Rethinking Higher Education/Chapter 2/en-zh
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Chapter 2: Teaching Means – Humanity and AI from a Philosopher's View
Ole Doering
| English (Source) | 中文 (Target) |
|---|---|
| == Teaching Means – Humanity and AI from a Philosopher’s View == | == 教学的意义——从哲学家视角看人性与人工智能 == |
| Ole Doering | Ole Doering |
| Hunan Normal University | 湖南师范大学 |
| Abstract | == 摘要 == |
| This paper discusses the nature of ‘AI’, as a challenge for human self-understanding and a task for pedagogy, to come to terms with the best of our knowledge. From a philosophical perspective, the outlook depends upon the chosen anthropology, which could integrate biological and social dimensions of technology literacy. The ways in which human learning is naturally programmed give us hope and dignity, regarding our ability to refresh our education system accordingly, stimulated by the need to come to terms with ‘AI’. | 本文从哲学角度探讨了"人工智能"的本质，将其视为对人类自我认知的挑战和教育学的一项任务，以便融合我们最好的知识。从哲学视角来看，前景取决于所选择的人类学立场，而这一立场可以整合技术素养的生物和社会维度。人类学习的自然编程方式给予我们希望和尊严，使我们有能力相应地更新教育体系，这种更新的动力来自于与"人工智能"和谐共处的需要。 |
| 1 Introduction | == 1 引言 == |
| How should we make sense out of the phenomenon that ‘AI’ has become a connecting theme for humanity in the 21 Century? How realistic is this theme? Is ‘AI’ already established as a ubiquitous technology in university teaching, or is its common use just beginning? Are the questions related to its suitability and application fundamentally the same everywhere or are they context sensitive? What is the perception of purpose and best practice in education and how could Europe and China learn from each other when exploring standards of its implementation? | 我们应该如何理解"人工智能"已成为二十一世纪人类连接主题这一现象？这个主题有多现实？"人工智能"是否已作为一种无处不在的技术在大学教学中确立，还是其普遍使用才刚刚开始？与其适用性和应用相关的问题在所有地方是否基本相同，还是对具体情境敏感？教育中的目的和最佳实践的认知是怎样的？在探索其实施标准时，欧洲和中国如何相互学习？ |
| In this paper, I am looking at the use of ‘AI’ in teaching, learning and pedagogy, through the lens of a healthy relation and reasonable human attitude towards AI, considering the objective of learning, the characteristics of this technology and the nature of humanity. The title includes an ambiguous term, ‘teaching means’. Its semantics encompass the interrelation between teaching and its means (tools and measures), what it actually means to teach (instead of preaching or mimicking), to instruct in the proper use of suitable means, and the implements for good teaching. The coherence of this ambiguity is established through philosophical acts of alignment of humanity and ‘AI’, that is, enquiring into the use of language and the operations of coherence and integrity. | 在这篇论文中，我通过健康的关系和人类对人工智能的合理态度这一视角，审视了"人工智能"在教学、学习和教育学中的使用，考虑了学习目标、这项技术的特征以及人类的本质。标题包含一个具有歧义的术语"教学的意义"（teaching means）。其语义涵盖了教学与其手段（工具和措施）之间的相互关系、教学（而非布道或模仿）的真正含义、指导正确使用适当手段的方法，以及良好教学的工具。这种歧义的连贯性通过人性与"人工智能"之间哲学调和的行为来建立，即探究语言使用以及连贯性和完整性的运作。 |
| The public marketing of the technology uses ‘AI’ as a general label across different industrial sectors. More accurately, one should talk about purpose-built machine learning based algorithmic processors of different types of data. The meaning of ‘artificial’, especially in its relation to natural and cultural is important but not standardized, so it needs to be determined. The meaning of ‘intelligence’ is even more important, because it does not merely express our understanding of the matter but is connected with the potential for agency. Hence, it is a matter of clarity and responsibility, to define these core terms, as names of a technology, the perception and application of which has great impact on human self-awareness, on the organization of crucial activities and the understanding of best practices, which contribute to the frameworks of curricula, regulations and social order. | 技术的公共营销在不同工业部门中使用"人工智能"作为通用标签。更准确地说，应该谈论的是为特定目的构建的、基于机器学习的不同类型数据的算法处理器。"人工的"（artificial）一词的含义，特别是其与自然和文化的关系非常重要，但并未标准化，因此需要加以界定。"智能"一词的含义更为重要，因为它不仅表达了我们对事物的理解，而且与能动性的潜力相关。因此，界定这些核心术语是一个清晰性和责任性的问题，因为作为一种技术的名称，对其感知和应用对人类自我意识、关键活动的组织以及最佳实践的理解都有重大影响，而这些理解又构成课程、法规和社会秩序的框架。 |
| Surprisingly little is being discussed regarding the proper naming of the technology, its processes and features. Considering the power of language and the responsibility, to use words to convey robust or truthful meaning, this failure needs to be redressed, considering the scale of the impact of imagination on social reality. It cannot suffice to refer to the habitual nonchalance in which society receives the linguistic design from interested parties. The absence of critical responses to instrumental usage of suggestive language, such as regarding advertisements, policy or sales pitches, or marketing campaigns, the most blatant recent example is the name ‘Meta-Verse’, promoting cyber virtual reality projections as a higher form of ontology, replacing metaphysics with wishful thinking. Whereas the metaverse has apparently failed, by a lack of infrastructure for both hardware and software, a monopolistic approach to platform development, and a lack of clear governance standards, the same vigor of hype and hubris is at work in portraying ‘AI’ as a novel entity beyond human comprehension, as it has been done with several previous technology breakthroughs driven by marketing expectations, including genomics as revolutionizing health or information technology as revolutionizing knowledge. On one hand, the revenues for some industries and many careers were boosted. On the other hand, a sizable sustainable impact on social welfare, stability, health and knowledge has yet to be seen, owing to exaggerated expectations, misleading perceptions and the limited shelf-life of shallow promises. | 令人惊讶的是，关于技术及其过程和特征的恰当命名的讨论少之又少。考虑到语言的力量和用词语传达可靠或真实含义的责任，这种缺失需要得到纠正，特别是考虑到想象对社会现实的影响规模。仅仅引用社会接受利益相关方语言设计时的习惯性漫不经心是不够的。对广告、政策、推销或营销活动中暗示性语言的工具性使用缺乏批判性回应，最为明显的近期例子是"元宇宙"这个名称，它将网络虚拟现实投射宣传为本体论的更高形式，用一厢情愿的想法取代了形而上学。虽然元宇宙显然已经失败了——由于软硬件基础设施不足、平台开发的垄断方法以及缺乏明确的治理标准——但同样的炒作和傲慢的力量也在将"人工智能"描绘为一种超越人类理解的新实体，正如之前几次由营销预期驱动的技术突破所做的那样，包括基因组学对健康的革命和信息技术对知识的革命。一方面，一些产业和许多职业的收入得到了提升。另一方面，对社会福利、稳定、健康和知识的可持续重大影响尚待观察，原因在于夸大的期望、误导性的认知和浅薄承诺的有限保质期。 |
| Intelligence is understood as a function of reason (Vernunft) or rationality (Verstand), to make spontaneous connections between patterns of knowledge and perceptions, as part of the operations of experience (Erlebnis and Erfahrung). The more specifically defined German terminology is used when this adds to the desired precision of the analysis. The connectivity is a natural technical feature of human intelligence, but not reducible to cognitive performance. In humans, intelligence is an activity and quality, woven into the entire fabric of sensuality, perception and reflection, the extended body (Leib), social interaction, empathy, language, guessing, learning by doing, etc. Intelligence is incomplete, alive, contradictory, generic, repetitive and conservative, spontaneous and functional. Re-making it artificially is possible by reduction and simulation, through models and incremental advancement. The astonishing progress in the related science and technology advancement should inspire sober critical scrutiny rather than | 智能被理解为理性（Vernunft）或知性（Verstand）的一种功能，它能在知识和感知的模式之间建立自发联系，作为经验（Erlebnis和Erfahrung）运作的一部分。在分析需要更高精确度时，使用定义更为具体的德语术语。连接性是人类智能的一种自然技术特征，但不可还原为认知表现。在人类身上，智能是一种活动和品质，编织在感官性、感知和反思的整体结构中，包括延伸的身体（Leib）、社会互动、共情、语言、猜测、在实践中学习等。智能是不完整的、活的、矛盾的、通用的、重复的和保守的，同时又是自发的和功能性的。通过还原和模拟、通过模型和渐进式改进来人工重建它是可能的。在相关科学和技术进步方面取得的惊人进展应该激发冷静的批判性审视，而非盲目的崇拜。 |
| This paper is based on a lecture and takes the form of an annotation to a debate that ought to take place. | 本文以讲座为基础，采取了对一场应当发生的辩论的批注形式。 |
| 2 Humanity in AI | == 2 人工智能中的人性 == |
| Language is the expressed experience of the operation of reason, within a culture and across cultures, connected by human reason-gifted nature. Learning in the sense of extending knowledge is the continuation of this experience, in a pedagogical way that engages natural and reflected language operations. Pedagogy keeps this process of engagement practical, according to human standards and individual capabilities. | 语言是理性运作的表达经验，存在于一种文化中并跨越文化，通过人类赋予理性的天性相连接。扩展知识意义上的学习是这一经验的延续，以一种教育学方式让自然和反思性的语言运作参与其中。教育学使这一参与过程保持实践性，符合人类标准和个人能力。 |
| 2.1 The human position | === 2.1 人的立场 === |
| In the perspective of the historical evolution of humanity, the meaning of ‘AI’, as an efficient set of invisible tools based on mathematical processes and simulations of language and cognition, should be understood in relation to natural language and learning, namely, how to use it well. | 从人类历史演变的视角来看，"人工智能"作为一套基于数学过程以及语言和认知模拟的高效的不可见工具的含义，应该从自然语言和学习的角度来理解，即如何善用它。 |
| Language represents knowledge culture and cultured knowledge. Knowledge culture is a social-epistemic environment that encourages, favors and supports knowledge as befits human beings according to our nature. Cultured knowledge is naturally grown structure and content of what we have learned, in ways that accommodate humanity. Technology is a priori instrumental, no matter its degree of intellectuality, materiality technicality, it is technology only when it can be and is meant to be used to serve imagined ends. The ambiguous, often metaphorical language of technology description can confuse the relationship between humanity and technology and its proper understanding. Typically, the ambiguity which is characteristically rooted in the instrumental ontology of technology, is taking the form of either disproportional faith in what the latter can achieve, (thereby ‘forgetting’ the subject agent), or pre-emptive submission of human inferiority under its fictitious omni-potency (thereby ignoring the limitations of a mere object). When combined with hype, hubris or strategic communication, this confusion can be naturally amplified or purposefully manipulated, so that consumption and obedience remain as the sole human roles. However, it can also be countered by enlightenment, that is, by utilizing reasonability, proportionality and humility, so that best use can be enabled. | 语言代表着知识文化和有教养的知识。知识文化是一种社会认识论环境，它根据我们的本性鼓励、偏好和支持适合人类的知识。有教养的知识是我们以适应人性的方式所学到的自然生长的结构和内容。技术先验地是工具性的，无论其智力性、物质性、技术性的程度如何，只有当它能够并且旨在被用于服务想象中的目的时，它才是技术。技术描述中含糊的、往往是隐喻性的语言可能会混淆人性与技术之间的关系及其正确理解。通常，这种根植于技术的工具性本体论中的歧义，要么表现为对技术所能实现之物的不成比例的信心（从而"忘记"了主体能动者），要么表现为在其虚构的全能性面前人类先验地自我臣服（从而忽视了一个纯粹客体的局限性）。当与炒作、傲慢或战略传播相结合时，这种混淆可以被自然放大或有目的地操纵，使消费和服从成为人类唯一的角色。然而，它也可以通过启蒙来对抗，即通过运用合理性、比例性和谦逊，使最佳使用成为可能。 |
| Therefore, responsible, sincere and accurate language is needed, for a clear understanding and a healthy attitude towards our technologies, especially those that directly affect our cognition, through simulation or speech. Thereby, can overcome ontological confusions, for example when redundant activities such as gaming are misconstrued as ‘to play’, and epistemic confusion, such as when quality is measured by outcomes instead of performance. That is, beginning with the input. | 因此，需要负责任的、真诚的和准确的语言，以清楚地理解我们的技术并对其保持健康的态度，特别是那些通过模拟或言语直接影响我们认知的技术。由此，可以克服本体论上的混淆，例如，当游戏（gaming）等冗余活动被误解为"玩耍"（to play）时，以及认识论上的混淆，例如，当质量以结果而非表现来衡量时。也就是说，从输入开始。 |
| An educated view on language will help establish a position, in which ‘to talk about what I know, and at the same time, know what I talk about, and how, considering the purpose’ of my speech. This means, language is instrumental for speaking truthful about ‚AI’, not just use ‘empty words’ without language. When facing an instrumental object, this relationship should be defined, first, whether and in what sense it is reasonable to trust machines more than humans. This leads to the requirement of shared roles. Can we trust humans to control machines so that they will serve our purpose and not just function according to design? With this attitude, ethics and science converge in a culture of deliberate humility, caution and care. | 对语言的有教养的看法将有助于建立一种立场："谈论我所知道的，同时知道我在谈论什么以及如何谈论，考虑到我言语的目的。"这意味着，语言对于真实地谈论"人工智能"是工具性的，而不仅仅是使用没有语言的"空话"。当面对一个工具性对象时，这种关系应该首先被界定：是否以及在什么意义上信任机器比信任人更为合理。这引出了共享角色的要求。我们能否信任人类控制机器，使其服务于我们的目的，而不仅仅是按照设计运行？带着这种态度，伦理和科学在深思熟虑的谦逊、谨慎和关怀的文化中汇聚。 |
| 2.2 The economic angle | === 2.2 经济视角 === |
| Like with many other technological innovations before, ‘AI’ inspires debate over the future of traditional ways of life. For example, the ‘Generative Pre-trained Transformer’, namely GPT, shows potential to expedite the mechanical operations of linguistic functions by orders of magnitude, provided energy and data are sufficiently well supplied. It might revolutionize online researches or the translation of written or spoken text. Naturally, there is concern about this technology’s impact on human work. Will it become obsolete? Can work be replaced by production? In the past, such fears were based on evidence of the labor market. Mechanically repetitive activities in the workflow were automated, then automation replaced several areas of blue collar, at the same time increasing demand for specialized or generalized white-collar activities. | 与之前的许多其他技术创新一样，"人工智能"激发了关于传统生活方式未来的辩论。例如，"生成式预训练变换器"即GPT，展示了在能源和数据得到充分供应的条件下，以数量级的速度加快语言功能机械操作的潜力。它可能会彻底改变在线检索或书面及口头文本的翻译。自然地，人们对这项技术对人类工作的影响感到担忧。工作会变得过时吗？工作能被生产取代吗？过去，这种恐惧基于劳动力市场的证据。工作流程中机械重复的活动被自动化了，然后自动化取代了多个蓝领领域，同时增加了对专门或通用白领活动的需求。 |
| Notably, the strategies of human resources development follow specific economic rationales, which are determined by policies and politics that define what is valuable and incentivized. The investment from society, direct and indirect, into the making of these opportunities and technological advantages, was taken in, without due consideration of the difference between market price and economic value of the machines. The practice of combined manufacturing replacement and social displacement was driven by opportunity that partly depended on oversight or carelessness. More sustainable options for a constructive employment of machines were not explored. The focus was only on the re-training or social aid for laid-off employees. Compensation of the added value and social cost of the inventions, that is normally calculated, as taxes or fees, was not even considered. Much of the cascading double-effect of accelerated innovation and social problems could be compensated from the onset, with replacement taxes or social dues, in proportion with the investment from society which enabled these developments. Whichever the means, the gain would be time, for learning, studying, planning, adding social benefit. | 值得注意的是，人力资源开发的策略遵循特定的经济逻辑，这些逻辑由界定什么是有价值的和受到激励的政策和政治所决定。社会直接和间接地对这些机会和技术优势的创造所进行的投资被吸纳了，而没有适当考虑机器的市场价格与经济价值之间的差异。这种将制造替代和社会置换相结合的做法是由部分依赖于疏忽或大意的机会驱动的。没有探索更可持续的机器建设性使用方案。焦点仅仅放在了被解雇员工的再培训或社会救助上。对取代传统劳动的发明所增加的价值和社会成本的补偿，通常以税收或费用来计算，甚至没有被考虑。加速创新和社会问题的级联双重效应中的大部分可以从一开始就得到补偿，通过替代税或社会缴费，与使这些发展成为可能的社会投资成比例。无论采取何种手段，收益将是时间——用于学习、研究、规划和增加社会效益。 |
| There would also be a cultivating effect, by taking some desperation out of the greed for quick revenue. Buying time would also mean a re-allocation of this most precious resources, to a variety of value-providers in society for the benefit of a more sustainable economic development. Considering that most of the advantages of AI-driven inventions lie in the efficiency gain and effectiveness indifference, there can be various scenarios for additional trade-offs when such strategy were pursued. This cultivating effect would earn the revenue of consumers’ maturity, by allowing them to get use to the new tools, to see them as objects and find out how they matter. The absence of such cultivation efforts translates into the need for general and special education, namely in technology us and economics. This is evident from the standpoint of humanity, serving the people and leaving no one behind, on the path of democratic modernization. | 还会有一种文化培育效果，通过从对快速收益的贪婪中消除一些绝望。购买时间也意味着将这种最宝贵的资源重新分配给社会中各种价值提供者，以促进更可持续的经济发展。考虑到人工智能驱动的发明的大多数优势在于效率提升和效果的无差别性，当采取这种策略时，可以有各种额外权衡的情景。这种文化培育效果将通过允许消费者习惯新工具、将其视为客体并发现它们的意义来获得消费者成熟度的回报。这种培育努力的缺失转化为对通用和专业教育的需求，即技术使用和经济学教育。这从服务人民、不让任何人掉队的人性立场和民主现代化道路来看是显而易见的。 |
| Within such an environment, work as an anthropological necessity and a source of dignity in society, would be rehabilitated as a social good connected with education. It would increase social integrity, counter new stratification and sustain sustainable development. The fact that we are not used to leading such debates in a reasonable way publicly, but are driven by hasty responses to relentless new market-impulses, indicates the need to dig deeper than mending superficial phenomena, opportunities or conflicts. This is the first contribution to a debate on ‘AI’ emancipated from the technological rationale. | 在这样的环境中，工作作为人类学上的必要性和社会中尊严的来源，将作为与教育相联系的社会公益而得到恢复。它将增加社会完整性，对抗新的社会分层，维持可持续发展。我们不习惯以合理的方式公开领导此类辩论，而是被对不断涌来的新市场冲动的仓促回应所驱动，这一事实表明需要比修补表面现象、机会或冲突更深入地挖掘。这是从技术理性中解放出来的对"人工智能"辩论的第一个贡献。 |
| 2.3 Aesthetic quality | === 2.3 审美品质 === |
| Aesthetics in general connects coherent perception with normative judgement, transcending what is beautiful into the concept of beauty, what is good into the concept of goodness, or what is truthful into the concept of truth, so that such concepts can carry the learning momentum teleologically towards the concept of cultivation. | 一般意义上的审美学将连贯的感知与规范性判断联系起来，将美的事物超越为美的概念，将善的事物超越为善的概念，将真实的事物超越为真理的概念，使这些概念能够以目的论的方式将学习动力引向文化修养的概念。 |
| This prospect, however, is enabled by human experience, of the world, of itself and of intellectual reflection. Our dreams of perfection remain as ambiguous as they are ambitious, unless they are matched, balanced and integrated by the experience of agency. The most inclusive microcosm to which aesthetics can be applied is ones own extended human body (Leib), as the agent of cultivation and education. This is where we distinguish the original from simulation, responsible agency from play, meaningful coherence from logical plausibility, purpose from function, dignity from value, work from activity, learning (Bildung) from training (Ausbildung). This is where we also revisit the logical relevance of classical forms of indirect speech, such as analogy, metaphor, correlation, irony, humor or allusion. | 然而，这种前景是由人类经验所赋能的——对世界的经验、对自身的经验以及知性反思的经验。我们对完美的梦想既模糊又雄心勃勃，除非它们与能动性经验相匹配、平衡和整合。审美可以应用的最具包容性的小宇宙是人自己的延伸身体（Leib），作为修养和教育的能动者。这是我们区分原创与模拟、负责任的能动性与游戏、有意义的连贯性与逻辑合理性、目的与功能、尊严与价值、工作与活动、教养（Bildung）与训练（Ausbildung）的地方。这也是我们重新审视间接言语经典形式（如类比、隐喻、关联、反讽、幽默或暗示）的逻辑相关性的地方。 |
| As a social entity, such a body has a face. Can we give it real human face and purpose in education? Even when it is convenient, or appealing or consoling, to offer a substitute instead of the natural you, a perfect simile of a human caregiver, such as in nursing or teaching, deploying humanoid robots to handle the needs of vulnerable groups, may be less risky socially than cheating well-off citizens with a friendly mask, but is certainly unfair. The resources saved through this shortcut, (if not miserly), could better be invested in the real development of social relationship that can function well under conditions of the 21st Century. | 作为一个社会实体，这样的身体有一张面孔。我们能否在教育中赋予它真正的人类面孔和目的？即使提供一个替代品而非真实的你是方便的、吸引人的或令人安慰的——一个完美的人类照护者的仿品，例如在护理或教学中部署人形机器人来处理弱势群体的需求——这可能比用友好的面具欺骗富裕公民的社会风险更低，但肯定是不公平的。通过这种捷径节省的资源（如果不是出于吝啬的话），最好投资于发展能够在二十一世纪条件下良好运作的真实社会关系。 |
| A human face and human shape belong to humans, not machines. Irony, arts and comedy have taught modern citizens the value of liberal playfulness with anything, challenging traditional taboos, in order to overcome limitations of humanity that were based on external powers, (such as political institutions or religious authorities). However, there is a necessary natural tension between playful expression and transgressions, and human social sustainability; social freedom is limited by the freedom of the other, which includes cherishing human nature or tradition. The margins of tolerance must be re-negotiated while technology advances, so as to prevent that simulation and mimicry design become novel forms of fraudulent language. Therefore, traditional knowledge about simulation and truth should be mobilized. | 人的面孔和人的形态属于人，而非机器。反讽、艺术和喜剧教导了现代公民自由地玩弄一切事物的价值，挑战传统禁忌，以克服基于外部权力（如政治机构或宗教权威）的人类局限性。然而，在玩耍性表达与越界和人类社会可持续性之间存在着必要的自然张力；社会自由受到他人自由的限制，这包括珍视人的本性或传统。随着技术的进步，容忍的边界必须重新协商，以防止模拟和拟态设计成为新形式的欺诈性语言。因此，需要调动关于模拟与真理的传统知识。 |
| The same applies to the wisdom of language. This is not a condescending phrase to mollify conservative thought, but a philosophical insight formulated by Gadamer, „Language is the universal medium in which understanding itself takes place. The manner in which understanding is realized is exegetic”. In other words, the meaning is in the use of the language. This is the beginning of the value of analytical approaches to philosophy. | 同样的道理也适用于语言的智慧。这不是一个居高临下的话语来安抚保守思想，而是Gadamer提出的一个哲学洞见："语言是理解本身得以实现的普遍媒介。理解的实现方式是诠释性的。"换言之，意义在于语言的使用。这是分析哲学方法价值的起点。 |
| When we talk about digital inventions, we cannot start with smartphones, but with tools and language itself, as physical and intellectual implements of digitalization. The word ‘digital’ derives from the Latin digitus, meaning ‘finger’ and extensions of the hand: wooden sticks and stone tools mark the beginning of human ability to shape the material world according to purpose and to thereby establish new horizons. This is literally the beginning of manufacturing, (use hands to make something). Understanding the meaning of technology, as the means to transform ‘that what is given’, data, into ‘that what is made’, facts, helps appreciate what matters in learning to manu-facture. | 当我们谈论数字发明时，不能从智能手机开始，而应从工具和语言本身开始，作为数字化的物理和智力工具。"数字的"（digital）一词源于拉丁语digitus，意为"手指"以及手的延伸：木棍和石器标志着人类根据目的塑造物质世界并由此建立新视野的能力的开始。这从字面上就是制造（manufacturing）的开始（用手来做东西）。理解技术的含义——作为将"已给予之物"（data，数据）转化为"已制成之物"（facts，事实）的手段——有助于理解在学习制造（manu-facture）中什么是重要的。 |
| This enquiry also helps us, to take one further step, towards the mapping of the conceptual landscape of learning and ‘AI’, namely connect ontology and epistemology. Manipulating the matter of symbols of meaning, organically and mentally, takes the form of reading and writing, that is the ability to draft alternative designs of the world, of truth, beauty and good, to create space for imagination, that is, for exploring new ways, to play according to, and even testing known rules, thereby inspiring culture. Using language instrumentally, in speech and writing, mark the beginning of the human ability to connect across time and space according to purpose – that is, of intellectual learning. In the same vein, misunderstanding and fault, twisting and lying enter cultural life. The aesthetic perspective makes sure that there remain options for aligning different approaches to using ‘AI’ within one trajectory of wholesome cultivation, culminating in the connotations and expressions of language. | 这种探究也帮助我们再迈出一步，走向学习与"人工智能"概念地图的绘制，即连接本体论与认识论。有机地和心理地操纵意义符号的物质，采取阅读和写作的形式，即起草世界、真理、美和善的替代设计的能力，为想象创造空间，即探索新方式，按照甚至测试已知规则来进行游戏，从而激发文化。在言语和写作中工具性地使用语言，标志着人类根据目的跨越时空进行连接的能力的开始——即智力学习的开始。同样，误解和错误、曲解和谎言也进入了文化生活。审美视角确保在一种全面修养的轨道上，仍然有对齐不同"人工智能"使用方法的选项，最终汇聚于语言的内涵和表达。 |
| 2.4 Our task | === 2.4 我们的任务 === |
| The practice of knowledge changes. For humans, it remains naturally digital. Culture is a natural human technology. Culture and technology are functional expressions of human nature. Over time, we disconnect technical learning from evolutionary (practical) learning, in acts of specialization. This is paralleled by scientific reductionism in disciplines within the science field (Wissenschaft). In both cases, how to assure the humanity-driven alignment of areas of knowledge? Should the emerged silos be re-synchronized, synthesized or transformed into a new kind of competence, suited for the 21st Century, in a healthy manner? How can we learn to program methodical specialization so that it can serve the evolution of humanity? | 知识的实践在变化。对人类而言，它天然是数字的。文化是一种天然的人类技术。文化和技术是人类本性的功能表达。随着时间推移，我们在专业化行为中将技术学习与进化性（实践性）学习脱节。这与科学领域（Wissenschaft）内学科的科学还原论并行。在这两种情况下，如何确保以人性为驱动力的知识领域对齐？这些已经出现的孤岛是否应该以健康的方式重新同步、综合或转化为一种适合二十一世纪的新型能力？我们如何学习编程系统性的专业化，使之能够服务于人类的进化？ |
| Our capability to manipulate the world extends macro-and microscopic boundaries, feeding fantasy and fiction, to drive the imagination. What had been the traditional projections of the margins of humanity, such as golem, homunculus, androids or cyborg, now seem to require new, timely images. Traditional narratives were carried by imagination based on very little experience, typically under the provisional label of science fiction. Now, we need names and stories that are based on updated experiences, with the social practice of technology applications in biology, information processing, chemistry or physics. Such narratives cannot not be chiefly shaped by economic or sci-tec stakeholders, whose language and interest are not fully rooted in society. They cannot realistically be left to experts or natural social evolution, either, because we have learned that the power of technology development and marketing is muting common sense. Certainly, hyped fears and promises, dramatized expectations and premature market entry are no new phenomena. However, the impact of unresolved issues with the existing, pre-maturely implemented products, such as smart phones, is incrementally increasing in the uncertainty of ensuing risks, while at the same time the cultural resilience required for proper assessment and societal processing has been eroding over decades. | 我们操纵世界的能力扩展了宏观和微观的边界，滋养着幻想和虚构，驱动着想象力。曾经是人类边界的传统投射——如魔像（golem）、人造小人（homunculus）、仿生人（android）或赛博格（cyborg）——现在似乎需要新的、及时的形象。传统叙事由基于极少经验的想象所承载，通常被暂时标记为科幻小说。现在，我们需要基于更新经验的名称和故事，涉及生物学、信息处理、化学或物理技术应用的社会实践。这些叙事不能主要由经济或科技利益相关者来塑造，因为他们的语言和兴趣并非完全植根于社会。它们也不能现实地留给专家或自然的社会演化，因为我们已经了解到技术开发和营销的力量正在使常识失声。当然，被炒作的恐惧和承诺、戏剧化的期望和过早的市场进入并非新现象。然而，现有过早实施产品（如智能手机）的未解决问题的影响正在不断增加后续风险的不确定性，同时适当评估和社会处理所需的文化韧性数十年来一直在被侵蚀。 |
| This is evident in three areas, on different levels. | 这在三个不同层面的领域中显而易见。 |
| First, there is a widely nurtured confusion about agency. When talking about „what ‘AI’ does”, the grammatical subject and object must be clearly distinguished from the real subject and object. The dynamic composite artefact we call ‘AI’ owes its entire existence, functionality and evolution to human agency, no matter how much they agree with the intended performance. We should avoid socially rich semantical connotations, such as ascribing learning, thinking or suggesting to ‘AI’. A technology deserves technical terminology, not emotionalized labeling. The suggested anthropomorphism is begging the question of an emerging self-consciousness or even conscience and inflates the probability of increasingly automated-perfection, even when the axiomatic assumption is not shared, that it is impossible for reasons a priori. This is not a benign slip (any longer), because the very purpose of the technology is to amplify efficiency, that is, power. Thereby, by design and default, miniscule issues, faults, risks or dangers are amplified as well. What used to be benign and speculative might now have huge and deep real consequences. This is why painstaking scrutiny of the propriety of language is called for. | 第一，关于能动性存在广泛培养的混淆。当谈论"'人工智能'做了什么"时，语法上的主语和宾语必须与真实的主体和客体清楚区分。我们称之为"人工智能"的动态复合人工制品的全部存在、功能和演化都归功于人类的能动性，无论它们与预期表现多么一致。我们应该避免社会内涵丰富的语义联想，例如将学习、思考或建议归因于"人工智能"。技术应当使用技术术语，而非情感化的标签。所暗示的拟人化在预设一种新兴自我意识甚至良知的问题，并夸大了日益自动化完美的概率，即使并不接受这一先验的公理性假设——即这对于先验理由来说是不可能的。这不再是一种善意的失误，因为这项技术的目的本身就是放大效率，即权力。因此，在设计和默认情况下，极小的问题、错误、风险或危险也会被放大。过去可能是善意的和推测性的东西现在可能产生巨大而深远的现实后果。这就是为什么需要对语言的恰当性进行一丝不苟的审视。 |
| Second, compared with the circles of social accommodation to earlier technology immersions in society, the sequences in which new dimensions of efficiency and impact are being introduced, are getting ever more hastily and truncated. Here ‘introduction’ is a euphemism, because the insinuated social decency is not observed. Scientific disciplines such as Technology Assessment (Technikfolgenabschätzung), technology ethics, applied mathematics (Informatik) and general skills in understanding these inventions have not been established as major subjects. This implies that most citizens are virtually illiterate when it comes to ‘AI’. | 第二，与社会适应早期技术沉浸的周期相比，引入新的效率和影响维度的序列变得越来越仓促和截断。这里的"引入"是一种委婉说法，因为暗含的社会礼节并未被遵守。技术评估（Technikfolgenabschätzung）、技术伦理、应用数学（Informatik）以及理解这些发明的通用技能等科学学科尚未被确立为主要学科。这意味着大多数公民在"人工智能"方面实际上是文盲。 |
| Third, substitution of human activity by machines is part of their desired purpose. However, it must be reasonably planned, with suitable measures attached, in proportion towards the entire process, not just investment and revenue. As mentioned above in the passage about work and production, what applies now in the area of language should have been applied in economics decades ago, namely honesty about the genesis and distribution of added value generated by machines that supplant traditional labor. | 第三，用机器替代人类活动是其预期目的的一部分。然而，必须合理地规划，并附以适当的措施，与整个过程成比例，而不仅仅是投资和收益。如上文关于工作与生产的段落所述，现在在语言领域应当适用的原则，几十年前就应该在经济学中适用，即对于取代传统劳动的机器所产生的增值的起源和分配保持诚实。 |
| This is a typical situation that calls for institutional structural solutions, in order to ensure purpose and sustainability of policies. ‘AI’ literacy, concerning humans, is a germane metaphor. It barely expands the literal meaning of being able to read, calculate and write in a cultivated manner about the symbolic and physical operations of the hard- and software, within their ontological, economic and infrastructural context. Herein lies the obvious task for a reformed educational sector. Although applied interdisciplinary curricula should have remained, or have been rehabilitated, as the gold standard in education in general, in any field, such as health, economics, history or philosophy, ‘AI’ as a social sphere, (and significantly connected indeed with health, economics, history or philosophy), could be the culmination point. | 这是一个典型的需要制度性结构解决方案的情况，以确保政策的目的性和可持续性。关于人类的"人工智能"素养是一个贴切的隐喻。它几乎只是将能够以有教养的方式阅读、计算和书写关于硬件和软件的符号和物理操作的字面含义加以扩展，并将其置于本体论、经济和基础设施的背景下。这里有教育部门改革的明显任务。虽然应用性的跨学科课程应该一直保持或被恢复为各领域（如健康、经济、历史或哲学）教育的金标准，但"人工智能"作为一个社会领域（确实与健康、经济、历史和哲学有着重大联系）可以成为其顶点。 |
| The means to understand the natural balance between what we can do, what we may do and what we must do are challenged. Traditional knowledge from the classics, for prudence and pragmatism, needs revisiting: How to do the right things for the right reasons; how to know what you do and do as you know it; how to say what you mean and mean what you say, regarding what ‘AI’ is and what it means. Considering responsibility, normative prudence is a meta-level requirement of digital competence (in morality, law and ethics). | 理解我们能做什么、可以做什么和必须做什么之间自然平衡的手段受到了挑战。来自经典的关于审慎和务实的传统知识需要重新审视：如何为正确的理由做正确的事情；如何知道你所做的并按照你所知道的去做；如何言行一致，关于"人工智能"是什么以及它意味着什么。考虑到责任，规范性的审慎是数字能力的元层面要求（在道德、法律和伦理方面）。 |
| 2.5 Pedagogy: How to learn from being prepared for ‘AI’ | === 2.5 教育学：如何从为"人工智能"做准备中学习 === |
| Bringing the language, the conceptual and institutional discourse about ‘AI’ and pedagogy towards a timely state of experience and knowledge, practically means to coordinate science and nature so that we can support cultivated learning. | 将关于"人工智能"和教育学的语言、概念和制度话语带到经验和知识的及时状态，实际上意味着协调科学与自然，以便我们能够支持有教养的学习。 |
| The related most basic understanding derives from applied holistic theories about learning programs, in combination with neurological research on the actual brain development, as the biological habitat for learning. Humans learn from integrated processes of experience involving both, the respective individual and the social factors. Philosophy offers interpretations, of how to make sense out of the overall practical implications of this disconnected multi-disciplinary knowledge. | 最基本的相关理解来自关于学习程序的应用性整体理论，结合关于实际大脑发育的神经学研究——大脑是学习的生物栖息地。人类从涉及个人和社会因素的整合经验过程中学习。哲学提供了如何从这些断裂的多学科知识的整体实践含义中获取意义的解释。 |
| Considering learning, the first primary experience (Erleben) spontaneously connects the sensual individual with the environment. This is not a neutral act but a keynote for the learning biography and epistemic career. The quality of this connection determines the range of involuntary perception, together with the subjective mode of learning, how it has a ‘feeling for’ judgement. In particular, Erleben preconditions secondary experience (Erfahrung), that is, the consciousness-building processes of connectivity events, coordinated according to the patterns of cognition, and rationalized with the capacities of concepts (Verstand) and principles (Vernuft). | 考虑到学习，第一性的原初经验（Erleben）自发地将感官个体与环境联系起来。这不是一种中性行为，而是学习传记和认识论生涯的基调。这种联系的质量决定了非自愿感知的范围，连同学习的主观模式，即它对判断的"感觉"。特别是，Erleben预设了二次经验（Erfahrung），即意识建构的连接性事件过程，根据认知模式进行协调，并用概念能力（Verstand）和原理能力（Vernunft）加以合理化。 |
| The external connectivity continuum of learning is explained as the systematic unfolding of ‘learning-by-doing’ exercises‚ from observation to hands-on experience during work, as methodic interconnection with the tangible environment, by the father of modern pedagogy, Johann Heinrich Pestalozzi (1746-1827). Learning through his own failed experiments, he moved from theological and technocratic dogmatism towards the appreciation of mathematics as an interpretive approach to human nature, learning patterns from working, (as distinguished from production). This makes his theory accessible and relevant for contemporary enquiries into the conditions of learning best with and about ‘AI’, as humans. | 学习的外部连接性连续体被现代教育学之父Johann Heinrich Pestalozzi（1746-1827）解释为"做中学"练习的系统展开——从观察到工作中的动手经验，作为与有形环境的方法性互联。从自己失败的实验中学习，他从神学和技术官僚教条主义转向了对数学作为解释人类本性方法的欣赏，从工作（而非生产）中学习模式。这使他的理论对当代关于作为人类如何最佳地与"人工智能"一起学习的条件的探究变得可理解和相关。 |
| He thus explains, how algorithms, their architecture and operations at work in ‘AI’ should be serving human natural needs. ‘My method does nothing other than reproducing the simple course of nature.’ ... ‘Every sensitive perception deeply imprinted in the human mind triggers a series of secondary notions that come more or less close to this perception...thus bringing together objects whose essence is the same; your understanding of the inner truth of these objects will be expanded, sharpened, and strengthened’. Such education nurtures literacy and sovereignty over the technology. | 他这样解释了算法及其架构和运作如何在"人工智能"中服务于人类的自然需求。"我的方法不过是再现自然的简单过程。"……"深深印刻在人类心灵中的每一个敏感感知都会触发一系列二次概念，这些概念或多或少接近这种感知……从而将本质相同的对象聚集在一起；你对这些对象内在真理的理解将被扩展、锐化和加强。"这样的教育培养了对技术的素养和主权。 |
| On the other hand, the internal connectivity of learning is explained by neuro-biology research, under the term of ‘brain-friendly learning’. For ‘AI’ this is relevant because the axiomatic neural network models draw from the conceptual coherence, or aesthetics, the generic connectivity and plasticity of natural models, and motivational orientation from prompts. Hence, there appear significant overlapping heuristics, between the biological and technological models that can be explored for the purpose of clarifying the conceptual relationship and alignment of developmental trajectories, such as human wellbeing. | 另一方面，学习的内部连接性由神经生物学研究在"脑友好学习"（brain-friendly learning）的术语下加以解释。对于"人工智能"而言，这很重要，因为公理性的神经网络模型从自然模型的概念连贯性或审美性、通用连接性和可塑性以及提示的动机导向中汲取力量。因此，在生物模型和技术模型之间似乎存在重要的重叠启发式方法，可以被用于澄清概念关系和发展轨迹（如人类福祉）的对齐。 |
| Proper pedagogy is efficient and effective. It pursues its purpose, namely education, with the least possible collateral damage and the greatest desired developmental impact on the individual. From the perspective of the biology of ‘brain-friendly learning’, pedagogy, with its curricula and institutions, avoids damaging or misguiding interventions, while carefully amplifying natural processes. Notably, amplifying is different from simulation or correlation, because it directly manages intentional causality, without pretending or even the need to theorize. Regarding organic structures, such pedagogy utilizes the mechanism of neuro-plasticity as a property for learning, with the goal, to make best use of education, as a humanistic technology, to handle technologies. On the theoretical level, it avoids closed concepts, speculative ideologies and justification of shame or ‘blunt force of perfection’, as means to the end of education, such as in the measures of body-transformation envisaged by proponents of trans-humanism. | 恰当的教育学是高效且有效的。它以最少的附带损害和对个人最大的预期发展影响来追求其目标——即教育。从"脑友好学习"生物学视角来看，教育学及其课程和机构避免了破坏性或误导性的干预，同时谨慎地放大自然过程。值得注意的是，放大不同于模拟或关联，因为它直接管理有意的因果性，无需假装甚至不需要理论化。关于有机结构，这样的教育学利用神经可塑性机制作为学习属性，目标是最好地利用教育——作为一种人文主义技术——来处理技术。在理论层面，它避免封闭的概念、投机性意识形态和以羞耻或"完美的蛮力"作为教育目的之手段的正当化——例如超人主义倡导者所设想的身体改造措施。 |
| ‘Brain-friendly learning’ is not just treating the brain in a friendly manner, but also reversely allows us to be treated friendly by our body, while we strive to advance, connecting all human resources into a natural learning hub. At the very beginning, it activates the ‘joy’-ful (homoeostatic) experience (Erlebnis) in connections with the environment, combining pre-conscious responses to need, and first experiences (Erfahrungen) of causality, power and relatedness. | "脑友好学习"不仅是以友好的方式对待大脑，而且反过来允许我们在努力进步的同时被身体友好地对待，将所有人类资源连接成一个天然的学习枢纽。在最初阶段，它激活了与环境连接中"快乐的"（稳态的）经验（Erlebnis），结合了对需求的前意识反应，以及对因果性、力量和关联性的最初经验（Erfahrungen）。 |
| ‘All newborns possess a certain repertoire of behavioral reactions which are activated in the course of, or together with, the activation of the central stress responsive systems when their homeostasis is threatened by cold, hunger, thirst etc. … [The] early recognition of the controllability of a stressor by an own action is one of the earliest associative learning experiences of a child and it has a strong imprinting impact on the developing brain.’ | "所有新生儿都拥有一定的行为反应库，当其稳态受到寒冷、饥饿、口渴等威胁时，这些反应在中央应激反应系统激活的过程中或同时被激活……[对]压力源可通过自身行为加以控制的早期识别，是儿童最早的联想学习经验之一，它对发育中的大脑具有强烈的印刻影响。" |
| In other words, ‘brain-friendly learning’ cultivates human propensity for joy (Freude), as a positive and powerful educational Leitmotiv. It shapes and manages learning as organic growth, driven by human purpose, not as augmentation with dead matter or coercive molding. It supports life-long learning, by emancipating curiosity from greed, fear or narrow pragmatism. Thereby, it enables healthy attitudes, literacy and genuine competence towards technology. | 换言之，"脑友好学习"培养了人类对快乐（Freude）的倾向，将其作为一种积极而强大的教育主旋律（Leitmotiv）。它将学习塑造和管理为由人类目的驱动的有机增长，而非用死物质进行增强或强制塑造。它通过将好奇心从贪婪、恐惧或狭隘的实用主义中解放出来，支持终身学习。从而使人对技术形成健康的态度、素养和真正的能力。 |
| As in the invention of previous tools, such as the pocket calculator or the laptop computer, ontological confusion and the lure of convenience can challenge a society’s maturity to manage innovation in its best interest. The attention of discourse, market-forces and regulation is often distracted from long-term welfare to short-term promises. Along these lines, foresight and responsibility in innovation and implementation of technologies have not gathered strength and strategy for a reasonable discourse. | 与以前的工具发明（如袖珍计算器或笔记本电脑）一样，本体论上的混淆和便利的诱惑可能挑战一个社会管理创新以符合其最大利益的成熟度。话语、市场力量和监管的注意力往往从长期福利转向短期承诺。沿着这些路线，技术创新和实施中的远见和责任并未为合理的话语聚集力量和策略。 |
| As Australian researchers of ‘Education Futures’ recently observed, responding to an MIT-study of early epidemiological data on ‘AI’ in education that warned about significant risks from perceived epistemic frustration and even ‘brain rot’, that, ‘AI can indeed be detrimental. Students can for the most part offload critical engagement with learning to AI, which results in „metacognitive laziness”. However, just like calculators, AI can and should help us accomplish tasks that were previously impossible – and still require significant engagement. For example, we might ask teaching students to use AI to produce a detailed lesson plan, which will then be evaluated for quality and pedagogical soundness in an oral examination’. | 正如澳大利亚"教育未来"研究者最近在回应一项关于教育中"人工智能"的早期流行病学数据的麻省理工学院研究时所观察到的——该研究警告存在感知到的认识论挫败甚至"大脑退化"的重大风险——"人工智能确实可能是有害的。学生在大多数情况下可以将与学习的批判性参与外包给人工智能，这导致了'元认知懒惰'。然而，就像计算器一样，人工智能能够而且应该帮助我们完成以前不可能完成的任务——而这些任务仍然需要大量的参与。例如，我们可能要求师范生使用人工智能制作详细的课程计划，然后在口试中评估其质量和教学合理性。" |
| This discussion can help to de-dramatize, inform and recalibrate the debate while clearly advocating better understanding of the entire field of practice. It will not remain merely apologetic, when the ‘accomplished tasks’ have a value that justifies such ‘significant engagement’, not as an end in itself but for commonly acceptable reasons. A social perspective moderated by philosophical enquiry can refine the understanding of what ‘AI’ is and ‘does’, in human terms. Connectivity of the relevant disciplines and perhaps even industries and governance bodies can be strengthened, in order to put innovation under legitimate societal control, so as to govern the economy, the justice and the dignity in using and controlling ‘AI’. | 这一讨论可以帮助去戏剧化、告知和重新校准辩论，同时明确倡导更好地理解整个实践领域。当"完成的任务"具有证明这种"重大参与"合理性的价值时——不是作为自身目的而是出于普遍可接受的理由——它就不会仅仅是辩护性的了。一种由哲学探究调节的社会视角可以细化对"人工智能"是什么以及用人类术语来说它"做了什么"的理解。可以加强相关学科乃至产业和治理机构的连接性，以将创新置于合法的社会控制之下，从而治理使用和控制"人工智能"中的经济、正义和尊严。 |
| The ambiguous language of technology, combined with commercial hype can blur the acute attentiveness, on the side of teachers, to listen to and empathize with students. Students need caring, truthful and helpful guidance, learning when to use their own minds, eyes and hands, and when to use machines. Responsible language has been neglected in the ways we talk about the technology and ourselves, for example when we are confusing play and games, performance and output measure, same and similar. The deepest source for connectivity when making ‘AI’ serve humanity, is to learn from language, namely as embedded deep systems of knowledge, including natural, social, mathematical and biological languages, which all combined contribute to culture. | 技术的模糊语言与商业炒作相结合，可能模糊教师倾听和与学生共情的敏锐注意力。学生需要关怀的、真实的和有帮助的指导，学习何时使用自己的头脑、眼睛和双手，何时使用机器。在我们谈论技术和自身的方式中，负责任的语言被忽视了，例如，当我们混淆游戏与玩耍、表现与产出衡量、相同与相似时。使"人工智能"服务于人类时，最深层的连接性源泉是向语言学习，即作为知识的深层嵌入系统，包括自然、社会、数学和生物语言，它们共同为文化做出贡献。 |
| Going back to simple philosophical language, a scientific attitude to language will make sure that, while ‘I talk about what I know, at the same time, I know what I talk about, and how, considering the purpose’. The coherence of intention and action, theory and practice, name and object cannot be taken for granted, but can be supported from the above-mentioned contributors to culture. | 回到简单的哲学语言，一种科学的语言态度将确保，"在我谈论我所知道的同时，我知道我在谈论什么以及如何谈论，考虑到目的。"意图与行动、理论与实践、名称与对象的连贯性不能被视为理所当然，但可以从上述文化贡献者那里得到支持。 |
| 3 Conclusion | == 3 结论 == |
| What can we learn from this discussion? First, about the attitude that goes together with perception and expectations. This enquiry is inspired not by fear but reasonable hope and idealism. It is based upon the assumption that a proper understanding of ‘AI’ is possible, that is, a holistic, socially meaningful and normatively instructive understanding. Second, about language, as a living body of expressive symbolic meaning, that can connect nature, technology and culture, especially through its mathematical features. Third, about society, as the habitat and laboratory of understanding, not just a market or an object of governance, but a participant in the quest for the understanding that matters. Fourth, about learning, as a constructive, playful extension of rules-knowledge, that embed agency in language and open access for pedagogy to individualize cultivation. Fifth, about technology, as a set of tools, which humans have created without fully anticipating the properties and consequences, and hence need to learn mastering it for human purposes, in particular by allowing time to mature and inspiration for alternative approaches. Sixth, about psychology of self-reliance, joy, empathy and collaboration, to first trust humanity and not attach faces to machines, understanding that, different from reasonable confidence regarding functionality and probability‚ trust cannot apply to machines. Engineers can be trustworthy and trusted, their constructions can, hopefully, be relied upon. Seventh, about pedagogy, we learn to be prepared for the prospective continuum of dealing with known and unknown unknowns, so as to manage risks-related decision-making in increasingly complex environments. Eights, about caring, we understand the need to upgrade our attention and rehabilitate the importance of the ubiquitous human factor. Inter-disciplinary competence is can enable society to make sense and take responsible action regarding ‘AI’. Namely, the nature of the matter must be properly established, normative stakes must be fully described, so that practical judgements regarding specific ‘AI’ issues are well grounded. | 我们能从这场讨论中学到什么？第一，关于与感知和期望相伴的态度。这一探究不是出于恐惧，而是出于合理的希望和理想主义。它基于这样的假设：对"人工智能"的恰当理解是可能的，即一种整体的、具有社会意义的和规范性指导意义的理解。第二，关于语言，作为表达性符号意义的活体，它可以连接自然、技术和文化，特别是通过其数学特征。第三，关于社会，作为理解的栖息地和实验室，不仅仅是市场或治理对象，而是参与重要理解追求的参与者。第四，关于学习，作为规则知识的建设性、玩耍性扩展，将能动性嵌入语言中，并为教育学开放个性化修养的途径。第五，关于技术，作为一套人类创造但未完全预见其属性和后果的工具，因此需要学习为人类目的掌控它，特别是通过给予成熟的时间和替代方法的灵感。第六，关于自我依赖、快乐、共情和合作的心理学，首先信任人性而不是给机器贴上面孔，理解不同于关于功能性和概率的合理信心，信任不能适用于机器。工程师可以是值得信赖和被信任的，他们的构造可以——希望如此——被依赖。第七，关于教育学，我们学会为应对已知和未知的未知事物的前瞻性连续体做准备，以便在日益复杂的环境中管理与风险相关的决策。第八，关于关怀，我们理解了需要提升我们的关注度并恢复无处不在的人类因素的重要性。跨学科能力能够使社会对"人工智能"产生意义并采取负责任的行动。即，事物的本质必须被恰当地建立，规范性利害关系必须被充分描述，以便关于特定"人工智能"问题的实际判断建立在坚实的基础之上。 |
| To conclude this programmatic brief, what matters most, regarding the meaning of AI in teaching is the following. A thoroughly innovative discussion of technology, learning and language is overdue, involving all cultures. After decades of systemic forgetfulness about the demand for holistic and universal integration of eutrophic and dystopic growth of specific, positive and technical kinds of knowledge, and in particular of institutional monoculture and general ignorance of the role of language in science, the mathematical structure and linguistic substance of AI technologies now become even more tangibly practical, than information- and biotechnologies, which had taken the lead over the past six decades of globalized impact on world economies. In fact, economy is the social area, where science and education meet, in order, ideally, to go hand in hand, or else, to propel alienation and injustice between humanity and our products. | 总结这篇纲领性简报，关于人工智能在教学中的意义，最重要的是以下几点。一场涉及所有文化的关于技术、学习和语言的彻底创新讨论早已是当务之急。在数十年来系统性地遗忘了对营养性和反营养性增长的特定、实证和技术知识的整体和普遍整合的需求之后，特别是在制度单一文化和对语言在科学中作用的普遍无知的背景下，人工智能技术的数学结构和语言实质现在变得比过去六十年全球化影响世界经济的信息技术和生物技术更为切实地实用。事实上，经济是科学和教育相遇的社会领域，理想地携手共进，否则就会在人类与我们的产品之间推动异化和不公正。 |
| Learning theories hinge upon who we want to be (anthropology), as technology drivers. The best role of AI in teaching depends upon the relationship we define regarding technologies: to use or to serve them, means to first decide whether to dare maturity. Do we trust in our human condition, or feel shame in front of our creations? Thereby, ‘AI’ becomes a matter of morality, knowledge and style. | 学习理论取决于我们想成为谁（人类学），作为技术驱动者。人工智能在教学中的最佳角色取决于我们关于技术所定义的关系：使用还是服务于它们，意味着首先决定是否敢于成熟。我们是信任人类的处境，还是在自己的创造物面前感到羞愧？由此，"人工智能"成为道德、知识和风格的问题。 |
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| 第一部分：基础与框架（续） |