AI greatly increases the amount of classroom information, the completeness of students’ theoretical system construction shows a trend of not rising but falling, along with the coexistence of knowledge fragmentation and value suspension. Third, the illusion of evolutionary symbiosis. Some educators expect to achieve co-evolution through continuous human-machine interaction, but in fact, teachers who over-rely on technological systems instead experience a degradation of innovation capacity. The essence of this cognitive misconception is to simplify the complexity of education into a problem of technological adaptability. This also warns us: human-machine collaboration is by no means a simple functional complementarity but a value game involving the essence of education.

这种认知偏差在实践中将会制造出三重幻象。其一，角色互补幻象。教育者设想人工智能承担知识传授等“低级”工作，教育者则专注于价值引领等“高级”任务。但事实上，长期使用人工智能进行知识讲解的教师群体，其价值引导能力不升反降——因缺乏知识传递过程中的情感互动与辩证阐释，学生的价值观内化效率也会相应下降。其二，效能叠加幻象。虽然人工智能使课堂信息量大幅提升，但学生的理论体系建构完整度却会呈现不升反降的趋势，以及知识碎片化与价值悬浮化并存的特征。其三，进化共生幻象。一些教育者期待通过持续的人机交互实现协同进化，但事实上，过度依赖技术系统的教师反而会出现创新能力的退化。这种认知误区的本质，是将教育复杂性简化为技术适配性问题。这也警示我们：人机协同绝非简单的功能互补，而是涉及教育本质的价值博弈。

IV. Pathways to Eliminate AI Hallucinations in Ideological and Political Education

四、思想政治教育中人工智能幻觉的消弭路径

The deep integration of artificial intelligence with ideological and political education must transcend the paradigm of technological instrumentalism and, under the guidance of the Marxist view of technology, construct a value-oriented development path. By establishing a four-dimensional governance system of “data-model governance—algorithm auditing—cognitive revolution—institutional guarantees,” the dialectical unity of technological empowerment and value leadership can be achieved, providing a new paradigm for the innovative development of ideological and political education in the New Era.

人工智能与思想政治教育的深度融合必须超越技术工具主义范式，在马克思主义技术观指导下构建价值导向型发展路径。通过建立“数据模型治理—算法审计—认知革命—制度保障”四维治理体系，实现技术赋能与价值引领的辩证统一，为新时代思想政治教育创新发展提供新范式。

(I) Technical Level: Data-Model Governance

（一）技术层面：数据模型治理

1. Strengthening Data Quality Control

1.强化数据质量控制

Building a solid dam against technological hallucinations at the data source is the core of the data governance system. In the context of ideological and political education, data quality control has a dual nature: it is both a guarantee mechanism for technical precision and a protective barrier for ideological security.

从数据源头上筑牢防范技术幻觉的堤坝是数据治理体系的核心。在思想政治教育场景中，数据质量控制具有双重属性，既是技术精度的保障机制，更是意识形态安全的守护屏障。

Faced with data accuracy issues such as the fragmentation of core theoretical data, homogenization of practical case data, and ambiguity of value-oriented data encountered by AI platform enterprises, establishing a precise data screening mechanism has become an urgent task. Technically, data purification can be achieved by constructing a five-layer filtering model: the first layer screens out non-authoritative sources, excluding low-quality data such as online forum data, explicitly labeled AI-generated data, and obviously contaminated data; the second layer conducts ideological compliance testing to identify potential value deviations; the third layer implements theoretical integrity verification to ensure the systematic nature of the basic principles of Marxism; the fourth layer carries out contextual restoration processing to preserve the spatiotemporal characteristics of the educational scenario; the fifth layer completes an educational suitability assessment to match the cognitive patterns of different educational stages.

面对人工智能平台企业所面临的核心理论数据碎片化、实践案例数据同质化、价值导向数据模糊化等数据准确性问题，建立精准化数据筛选机制成为当务之急。技术上可以通过构建五层过滤模型实现数据的提纯：第一层筛除非权威信源，排除网络论坛数据、明确标注的人工智能生成数据、明显被污染过的数据等低质量数据；第二层进行意识形态合规检测，识别潜在价值偏差；第三层实施理论完整性验证，确保马克思主义基本原理的系统性；第四层开展语境还原处理，保留教育场景的时空特征；第五层完成教育适配度评估，匹配不同学段认知规律。

The “quantity hallucination” caused by insufficient data diversity can be addressed through data expansion. In the temporal dimension, establish a diachronic database of the history of Marxist theory development; in the spatial dimension, collect characteristic practical cases from various regions across the country; in the morphological dimension, integrate various educational media such as texts, images, and physical objects. By establishing a three-dimensional data ecosystem, the AI model can have data to rely on.

数据多样性不足引发的“数量幻觉”，可以通过数据扩容来解决。在时间维度上，建立马克思主义理论发展史的历时性数据库；在空间维度上，采集全国各地区的特色实践案例；在形态维度上，整合文本、影像、实物等各类教育媒介，通过建立立体化数据生态使人工智能模型有数据可依。

2. Enhancing Model Comprehension Capabilities

2.增强模型理解能力

Breaking through the formal imitation of the laws of ideological and political education by algorithms requires establishing a value-oriented cognitive enhancement framework. When processing composite concepts of Marxist theory, traditional AI models often fall into the predicament of “semantic dimensionality reduction”—simplifying rich theoretical connotations into word co-occurrence relationships. To this end, by constructing knowledge networks and mind maps that include core Marxist concepts and the Marxist theoretical system, the model can be guided to establish logical associations of political discourse.

突破算法对思想政治教育规律的形式化模仿，需要建立价值导向的认知增强框架。传统人工智能模型在处理马克思主义理论的复合概念时，往往陷入“语义降维”困境——将丰富理论内涵简化为词语共现关系。为此，可以通过构建包含马克思主义核心概念、马克思主义理论体系的知识网络和思维导图等，引导模型建立政治话语的逻辑关联。

Strengthening contextual comprehension capabilities is key to eliminating technological hallucinations. Through a three-layer architecture of the AI model, the model’s sense of historical positioning can be enhanced: the first layer embeds era-specific feature tags to automatically identify the historical context of the text; the second layer constructs event causal chains to restore the practical logic of theoretical development; the third layer implants a value analysis dimension to ensure the value consistency of interpretations.

语境理解能力的强化是消除技术幻觉的关键。通过人工智能模型的三层架构提升模型的历史方位感，第一层嵌入时代特征标签，自动识别文本的历史语境；第二层构建事件因果链，还原理论发展的实践逻辑；第三层植入价值观分析维度，确保阐释的价值一致性。

Cultivating value judgment capabilities requires breaking through the cognitive myth of algorithmic neutrality. Through a “value alignment” training framework, the core socialist values can be refined into several evaluation dimensions. By using reinforcement learning to guide the model in establishing value priorities, the model’s value judgment capabilities can be enhanced.

价值判断能力的培养需要突破算法中立的认知迷思。可以通过“价值观对齐”训练框架，将社会主义核心价值观细化为若干个评估维度，通过强化学习引导模型建立价值优先级，以此增强模型的价值判断能力。

3. Developing Multimodal AI Models

3.开发多模态人工智能模型

The technical limitations of a single modality are particularly prominent in the context of ideological and political education. Taking the promotion of the Yan’an Spirit as an example, when the model processes only textual data, its understanding of the Yan’an Spirit lacks the concrete support of visual symbols, and its interpretation of the text of the *Yellow River Cantata* lacks the emotional transmission of auditory shock. This limitation can be overcome by constructing a three-layer fusion architecture of a “red multimodal large model”: the bottom layer establishes a cross-modal alignment space to achieve semantic mapping of text, images, audio, and video; the middle layer builds a situational understanding module to restore the multidimensional context of historical events; the top layer develops a value reinforcement network to ensure the value consistency of multimodal expressions.

单一模态的技术局限在思想政治教育场景中尤为凸显。以弘扬延安精神为例，当模型仅处理文本数据时，对延安精神的理解缺失视觉符号的具象支撑，对《黄河大合唱》文本的阐释缺乏听觉震撼的情感传递。通过构建“红色多模态大模型”的三层融合架构可以突破此局限，即底层建立跨模态对齐空间，实现图文音视频的语义映射；中间层构建情境理解模块，还原历史事件的多维语境；顶层开发价值强化网络，确保多模态表达的价值一致性。

Of course, the deep integration of multimodal technologies also faces some challenges. First, the problem of cross-modal semantic gaps requires the development of alignment algorithms with ideological sensitivity. Second, the difficulty of dynamic adaptation to educational scenarios requires the model to perceive the modal needs of the teaching environment in real time. Third, the risk of computational ethics necessitates the establishment of a value review mechanism for multimodal outputs. However, as long as the unity of instrumental rationality and value rationality is upheld, and the educational task of “striving to cultivate more new-era talents who reassure the Party, love the country, contribute to society, and shoulder the heavy responsibility of national rejuvenation” is consistently taken as the value coordinate of technological innovation, AI can truly become an enabler rather than a deconstructor of ideological and political education in the New Era.

当然，多模态技术的深度融合也面临一些挑战。其一，跨模态语义鸿沟问题，需开发具有意识形态敏感性的对齐算法。其二，教育场景的动态适配难题，要求模型实时感知教学环境的模态需求。其三，计算伦理风险，需建立多模态输出的价值观审查机制。但是，只要坚持工具理性与价值理性的统一，始终将“努力培养更多让党放心、爱国奉献、担当民族复兴重任的时代新人”的教育任务作为技术创新的价值坐标，人工智能就能真正成为新时代思想政治教育的赋能者而非解构者。

(II) Management Level: Algorithm Auditing

（二）管理层面：算法审计

1. Implementing Manual Review and Intervention

1.实施人工审核与干预

In ideologically sensitive fields, manual review is not a technical auxiliary means but a necessary line of defense for maintaining educational leadership. Sole reliance on algorithmic review can lead to missed detection of ideologically deviant content. Therefore, adopting a dual-track review system is extremely necessary: primary review is completed by AI for keyword screening and semantic early warning, while advanced review is carried out by expert-led institutions for value judgment. When implementing manual intervention, the timeliness is particularly critical. Through a three-tier response mechanism, the intervention node can be moved forward: first-tier intervention occurs during the algorithm training phase, where experts in ideological and political education can be invited to directly participate in related engineering design; second-tier intervention occurs during the model deployment phase, establishing a dynamic monitoring system for the teaching scenario; third-tier intervention occurs during the content generation phase, setting a “red emergency stop button” to achieve real-time blocking of abnormal outputs.

在意识形态敏感领域，人工审核不是技术辅助手段，而是维护教育主导权的必要防线。单纯依赖算法审核会导致意识形态偏差内容漏检，因此，采取双轨审核体系极为必要，即初级审核由人工智能完成关键词筛查与语义预警，高级审核则通过专家组成的机构实施价值研判。在实施人工干预时，时效性的把握尤为关键。可以通过三阶响应机制将干预节点前置，即一阶干预在算法训练阶段，可以邀请思想政治教育相关专家直接参与相关工程设计；二阶干预在模型部署阶段，建立教学场景的动态监测系统；三阶干预在内容生成阶段，设置“红色急停按钮”实现异常输出的实时阻断。

2. Introducing Official Data and Expert Knowledge

2.引入官方数据和专家知识

The injection of authoritative knowledge is a key path to cracking the “value suspension” of algorithms. By implementing the “Red Knowledge Embedding Project,” an official data pool containing three major knowledge modules can be constructed under state leadership: the Marxist classic literature database, the Party’s innovative theory database, and the database of practical cases with Chinese characteristics. Additionally, through expert knowledge distillation technology, the problem-elaboration process of authoritative theoretical experts can be systematically recorded and organized, extracting several value judgment rules to inject into the algorithm, successfully correcting common cognitive biases of the model in the field of Marxist theory. Given the practical difficulties of knowledge transformation barriers, establishing an expert collaboration mechanism is extremely necessary. Expert knowledge can be deconstructed into four computable dimensions: conceptual networks, logical paradigms, value coordinates, and contextual rules, among which the priority rules of the core socialist values are clearly set to effectively enhance value consistency.

权威知识注入是破解算法“价值悬浮”的关键路径。通过实施“红色知识嵌入工程”，由国家主导构建包含三大知识模块的官方数据池，即马克思主义经典文献库、党的创新理论数据库、中国特色实践案例库。还可以通过专家知识蒸馏技术，系统记录整理权威理论专家的问题阐释过程，提炼出若干个价值判断规则注入算法，成功矫正模型在马克思主义理论领域常见的认知偏差。鉴于知识转化壁垒的现实困境，建立专家协同机制极为必要。可以将专家知识解构为四个可计算维度：概念网络、逻辑范式、价值坐标和语境规则，其中明确设定社会主义核心价值观的优先级规则，以有效提升价值一致性。

3. Regularly Updating AI Models

3.定期更新人工智能模型

The dynamic evolution of educational contexts requires establishing a “biological clock mechanism” for model updates. A four-dimensional update system can be established to achieve real-time updates: in the temporal dimension, set quarterly mandatory update nodes; in the spatial dimension, continuously collect new practical samples from various regions; in the theoretical dimension, track the development of the Party’s innovative theories; in the technical dimension, integrate the latest algorithmic breakthroughs. Through real-time updates, the timeliness of AI model interpretations can be comprehensively enhanced. At the same time, update quality needs to be guaranteed by establishing a verification mechanism. For example, by establishing a “red-blue adversarial test” to simulate ideological game scenarios, the red team inputs the Party’s innovative theories, and the blue team designs test cases for Western value infiltration. This can successfully identify new discourse variants in Western ideological infiltration, promote the upgrade of model defense strategies, and effectively enhance the capability to defend against ideological risks.

教育语境的动态演进要求建立模型更新的“生物钟机制”。可以通过建立四维更新体系实现实时更新，即在时间维度设置季度强制更新节点，在空间维度持续采集各地区实践新样本，在理论维度跟踪党的创新理论发展，在技术维度融合最新算法突破。通过实时更新，全面提升人工智能模型阐释的时效性。与此同时，更新质量需要通过建立验证机制来保障，如可以通过建立“红蓝对抗测试”模拟意识形态博弈场景，由红方团队输入党的创新理论，蓝方团队设计西方价值渗透测试事例。借此成功识别出西方意识形态渗透中的新型话术变体，推动模型防御策略的升级，有效提升意识形态风险防御能力。

4. Improving the Transparency and Explainability of AI Models

4.提高人工智能模型的透明性与可解释性

Cracking the algorithm black box is a foundational project for rebuilding educational trust. A three-layer explanation framework for a decision traceability system can be established to achieve algorithm transparency: the first layer displays the knowledge sources of content generation, even down to specific literature paragraphs; the second layer displays a visualized value judgment path, presenting the weight distribution of values one by one; the third layer reveals potential cognitive biases, marking major category risk warning labels one by one.

破解算法黑箱是重建教育信任的基础工程。通过建立决策溯源系统的三层解释框架以实现算法的透明化：第一层是展示内容生成的知识来源，甚至精确到文献段落；第二层是展示可视化价值判断路径，逐一呈现价值观权重分布；第三层是揭示潜在认知偏差，逐一标注大类风险预警标记。

Deepening explainability technology requires overcoming the difficulty of visualizing value reasoning. By developing red decision tree technology, the generation process of AI can be transformed into an explanation path consistent with Marxist methodology. For example, from problem definition, contradiction analysis, historical positioning, theoretical interpretation, to value judgment, each node is linked to theoretical bases and practical cases. This can successfully trace the model’s deviation path and promote algorithm optimization.

可解释性技术的深化需要攻克价值推理可视化的难题。可以通过开发红色决策树技术，将人工智能的生成过程转化为符合马克思主义方法论的解释路径，如从问题界定、矛盾分析、历史定位、理论阐释到价值判断，每个节点都关联理论依据与实践案例，借此可以成功追溯模型的偏差路径，推动算法的优化。

(III) Educational Dimension: Cognitive Revolution

（三）教育层面：认知革命

1. Correctly Understanding the Essence of Artificial Intelligence Technology

1.正确认识人工智能技术本质

The deep transformation of ideological and political education empowered by artificial intelligence is essentially a digital transformation of the educational cognitive paradigm. When technological tools reshape the basic logic of knowledge production and value transmission, educational subjects urgently need to reconstruct the coordinate system of education in the technological era through a cognitive revolution. Of course, dispelling the fog of technological cognition is the ideological prerequisite for preventing educational alienation. The widespread "technological neutrality thesis" and "algorithmic omnipotence thesis" in the current educational field are in fact a metaphysicalization of technological cognition. The root of this cognitive deviation lies in the neglect of the social nature of technology—when educators overlook the logic of capital, cultural hegemony, and technological politics implicit in the training data of artificial intelligence, they essentially allow the penetration of technological power into the ideological domain.

人工智能赋能思想政治教育的深层变革，本质上是教育认知范式的数字化转型。当技术工具重塑知识生产与价值传递的基本逻辑时，教育主体亟待通过认知革命重建技术时代的育人坐标系。当然，破除技术认知迷雾是防范教育异化的思想前提。当前教育场域中普遍存在的“技术中立论”与“算法万能论”，实则是技术认知的形而上学化。这种认知偏差的根源在于对技术社会性的忽视——当教育者忽视人工智能训练数据中隐含的资本逻辑、文化霸权与技术政治时，实质上放任了技术权力对意识形态阵地的渗透。

Under the guidance of the Marxist view of technology, the cognitive framework of the subject needs to achieve three breakthroughs. First, at the level of technological ontology, reveal the social relational attributes of artificial intelligence systems. By analyzing the capital control mechanisms of recommendation algorithms and the Western discursive hegemony of natural language processing, enable educational subjects to recognize the essence that "algorithms are politics." Second, at the level of technological axiology, critique the technological myth of "value neutrality." Through training educational subjects in critical thinking about technology, enhance their ability to identify value biases in artificial intelligence content. Third, at the level of technological methodology, establish the educational stance of "technology for people." Through training educators in the dialectical relationship between "technological instrumentality" and "educational subjectivity," enable educational subjects to truly cultivate the conscious awareness of "using technology rather than being used by technology." Fourth, at the level of technological praxis, enhance the technological reflective capacity of educational subjects. By deconstructing technological myths, analyze the limited game space behind AlphaGo's victory over human chess players; by revealing technological power, dissect how social algorithms shape youth values; by exploring technological transcendence, discuss effective pathways for human-machine collaboration.

在马克思主义技术观的指导下，主体认知框架需要实现三重突破。其一，在技术本体论层面，揭示人工智能系统的社会关系属性，通过解析推荐算法的资本控制机制、自然语言处理的西方话语霸权，使教育主体认识到“算法即政治”的本质。其二，在技术价值论层面，批判“价值中立”的技术神话，通过教育主体接受技术批判思维训练，提升对人工智能内容价值偏差的识别能力。其三，在技术方法论层面，确立“技术为人”的教育立场。通过对教育者开展“技术工具性”与“教育主体性”的辩证关系培训，使教育主体真正树立“用技术而非被技术所用”的自觉意识。其四，在技术实践论层面，提升教育主体的技术反思能力。通过解构技术神话，分析AlphaGo战胜人类棋手背后的有限博弈空间；通过揭示技术权力，解剖社交算法如何塑造青年价值观；通过探索技术超越，研讨人机协同的有效路径。

2. Enhancing the Artificial Intelligence Literacy of Educators and Educands

2.提升教育者与受教育者的人工智能素养

The current structural deficiency in artificial intelligence literacy education is prominently manifested as the disconnection between skills training and value education, the disconnection between knowledge transmission and critical thinking, and the disconnection between theoretical learning and practical innovation. Therefore, cultivation should not stop at operational skills training but should construct a three-dimensional literacy system of "technological cognition—value judgment—innovative application."

当前人工智能素养教育的结构性缺陷突出表现为技能培训与价值教育脱节、知识传授与批判思维脱节、理论学习与实践创新脱节。因此，培育不应止步于操作技能训练，而应构建“技术认知—价值判断—创新应用”的三维素养体系。

Constructing an artificial intelligence literacy education system with Chinese characteristics requires consolidating four pillars. First, a tiered education system. The basic education stage focuses on technological cognition and information security; the higher education stage emphasizes value critique and innovative application; the teacher development level carries out leadership training in artificial intelligence education. Second, curriculum integration and innovation. By offering courses on artificial intelligence ethics, embed technological critique into professional teaching; in the "Basic Principles of Marxism" course, analyze the capital logic of recommendation algorithms; in the "Ideological Morality and Rule of Law" course, discuss the ethical boundaries of affective computing. Third, practical empowerment platforms. Reshape literacy education through practical scenarios, such as deconstructing the value judgment mechanism of the ChatGPT model, developing artificial intelligence assistants for ideological and political education, and conducting human-machine games on values. Fourth, evaluation reform as a driver. By establishing development archives for artificial intelligence literacy, break through traditional quantitative evaluation models, and implement process-oriented evaluation from three dimensions: depth of technological understanding, reliability of value judgment, and effectiveness of innovative application.

构建中国特色人工智能素养教育体系需要夯实四个支点。其一，分层教育体系。基础教育阶段聚焦技术认知与信息安全，高等教育阶段强化价值批判与创新应用，教师发展层面开展人工智能教育领导力培养。其二，课程融合创新。通过开设人工智能伦理课程，将技术批判嵌入专业教学；在“马克思主义基本原理”课程中解析推荐算法的资本逻辑，在“思想道德与法治”课程中讨论情感计算的伦理边界。其三，实践赋能平台。通过实践场景重塑素养教育，如拆解ChatGPT模型的价值判断机制、开发思想政治教育人工智能助手、开展价值观人机博弈等。其四，评价改革牵引。通过建立人工智能素养发展档案，突破传统量化评价模式，从技术理解深度、价值判断信度、创新应用效度三个维度实施过程性评价。

(IV) Ethical and Values Dimension: Institutional Guarantees

（四）伦理与价值观层面：制度保障

1. Embedding Ethical Rules in Algorithms

1.在算法中嵌入伦理规则

Traditional technological ethical governance often relies on post-hoc review and passive correction, but in the context of ideological and political education, ethical rules must be transformed into the underlying logic of algorithm operation. This can be achieved by establishing a multi-level governance model: at the core values layer, construct a Marxist ethical knowledge graph, transforming abstract ethical principles into several computable parameters; at the decision logic layer, develop an ethical decision tree based on dialectical materialism, requiring algorithms to pass logical checkpoints such as "historical legitimacy review," "real-world reasonableness verification," and "value justifiability test" before generating content; at the output control layer, establish a dynamic ethical review gateway, monitoring generated content in real time through neural networks. When high-risk expressions are detected, the system automatically triggers a "red circuit-breaking mechanism," blocking content dissemination and flagging it for review.

传统技术伦理治理往往依赖事后审查与被动纠偏，而在思想政治教育场景中，伦理规则必须转化为算法运行的底层逻辑。可以通过建立多层级治理模型，即在价值观内核层，构建马克思主义伦理知识图谱，将抽象伦理原则转化为若干个可计算参数；在决策逻辑层，开发基于辩证唯物主义的伦理决策树，要求算法在生成内容前必须通过“历史合法性审查”“现实合理性验证”“价值正当性检验”等逻辑关卡；在输出控制层，建立动态伦理审查网关，通过神经网络实时监测生成内容。当检测到高风险表述时，系统自动触发“红色熔断机制”，阻断内容传播并标记审查。

2. Establishing Institutional Guarantees for Guiding Mainstream Values in Algorithms

2.在算法中确立主流价值观导向的制度保障

The ideological security game in the intelligent era has extended to the algorithmic dimension. In the digital space of pluralistic value competition, artificial intelligence must become a steadfast disseminator of mainstream values rather than a passive carrier. Only by deeply embedding core socialist values into the value-coding layer of technological systems can we ensure that artificial intelligence truly becomes a strategic tool for consolidating mainstream ideology. To safeguard mainstream social values and eliminate uncontrollable risks in generated content, a "Five-Sphere Integrated Plan" [16]16 The "Five-Sphere Integrated Plan" (五位一体) refers to China's overall plan for promoting economic, political, cultural, social, and ecological progress. institutional guarantee system should be constructed. First, establish an algorithm filing system for value embedding. China's "Interim Measures for the Management of Generative Artificial Intelligence Services" explicitly requires "adherence to core socialist values." [17]17 The "Interim Measures for the Management of Generative Artificial Intelligence Services" (《生成式人工智能服务管理暂行办法》) is a Chinese regulation effective from August 15, 2023, governing the development and use of generative AI. Subsequently, all generative artificial intelligence models can be required to embed a database of core socialist values during the training phase, eliminate algorithmic bias through adversarial training, and file the core values code with regulatory authorities for review. Second, build a multi-party collaborative three-dimensional regulatory network. Establish a governance matrix composed of a technology ethics committee, third-party certification bodies, and user supervision platforms to conduct full life-cycle monitoring of artificial intelligence output content. By adopting a risk-graded regulatory model, implement a dual mechanism of "real-time content review + user warnings" for social chatbots. Third, improve the legal framework for the digital space. Clarify through legislation the responsibility of artificial intelligence service providers for disseminating values, establish a traceability mechanism for content generated by artificial intelligence; in artificial intelligence dialogues, a non-human identity must be declared, providing a legal basis for value correction. Of course, while injecting value rationality into artificial intelligence through the rule-of-law framework, institutional innovation must also consider the inclusiveness of technological development. General Secretary Xi Jinping points out, "Artificial intelligence is a strategic technology leading this round of technological revolution and industrial transformation, with a 'bellwether' effect that strongly drives spillover," and "accelerating the development of a new generation of artificial intelligence is a strategic issue concerning whether China can seize the opportunities of the new round of technological revolution and industrial transformation." [18]18 Xi Jinping, "Speech at the Ninth Collective Study Session of the 19th Politburo of the CPC Central Committee," October 31, 2018. Only by adhering to the dialectical unity of instrumental rationality and value rationality can artificial intelligence truly become a beacon of civilization benefiting humanity.

智能时代的意识形态安全博弈已延伸至算法维度，在多元价值博弈的数字空间中，人工智能必须成为主流价值观的坚定传播者而非被动载体。唯有将社会主义核心价值观深度植入技术系统的价值编码层，才能确保人工智能真正成为巩固主流意识形态的战略工具。捍卫社会主流价值观，杜绝生成内容的不可控风险，应当构建“五位一体”制度保障体系。第一，建立价值观嵌入的算法备案制度。我国《生成式人工智能服务管理暂行办法》明确要求“坚持社会主义核心价值观”。后续可以要求所有生成式人工智能模型在训练阶段内置社会主义核心价值观数据库，通过对抗性训练消除算法偏见，并将核心价值观代码向监管部门备案审查。第二，构建多方协同的立体监管网络。设立由技术伦理委员会、第三方认证机构、用户监督平台等构成的治理矩阵，对人工智能输出内容进行全生命周期监测。通过采用风险分级监管模式，对社交聊天机器人实施“实时内容审核+用户警示”双重机制。第三，完善数字空间法治框架。通过立法明确人工智能服务提供者的价值观传播责任，建立人工智能生成内容追溯机制；在人工智能对话中必须声明非人类身份，为价值观纠偏提供法律依据。当然，既要通过法治框架为人工智能注入价值理性，同时制度创新更要考量技术发展的包容性。习近平总书记指出，“人工智能是引领这一轮科技革命和产业变革的战略性技术，具有溢出带动性很强的‘头雁’效应”，“加快发展新一代人工智能是事关我国能否抓住新一轮科技革命和产业变革机遇的战略问题”。唯有坚持工具理性与价值理性的辩证统一，人工智能才能真正成为造福人类的文明之光。

Online Editor: Jing Mu

网络编辑：静穆