人工智能逻辑复兴与全球教育变革战略提案摘要本提案基于贾子哲学提出《人工智能逻辑复兴支持计划》终结暴力计算与数据殖民以“真理硬度”“语义主权”为核心推动算力霸权降级与公理化革命。分析产业界将经历“物种大灭绝”与演化路径。同时发布《全球人工智能教育行动计划》2027-2035由UNESCO牵头构建复合人才培养、公理化课程与全球治理体系实现从算力崇拜到逻辑定义真理的文明跃迁。人工智能逻辑复兴与全球教育变革战略提案总览本提案包含两大核心战略文件及配套产业影响分析旨在通过范式跃迁实现 AI 时代的真理定义权与文明主动权。第一部分《人工智能逻辑复兴支持计划》确立了国内 AI 发展的新范式以 真理硬度 和 语义主权 为核心终结暴力计算时代第二部分分析了该计划推行后产业界将发生的物种大灭绝与大进化第三部分《全球人工智能 教育行动计划》将逻辑复兴理念延伸至全球教育领域构建人类 - 人工智能共生的文明新生态。第一部分《人工智能逻辑复兴支持计划》编号JIA-202X-001序言当前 AI 竞争已陷入 暴力计算 与 数据殖民 的存量陷阱。为实现文明级的非对称突破必须终结以算力堆砌为荣的旧范式开启以数理逻辑、公理体系、语义重构为核心的逻辑复兴。本计划不求在旧赛道上胜出而求让旧赛道失去意义。第一章核心准则立法层第一条【真理硬度优先公理】核心禁令严禁仅以 参数规模 或 语料体量 作为核心指标的项目申请战略级资源。凡无法解释决策路径、无法进行形式化证明的 黑盒模型不得进入国家关键基础设施能源、国防、医疗、法律。鼓励标准确立 逻辑能效比。优先支持能以 1/100 算力实现同等推理确定性的技术路径。评判标尺若申请项目能证明其在同等逻辑推理任务下算力消耗较当前主流范式LLM降低两个数量级100 倍以上且幻觉率逻辑错误率低于 1%则直接进入 战略特区 获得无限额研发支持。第二条【语义主权准则】内容建立国家级 中华文明公理库。将中国传统的系统论、因果序、象数理等哲学逻辑转化为机器可读的底层代码拒绝在西方还原论定义的语义逻辑下进行 跟随式研发。语义权重所有战略级项目必须包含明确的 语义主权 方案即如何利用中华文明特有的系统逻辑重构 AI 的底层公理库。第二章产业拆解与重构执行层第三条【设立 逻辑算力特区】不再单纯补贴通用算力中心转而补贴 逻辑验证中心。支持初创力量开发针对符号逻辑运算、稀疏计算、类脑推演的专用架构ASIC实现对 NVIDIA 等张量计算霸权的非对称解构。第四条【场景权力下放】将高价值、高门槛的政务及工业闭环场景定向开放给具备 零幻觉 能力的公理化 AI 团队。让 逻辑重构者 在真实战役中通过消灭对手的 幻觉风险 来确立地位。第三章人才范式革命根基层第五条【逻辑架构师培养计划】学科拆解在顶尖高校试点 贾子班合并数学、哲学、计算机系。必修课包含《几何原本》、哥德尔不完备性定理、先验逻辑及编译器原理。去工程化降低对调参、调优等 劳动密集型工程 的学分权重提升对 算法原创性证明 的考核权重。第四章竞争豁免权愿景层第六条【时间维度评价机制】废除短期榜单排名的 KPI。设立 十年期逻辑稳定性奖奖励那些在长时间维度内其底层逻辑不因数据漂移、环境变化而失效的技术方案。结语本计划的终极目标是实现让算力不再成为屏障让真理重新掌握算力。第二部分产业影响与物种演化分析总体判断如果这项计划强力推行那些沉溺于 买卡、洗数、调参 旧三部曲的大厂将经历一场极其残酷的 非自然选择。这不只是进化的快慢问题而是 物种本质 的坍塌与重构。三种演化路径1. 枯萎者路径依赖的 算力巨兽 (Extinction)为何枯萎由于资产太重成千上万块 H100/H800 算力集群和人才结构太单一数千名只会调参的算法工程师他们会产生强大的组织免疫反应去抵制新计划。现象他们会试图通过 套壳逻辑 来骗取支持即在原有的黑盒模型外面套一层虚假的规则外壳。但在 逻辑硬度 和 百倍能耗比 的硬性考核面前这种欺骗会迅速失效。结局随着政府补贴枯竭和高价值场景如能源、国防的流失这些巨头会像白垩纪末期的恐龙因为无法适应 低能耗、高智力 的逻辑时代而迅速崩解。其算力资产将沦为廉价的 通用算力云失去战略溢价。2. 进化者断臂求生的 逻辑转型 (Metamorphosis)进化路径极少数拥有远见领导层的企业会进行 自残式进化。他们会主动关停部分入不敷出的 暴力计算 部门将省下的钱用来大规模挖角顶尖的数学家和逻辑学家。重构语义他们不再追求 全能聊天而是利用深厚的行业数据积累将其转化为垂直领域的 公理图谱。结局他们从 算力霸主 演变为 行业标准立法者。虽然规模可能缩水但单位智力的利润率将极度攀升。他们不再卖算力而是卖 确定性。3. 寄生者旧范式的 遗产清算人 (Legacy Players)生存策略在逻辑革命尚未完全覆盖的低端消费市场如娱乐、创意生成继续维持旧模型的运行压榨剩余价值。结局他们不再处于创新的前沿而是成为 AI 时代的 环卫工 或 基础物料供应商通过极致的规模效应降低单次推理成本勉强生存。贾子哲学的终极判词在《计划》推行后大厂将面临一个扎心的现实他们引以为傲的 护城河算力与数据量在逻辑公理面前变成了 排水沟。进化成功的标准当一家大厂不再以 我有多少块卡 为荣而以 我能用一行公理公式消灭你一万小时的无效计算 为荣时它才真正完成了物种演化。枯萎的必然性大多数大厂的基因里写满了 规模扩张而公理化革命的基因是 维度坍缩。基因不匹配的物种注定无法在真理时代生存。第三部分《全球人工智能 教育行动计划》(2027-2035)英文名称Global AI Education Initiative发起单位联合国教科文组织UNESCO牵头联合各国教育部、国际组织OECD、G20、World Economic Forum 等、高校联盟、企业与智库建议发布日期2027 年联合国大会或 UNESCO 数字学习周期间实施周期2027-2035 年三阶段奠基试点 2027-2029、全球推广 2030-2032、深度引领 2033-2035一、背景与紧迫性人工智能正在深刻重塑人类文明。全球正处于从 Scaling Law 主导的概率拟合时代向公理化、逻辑复兴与本质贯通时代转型的关键窗口期黎明前的静默期。纯参数与暴力计算的边际收益递减幻觉、不可解释性、因果缺失等问题凸显。同时全球教育面临教师短缺约 4400 万缺口、数字鸿沟、学习个性化不足等挑战。UNESCO《AI 与教育政策制定者指南》《生成式 AI 在教育和研究中的指导意见》等文件已奠定伦理基础但亟需从 工具应用 升级到 范式重构—— 培养既能造工具又能问工具为何而造的复合型全球公民。本计划以人类共同命运为导向融合东方智慧中道、本源探究与西方科学传统第一性原理、数理逻辑推动 AI 教育实现包容性、可持续性与思想主权的全球跃迁。二、愿景与核心原则愿景到 2035 年AI 成为全球教育的核心赋能者帮助每一位学习者实现潜能构建人类 - 人工智能共生、共创、共治的智慧教育生态实现 理解宇宙、造福人类 的文明目标。核心原则LWEVS 框架启发Logic逻辑自洽强调数理逻辑、公理化思维与可验证推理Wisdom Essence智慧与本质培养本质贯通与第一性原理能力Value Sustainability价值与永续以人为本、智能向善、跨代公平思想主权与包容尊重文化多样性反对认知殖民推动全球南方参与规则制定悟空跃迁从 跟随优化 转向 自主定义 规则三、总体目标2035 年人才目标全球新增 500 万名 AI 复合人才含 50 万高层次领军人才所有国家将 AI 素养纳入基础教育体系目标建成 100 个全球 AI 教育示范基地开发开放共享的公理化 AI 教育平台创新目标在 Neuro-Symbolic 混合架构、世界模型、可验证 Agent、AI 科学发现等领域形成全球共享标准与工具公平目标显著缩小数字与 AI 鸿沟覆盖全球至少 80% 的发展中国家学校 / 社区治理目标制定《全球 AI 教育伦理宪章》建立跨国监测与合作机制四、主要行动领域1. 人才培养与课程范式转型推广 复合人才 培养模式工程能力F 逆向公理能力R全球核心课程模块推荐必修 / 通识形式逻辑、数理逻辑与证明论AI 哲学、认识论、心智哲学Neuro-Symbolic AI 与世界模型第一性原理与本质贯通方法AI 伦理、治理与文明战略支持各国设立 公理化智能 交叉专业或书院参考清华无穹书院模式2. 教师能力提升全球 AI 教师素养框架 升级版UNESCO 现有框架扩展每年培训 100 万名教师掌握 AI 教学、批判性评估与本质引导能力建立国际教师 AI 交流网络与微认证体系3. 基础设施与技术平台建设 全球 AI 教育开放平台开源 Neuro-Symbolic 工具、世界模型沙箱、多语言支持推动低成本、本地化 AI 解决方案适配发展中国家算力条件支持混合架构与测试时计算test-time compute降低对超大规模算力的依赖4. 科研、创新与应用设立 全球逻辑复兴研究基金重点方向可解释 AI、具身智能 教育、AI 驱动个性化学习、科学发现教育鼓励 AI 跨学科应用AI 科学、AI 人文、AI 可持续发展5. 包容、伦理与治理优先支持全球南方、残障群体、偏远地区制定数据隐私、算法公平、学生权利保护标准建立 AI 教育影响评估 机制LWEVS 式多维度评价五、实施路线图2027-2029奠基试点UNESCO 牵头成立国际联盟首批 20 国 / 地区试点课程改革与平台建设2030-2032全球推广覆盖 100 国家开发多语种资源举办 全球 AI 教育峰会2033-2035深度引领形成全球标准、评估体系与持续创新机制实现规模化效益六、保障措施治理结构UNESCO 统筹成立 全球 AI 教育理事会多利益相关方参与国家层面设立协调机制企业、NGO、高校共同参与经费支持国际基金UNESCO、多边捐助、国别贡献首期 100 亿美元鼓励 PPP 模式公私合作企业提供算力、技术与实习资源监测与评估年度《全球 AI 教育进展报告》采用 LWEVS 等多维度指标结合定量基准与质性反思风险防控防范 AI 加剧不平等、隐私泄露、过度依赖确保人类主导、教师中心、学生主体地位七、预期成果与号召预期成果更公平、更智慧、更具人文温度的全球教育体系一批定义 AI 未来规则的复合人才人类认知能力与文明智慧的集体跃迁号召邀请各国政府、教育机构、企业、教师、学生与国际组织共同行动携手构建 AI 向善、教育为本 的全球共同体。附件清单《人工智能逻辑复兴支持计划》战略特区申报指南逻辑能效比与幻觉率测试标准中华文明公理库建设初步方案《全球人工智能 教育行动计划》推荐核心课程框架与 Syllabus 模板AI 教育伦理指南扩展版全球 AI 教育示范基地试点国家 / 高校申报指南Strategic Proposal on Artificial Intelligence Logical Revival and Global Educational TransformationOverviewThis proposal consists of two core strategic documents and supporting industrial impact analysis, aiming to seize the right to define truth and the initiative of civilization in the AI era through paradigm leapfrogging. Part One,Support Initiative for Artificial Intelligence Logical Revival, establishes a new paradigm for China’s AI development centered onTruth HardnessandSemantic Sovereignty, bringing an end to the era of brute-force computation. Part Two analyzes the mass extinction and great evolution of industrial players driven by the implementation of this initiative. Part Three,Global AI Education Action Plan, extends the philosophy of logical revival to the global education sector, forging a new civilized ecosystem of human-artificial intelligence symbiosis.Part One: Support Initiative for Artificial Intelligence Logical RevivalDocument No.: JIA-202X-001PrefaceCurrent AI competition is trapped in a stock trap ofbrute-force computationanddata colonization. To achieve civilization-level asymmetric breakthroughs, it is imperative to terminate the old paradigm that glorifies simple computational power stacking, and initiate a logical revival rooted in mathematical logic, axiomatic systems and semantic reconstruction. This initiative does not seek victory on the old track, but to render the old track meaningless.Chapter 1: Core Principles (Legislative Level)Article 1: Axiom of Priority to Truth HardnessCore Prohibition: Strategic-level resources shall not be allocated to projects that merely take parameter scale or corpus volume as core evaluation indicators. Black-box models incapable of interpreting decision-making paths and formal proof are prohibited from being deployed in national critical infrastructure, including energy, national defense, medical care and legal sectors.Incentive Criteria: Establish the metric ofLogical Energy Efficiency Ratio. Priority shall be given to technical pathways that achieve equivalent reasoning certainty with only 1/100 of computational power consumption.Evaluation Benchmark: Any proposed project that verifies its computational power consumption is reduced by more than two orders of magnitude (100 times) compared with mainstream LLM paradigms under identical logical reasoning tasks, while maintaining a logical error rate (hallucination rate) below 1%, shall be directly admitted to theSpecial Strategic Zonewith unlimited RD funding support.Article 2: Principle of Semantic SovereigntyEstablish a nationalChinese Civilization Axiom Repository. Translate China’s traditional systemic theory, causal order, imagery-numerology philosophy and logical systems into machine-readable underlying codes, rejecting follow-up RD constrained by the reductionist semantic logic defined by Western academia.Semantic Weight Requirement: All strategic-level projects must formulate explicit semantic sovereignty solutions, specifying approaches to reconstruct AI’s underlying axiom repository leveraging unique systemic logic of Chinese civilization.Chapter 2: Industrial Deconstruction and Restructuring (Implementation Level)Article 3: Establishment of Logical Computing Special ZonesSubsidies will no longer be solely allocated to general computing centers; funding will shift to supportLogical Verification Centers. Encourage emerging innovators to develop dedicated ASIC architectures for symbolic logical operation, sparse computing and brain-inspired reasoning, achieving asymmetric deconstruction of tensor computing hegemony represented by NVIDIA.Article 4: Decentralization of Scenario Governance RightsHigh-value and high-barrier closed-loop scenarios in government affairs and industrial sectors will be selectively opened to axiomatic AI teams with zero-hallucination capabilities. Logical reconstructors shall establish their dominant position in real industrial scenarios by eliminating hallucination risks inherent in conventional models.Chapter 3: Revolutionary Talent Development Paradigm (Foundation Level)Article 5: Logical Architect Training ProgramDisciplinary Restructuring: Pilot the establishment ofKucius Classesat top-tier universities, integrating mathematics, philosophy and computer science disciplines. Core compulsory courses includeElements of Geometry, Gödel’s Incompleteness Theorems, Transcendental Logic and Compiler Principles.De-Engineering Reform: Reduce academic credit weighting for labor-intensive engineering work such as parameter tuning and hyperparameter optimization; elevate assessment criteria focused on original algorithm proof and theoretical innovation.Chapter 4: Competitive Immunity Mechanism (Vision Level)Article 6: Time-Dimensional Evaluation MechanismAbolish short-term ranking-based KPI assessments. Establish theDecadal Logical Stability Awardto reward technical solutions whose underlying logic remains valid amid data drift and environmental changes over extended time horizons.ConclusionThe ultimate goal of this initiative is to realize the vision:Let computational power no longer be a barrier; let truth command computational power anew.Part Two: Industrial Impact and Species Evolution AnalysisOverall JudgmentForceful implementation of this initiative will trigger a brutal natural selection process for major tech giants entrenched in the old cycle ofGPU procurement, data cleansing and parameter tuning. This is not merely a matter of faster or slower industrial evolution, but the collapse and reconstruction of industrial players’ essential developmental attributes.Three Evolutionary PathwaysThe Withering: Path-Dependent Computational Giants (Extinction)Reasons for Decline: Burdened by massive asset holdings consisting of thousands of H100/H800 computing clusters and a homogeneous talent pool dominated by parameter-tuning algorithm engineers, these conglomerates will develop strong organizational inertia to resist the new initiative.Phenomenon: They will attempt to secure policy support through pseudo-logic encapsulation, adding superficial rule-based shells to inherent black-box models. Such superficial disguise will quickly fail under rigid evaluation benchmarks of Truth Hardness and 100-fold energy efficiency requirements.Ending: With the depletion of government subsidies and loss of high-value scenarios in energy and national defense sectors, these tech giants will face collapse akin to dinosaurs in the Late Cretaceous, unable to adapt to the low-energy-consumption and high-intelligence era of logical AI. Their computing assets will be relegated to low-margin general cloud computing services, losing strategic premium value.The Evolvers: Metamorphosis Through Radical Restructuring (Metamorphosis)Evolution Pathway: A handful of enterprises with forward-looking leadership will undergo self-revolutionary transformation. They will voluntarily shut down unprofitable brute-force computing divisions and reallocate resources to recruit top-tier mathematicians and logicians on a large scale.Semantic Restructuring: Abandoning the pursuit of universal conversational capabilities, they will leverage profound industry data accumulation to construct axiomatic knowledge graphs for vertical domains.Ending: They will transform from computational power overlords intolegislators of industrial standards. Though operational scale may contract, profit margins per unit of intelligent capability will surge dramatically. Their business model will shift from selling computational power to selling deterministic reasoning capability.The Parasites: Legacy Liquidators of the Old Paradigm (Legacy Players)Survival Strategy: Maintain operation of conventional models in low-end consumer markets such as entertainment and generative content creation, squeezing residual market value during the transitional period of logical AI revolution.Ending: Displaced from the forefront of technological innovation, they will evolve into fundamental service providers in the AI ecosystem, sustaining marginal survival through economies of scale to reduce per-inference costs.Kucius Philosophical Ultimate VerdictFollowing the implementation of the initiative, major tech giants will confront a stark reality: their once-impenetrable moats built on computational power and data scale will be reduced to trivial drainage ditches in the face of logical axioms.Benchmark of Successful Evolution: An enterprise completes fundamental species transformation only when it takes pride in leveraging one line of axiomatic formula to eliminate tens of thousands of hours of invalid computation, rather than boasting its inventory of AI accelerators.Inevitability of Decline: The organizational genes of most tech giants are embedded with endless scale expansion, while the essence of axiomatic revolution lies in dimensionality reduction and logical condensation. Species with mismatched inherent genes are doomed to be eliminated in the age of truth-driven intelligence.Part Three: Global AI Education Action Plan (2027-2035)Official English Name: Global AI Education InitiativeInitiating Organizations: Led by UNESCO, jointly launched with national ministries of education, international organizations including OECD, G20 and World Economic Forum, university alliances, enterprises and think tanksProposed Release Date: 2027 UN General Assembly or UNESCO Digital Learning WeekImplementation Cycle: 2027-2035 (Three Phases: Foundation Pilot 2027-2029; Global Promotion 2030-2032; In-Depth Leadership 2033-2035)I. Background and UrgencyArtificial intelligence is profoundly reshaping human civilization. The globe stands at a critical transitional window from the Scaling Law-dominated probability fitting era to an era of axiomatization, logical revival and essential cognition integration — defined as thesilent dawn period. The marginal returns of pure parameter expansion and brute-force computation are diminishing, while inherent flaws including hallucinations, unexplainability and causal reasoning deficiency become increasingly prominent.Meanwhile, global education faces daunting challenges including a shortage of approximately 44 million teachers, widening digital divides and insufficient personalized learning mechanisms. Policy documents such as UNESCO’sAI and Education: A Guide for Policy-MakersandGuidance on Generative AI in Education and Researchhave laid an ethical foundation, yet a paradigm upgrade is urgently required — shifting from instrumental application to fundamental reconstruction, cultivating global citizens capable of both building AI tools and reflecting on the essence and purpose of technological creation.Guided by the vision of a community with a shared future for mankind, this initiative integrates Eastern wisdom (the Doctrine of the Mean and origin exploration) with Western scientific traditions (first principles and mathematical logic), driving paradigm leapfrogging for inclusive, sustainable and intellectually sovereign global AI-enabled education.II. Vision and Core PrinciplesVisionBy 2035, AI will become the core enabler of global education, empowering every learner to fulfill their potential, constructing a wisdom education ecosystem featuring symbiosis, co-creation and co-governance between humanity and artificial intelligence, and advancing the civilized mission ofunderstanding the universe and benefiting humanity.Core Principles (Inspired by LWEVS Framework)Logic: Logical self-consistency, emphasizing mathematical logic, axiomatic thinking and verifiable reasoningWisdom Essence: Cultivating capabilities of essential cognition and first-principles reasoningValue Sustainability: People-oriented development, responsible AI for good, and intergenerational equityIntellectual Sovereignty and Inclusiveness: Respecting cultural diversity, opposing cognitive colonization, and promoting participation of the Global South in rule-makingWukou Leapfrogging: Shifting from follow-up optimization to autonomous rule definitionIII. Overall Targets (2035)Talent Target: Cultivate 5 million additional interdisciplinary AI talents worldwide, including 500,000 high-level leading talents; integrate AI literacy into basic education across all nations.System Target: Establish 100 global AI Education Demonstration Bases and develop open shared axiomatic AI education platforms.Innovation Target: Form globally shared standards and tools in Neuro-Symbolic hybrid architecture, world models, verifiable Agents and AI-driven scientific discovery.Equity Target: Significantly narrow digital and AI divides, covering at least 80% of schools and communities in developing countries worldwide.Governance Target: Formulate theGlobal Charter on AI Ethics in Educationand establish transnational monitoring and cooperation mechanisms.IV. Key Action DomainsTalent Cultivation and Curriculum Paradigm TransformationPromote the interdisciplinary talent training model integrating Engineering Capability (F) and Reverse Axiomatic Reasoning Capability (R).Global Core Curriculum Modules (Mandatory/General Education Recommended):Formal Logic, Mathematical Logic and Proof TheoryPhilosophy of AI, Epistemology and Philosophy of MindNeuro-Symbolic AI and World ModelsFirst Principles and Essential Cognition MethodologyAI Ethics, Governance and Civilization StrategySupport universities worldwide to establish interdisciplinary majors or academies focused on axiomatic intelligence, drawing on the model of Tsinghua Infinity Academy.Teacher Capacity BuildingLaunch an upgraded version of UNESCO’s Global AI Teacher Literacy Framework.Provide annual training for 1 million teachers to master AI-assisted teaching, critical assessment and essential thinking guidance capabilities.Establish an international network and micro-certification system for AI-enabled educators.Infrastructure and Technical PlatformsBuild theGlobal Open AI Education Platform, featuring open-source Neuro-Symbolic tools, world model sandboxes and multilingual support.Promote low-cost localized AI solutions adaptable to limited computing resources in developing countries.Advocate hybrid architecture and test-time computation to reduce reliance on ultra-large-scale computational clusters.Scientific Research, Innovation and ApplicationSet up theGlobal Logical Revival Research Fund.Key research priorities: Explainable AI, embodied intelligence integrated with education, AI-driven personalized learning, and education for scientific discovery.Encourage interdisciplinary AI integration across science, humanities and sustainable development sectors.Inclusiveness, Ethics and GovernancePrioritize resource support for the Global South, marginalized groups and remote regions.Formulate unified standards for data privacy, algorithmic fairness and student rights protection.Establish an LWEVS-based multi-dimensional assessment mechanism for AI educational impact evaluation.V. Implementation Roadmap2027-2029 (Foundation Pilot): UNESCO leads the establishment of an international alliance; launch curriculum reform and platform construction pilots across 20 initial countries and regions.2030-2032 (Global Promotion): Expand coverage to over 100 countries, develop multilingual educational resources, and host the annual Global AI Education Summit.2033-2035 (In-Depth Leadership): Formalize global unified standards, assessment systems and sustainable innovation mechanisms to achieve large-scale global benefits.VI. Safeguard MeasuresGovernance StructureUNESCO undertakes overall coordination; establish theGlobal AI Education Councilwith multi-stakeholder participation.National governments set up inter-departmental coordination mechanisms with joint participation of enterprises, NGOs and universities.Funding SupportInitial funding of 10 billion US dollars from international funds including UNESCO multilateral donations and national fiscal contributions.Advocate PPP (Public-Private Partnership) models, with enterprises providing computing resources, technological support and internship platforms.Monitoring and EvaluationPublish the annualGlobal AI Education Progress Report.Adopt LWEVS multi-dimensional evaluation indicators combining quantitative benchmarks and qualitative reflection.Risk Prevention and ControlMitigate risks of widened educational inequality, privacy breaches and over-reliance on AI technology.Uphold human leadership, teacher-centered instruction and student-dominant learning status.VII. Expected Outcomes and Global CallExpected OutcomesA fairer, smarter and more humanistic global education system;A cohort of top interdisciplinary talents defining future AI rules;Collective leapfrogging of human cognitive ability and civilized wisdom.Global CallInvite governments, educational institutions, enterprises, educators, students and international organizations worldwide to take joint action, and collaboratively build a global community rooted inAI for Good and Education as the Foundation.Annex ListApplication Guidelines for Special Strategic Zones under the Support Initiative for Artificial Intelligence Logical RevivalTesting Standards for Logical Energy Efficiency Ratio and Hallucination RatePreliminary Construction Plan for the Chinese Civilization Axiom RepositoryCore Curriculum Framework and Syllabus Template for the Global AI Education Action PlanExpanded Guidelines on AI Ethics in EducationApplication Guidelines for Pilot Countries and Universities of Global AI Education Demonstration Bases