Rethinking Higher Education/Chapter 2/en-zh

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Teaching Means – Humanity and AI from a Philosopher’s View	(格式背景是浅蓝色，教学之道 —— 哲学家视角下的人文与人工智能)
Ole Doering	(Ole Döring教授奥乐・德林)
'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’.	(本文结合现有前沿研究，探讨人工智能的本质：它既是人类认知自我的一大挑战，也是教育学需要应对的课题。从哲学视角来看，相关研究思路取决于所选取的人学理论，该理论可融合技术素养的生物学与社会学维度。面对人工智能带来的冲击，人类与生俱来的学习模式，让我们有信心、也有底气顺势革新教育体系。)
'1 Introduction'	(介绍)
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?	(我们该如何理解：人工智能已然成为 21 世纪人类社会的共同议题？这一议题的现实意义究竟如何？人工智能是否已全面普及于高校教学，抑或是才刚刚开始走入日常应用？围绕其适用性与落地应用的各类问题，是放之四海而皆准，还是会因地域环境不同而存在差异？人们对人工智能在教育领域的应用目标与最优实践有着怎样的认知？中欧双方在探索落地标准的过程中，又该如何相互借鉴、取长补短？)
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.	(本文立足于人类与人工智能之间良性的相处模式、理性的看待态度，结合学习的目标、人工智能技术的特性以及人的本质，探讨人工智能在教、学与教育学领域的应用。标题中出现了一个表意多元的概念 ——教学方式。其语义包含多层内涵：教学行为与教学手段（工具、方法）之间的关联；真正意义上的传授知识（而非单纯的说教或机械模仿）；引导学习者合理运用各类教学工具；以及实现优质教学所需的各类载体。我们借助哲学视角梳理人与人工智能的关系，厘清这一概念的多重含义，具体包括探究语言的运用逻辑，以及思维的连贯性与完整性构建。)
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.	(当下各行各业在市场宣传中，都将人工智能当作一个通用标签。更严谨地来说，我们所谈论的其实是针对不同数据类型、为特定用途设计的机器学习算法处理器。“人工” 一词，尤其是它与 “自然”“人文” 的相对关系，内涵关键却尚无统一界定，有待进一步明确。而 “智能” 的释义则更为重要：它不仅体现着我们对这一事物的认知，还关乎自主行为能力。因此，厘清这些核心概念，既是为了明确定义，也是一种责任。这项技术的认知与应用，深刻影响着人类的自我认知、重要社会活动的运作模式，以及最优实践理念。而这些理念又会进一步作用于课程体系、规章制度与社会秩序的构建。)
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.	(对于这项技术及其运行机制、特性该如何恰当命名，相关探讨寥寥无几。语言具备强大的影响力，用词本应传递严谨、真实的内涵，而大众想象又会深刻作用于社会现实，因此这一现状亟待改观。社会各界不能一味漠然接受利益相关方刻意打造的话术。面对广告、政策宣传、推销说辞与商业营销中这类带有诱导性的功利化表达，人们普遍缺乏批判性思考。近期最典型的例子便是元宇宙这一概念：它将网络虚拟场景鼓吹为更高阶的存在形态，用空想取代了纯粹的哲学思辨。如今元宇宙已然遇冷，究其原因，软硬件配套设施不足、平台发展走向垄断、治理规范缺失皆是症结所在。但当下舆论炒作与自负心态依旧盛行，人们又开始把人工智能塑造成一种人类无法参透的全新事物。过往多项技术突破也曾陷入同样的营销套路，比如宣称基因组学将彻底变革医疗、信息技术会颠覆知识体系。这类炒作一方面确实让部分行业与从业者收获了收益；但另一方面，由于预期被过度拔高、认知遭到误导，再加上空洞的承诺难以长久维系，其最终并未给社会福祉、秩序稳定、大众健康与知识发展带来持久且实质的影响。 )
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	(智能被视作理性的一种能力，能在知识体系与感知认知之间自发建立关联，这也是经验活动的组成部分。为保证分析的精准度，文中会使用释义更严谨的德语专业术语。建立关联是人类智能与生俱来的特质，但不能简单等同于认知表现。对人类而言，智能是一种行为与素养，深度融合于感官、知觉、思辨、具身体验、社会交往、共情能力、语言表达、直觉判断、实践学习等方方面面。人类智能兼具不完备性、灵动性、矛盾性、普遍性、重复性与守旧性，同时兼具自发性与实用性。借助简化建模与逐步迭代的模拟手段，我们能够以人工方式复刻智能。相关科技虽取得了惊人进展，但这更应促使我们保持理性、展开审慎研判，而非)
This paper is based on a lecture and takes the form of an annotation to a debate that ought to take place.	(本文源于一场讲座，旨在为一场本应开展的探讨作出评述。)
'2 Humanity in AI'	(AI中的人道)
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.	(语言是理性活动在文化内部与不同文化之间的外在表达，而人类与生俱来的理性天赋，让不同文化得以联结。以拓展知识为目标的学习，正是这类体验的延续；这一过程依托自然语言与思辨性语言活动展开，而教育学则立足人类普遍准则与个体能力，让整个学习实践落地可行。)
'2.1 The human position'	(2.1人类的地位)
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.	(从人类历史发展的视角来看，人工智能是依托数学运算、语言与认知模拟所构建的一套高效隐形工具。理解其内涵，需要结合自然语言与学习活动展开，核心在于探讨如何合理运用这项技术。)
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.	(语言承载着知识文化与人文积淀下的知识。知识文化是一种社会认知环境，它顺应人的本性，倡导、滋养并支撑人类求知活动。人文知识则是人类所学内容自然形成的体系与内涵，始终契合人性特质。技术本质上具有工具属性。无论其智能水平、物质形态与技术复杂度如何，只要被设计并用于服务预设目标，它便属于技术范畴。描述技术时那些表意模糊、常带有比喻色彩的表述，会扰乱人与技术之间的关系，也妨碍人们形成正确认知。这种模糊性根植于技术的工具本体论，通常表现为两种极端：一是过度迷信技术的能力，进而忽视人的主体地位；二是先入为主地自认人类低人一等，臣服于技术虚幻的全能表象，无视其作为器物本身存在的局限。一旦叠加舆论炒作、自大心态或功利性宣传，这种认知混乱会不断加剧，甚至被刻意操控，最终让人类只剩下被动接受与顺从的角色。但我们也可凭借理性思辨予以化解：秉持理智、客观与谦逊的态度，从而实现对技术的合理运用。)
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，我们需要严谨、诚恳且精准的表述，以此清晰认知各类技术，并树立理性态度，对于那些通过模拟运算、语音交互直接影响人类认知的技术而言，尤为如此。借此我们能够破除本体论层面的认知误区，比如将游戏这类消遣活动错误解读为 “体验式学习”；同时也能厘清认识论层面的偏差，例如单纯以最终结果而非全过程表现来评判优劣。简言之，问题要从源头抓起。)
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.	(树立对语言的理性认知，能帮我们建立这样一种立场：谈论所知之事，同时明晰言谈的内容、方式与目的。这意味着，语言是我们客观探讨人工智能的工具，而非空洞的泛泛之谈。面对工具类事物，我们首先要厘清一个问题：在何种情形下、从何种角度出发，信赖机器会比信赖人类更为合理。这就要求双方权责分工明确。我们能否信任人类可以掌控机器，使其服务于人类目标，而非单纯机械地执行预设程序？秉持这样的态度，伦理与科学将相融共生，形成一种谦逊、审慎且用心行事的文化氛围。)
'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.	(和以往众多技术革新一样，人工智能也引发了关于传统生活方式未来走向的讨论。以生成式预训练变换器（GPT）** 为例，在能源与数据供给充足的前提下，它能将语言相关的机械性工作效率提升数个量级，甚至有望彻底改变线上研究以及口笔译工作模式。人们自然也担忧这项技术对人类工作的冲击：传统工作会就此消亡吗？劳动会被机械产出所取代吗？回望过往，这类担忧均源于劳动力市场的实际变化：工作流程中机械重复的环节逐步实现自动化，自动化先是取代了大量蓝领岗位，同时也让专业型、综合型白领岗位的需求有所增加。)
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，人力资源发展策略遵循特定的经济逻辑，而评判价值、制定激励机制的相关政策与政治导向，又决定了这套经济逻辑。社会为创造发展机遇、构筑技术优势投入了大量直接与间接成本，但人们并未合理区分机器的市场价格与经济价值。以机器替代生产、进而引发社会岗位流失的现象，之所以成为常态，部分原因在于相关环节监管缺位、考量不周。人们并未探索如何以更可持续的方式合理运用机器，仅将重心放在为失业人员提供再培训与社会救助上。新技术创造的增值收益、带来的社会成本，本可通过税收、规费等形式进行核算与调节，这一点也完全被忽视。技术迭代加速与衍生社会问题形成的连锁双重影响，其实从一开始就能通过征收替代税、社会规费予以平衡，税费标准可参照社会为技术发展付出的投入来设定。无论采用何种方式，我们都能争取到更多时间，用于学习、研究与规划，进而创造更多社会价值。)
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.	(这也会产生正向的引导作用，消解人们急于逐利的浮躁心态。争取到的时间，也意味着可以重新分配这一最宝贵的资源，扶持社会中各类价值创造者，助力经济实现更可持续的发展。依托人工智能的创新成果，其优势大多体现在效率提升，而非实际效用的优化。若推行上述策略，还会衍生出多种权衡取舍的情形。这种引导作用能够推动大众认知趋于成熟：让人们逐步适应新工具，理性看待其工具属性，并认清其真正价值。倘若缺少这类引导工作，就必须依靠通识教育与专项教育来补足，重点围绕技术应用与经济认知展开。从以人为本、为民服务、不让任何人在民主现代化进程中掉队的理念来看，这一点是显而易见的。)
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.	(在这样的环境下，劳动作为人类生存的必然需求与社会尊严的来源，将重新被视作与教育相辅相成的社会福祉。它能够增进社会凝聚力、遏制新型阶层分化，维系可持续发展。面对层出不穷的市场新动向，我们往往仓促应对，而非以理性姿态开展公开探讨。这意味着，我们不能只停留在修补表面问题、追逐短期机遇或化解眼前矛盾，而需要进行更深层次的思考。这也是本次探讨试图跳出纯粹技术逻辑，重新审视人工智能议题的首要出发点。)
'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.	(广义上的美学，将连贯的感知与规范性判断融为一体，从具体的美的事物升华为美的概念，从善的行为升华为善的概念，亦从真实的表象升华为真的概念；由此，这类概念能以目的论的方式，将学习的导向引向教化这一终极范畴。)
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，这一发展前景依托于人对世界、对自我以及对理性反思的切身体验。人类对完满境界的憧憬虽志向高远，却始终含混不明，唯有结合主体实践体验加以调和、制衡与融合，方能明晰其内涵。美学所能关照的最具包容性的微观载体，便是人的肉身—— 作为教化与育人活动的实践主体。正是在此层面，我们得以区分本源存在与虚拟仿像、负责任的主体行动与纯粹嬉戏、有意义的内在连贯与单纯的逻辑合理、终极旨趣与外在功用、人格尊严与功利价值、劳作行为与泛化活动、人格教化与技能训练。与此同时，我们也可重新审视类比、隐喻、关联、反讽、幽默、暗示等经典间接表达方式所具备的逻辑价值。)
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.	(作为社会性的存在，这样的肉身拥有独有的面貌。我们能否在教育中赋予其真实的人性面貌与价值旨归？即便用仿拟人类看护者的完美虚拟形象来替代真实的人，会显得便利、诱人或是令人慰藉 —— 比如在护理、教育领域使用人形机器人照护弱势群体 —— 这类做法相较用虚伪的假面蒙蔽普通民众，社会风险或许更低，但本质上依然有失公允。即便此举并非出于吝啬，靠这种捷径节省下来的资源，也更应当投入到社会关系的真实建设之中，使之能够适配二十一世纪的社会环境、良性运转。)
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.	(人的容貌与形体本就归属于人类，而非机器。反讽、艺术与喜剧让现代民众懂得，可对万事万物自由戏谑、打破传统禁忌，以此挣脱那些依托外在权威（如政治体制、宗教势力）而形成的人性桎梏。然而，戏谑表达与越界行为，同人类社会的存续发展之间，天然存在着必然张力：社会自由始终受限于他人的自由，这其中也包含对人性与传统的尊重。在技术不断发展的当下，我们必须重新界定包容的边界，避免虚拟仿拟类设计演变为新式的虚假表达。为此，我们应当重拾有关仿拟与真实的传统思想智慧。)
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.	(语言智慧亦是如此。这句话并非为安抚保守观念而故作迁就，而是伽达默尔提出的哲学洞见：“语言是理解得以发生的普遍媒介，而理解的实现方式便是诠释。” 换言之，意义就蕴含在语言的运用之中。这也正是哲学分析方法价值的缘起。)
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.	(谈及数字技术创造，我们不应从智能手机谈起，而要追溯至工具与语言本身—— 二者是数字化进程中兼具物质与思维属性的载体。“数字” 一词源自拉丁语 digitus，本义为 “手指”，也代指手的延伸之物。木棍与石器，标志着人类开始依照自身意志改造物质世界，并由此开拓全新认知边界。这正是人类造物活动的真正开端，即运用双手创造器物。技术的本质，是将既有原始材料、各类信息数据，转化为人工造物与客观事实。理解这一点，有助于我们把握造物学习的核心要义。)
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.	(这一探究也能推动我们进一步梳理学习与人工智能的概念体系，进而打通本体论与认识论。人们以身心协同的方式处理承载意义的符号载体，具体表现为读写活动：借助读写，我们能够构想世界、真、善、美的不同图景，为想象力开辟空间，探索全新路径；人们依循既有规则展开思辨，甚至对既定规则加以检验，文化也由此得以孕育。在口头与书面表达中工具性地运用语言，标志着人类开始具备有目的地跨越时空建立联结的能力，这便是知性学习的开端。与之相伴，误解、过失、曲解与谎言也随之渗入文化生活。而从美学视角出发，我们能够找到可行路径，将各类人工智能应用方式统一纳入健全人格教化的发展脉络之中，并最终落脚于语言的内涵与表达。)
'2.4 Our task'	(2.4 本研究的任务)
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?	(知识的实践形态正在发生转变。对人类而言，数字化属性本就与生俱来。文化是人类与生俱来的技术，文化与技术皆是人性的功能性外在表现。长久以来，随着专业分工不断细化，我们逐渐将技术性学习与源于实践演进的学习割裂开来。这一现象，与广义科学领域各学科中盛行的科学还原论如出一辙。面对这两种状况，我们该如何确保各类知识领域始终秉持人文导向、协调发展？我们能否以合理的方式，打破已然形成的知识壁垒，对其重新整合、融会贯通，并转化为适配二十一世纪的新型综合能力？我们又该如何规划系统性的专业分科学习，使其真正服务于人类的发展演进？)
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.	(人类改造世界的能力已突破宏观与微观的界限，不断催生幻想与虚构，持续激发想象力。过去，哥莱姆、人造侏儒、仿生人、赛博格等形象，代表着人类对自身边界的传统想象；如今，我们亟需契合时代的全新意象。以往的叙事依托有限的经验展开想象，大多归为科幻范畴。而当下，各类技术已在生物、信息处理、化学、物理领域落地应用，我们需要立足全新现实体验，构建新的概念与故事。这类叙事绝不能主要由经济或科技相关利益方主导，他们的话语与诉求并未真正扎根于整个社会。同时，也无法单纯交由专业人士或是依靠社会自然演化来完成，因为我们已然看到，技术研发与市场推广的力量正在消解大众的常识判断。诚然，夸大的恐慌与期许、戏剧化的期待、仓促的市场投放，这些现象由来已久。但智能手机等过早落地的产品所遗留的各类问题，其衍生风险的不确定性正不断加剧；与此同时，数十年来，社会开展理性评估与应对所必需的文化韧性也在持续弱化。)
This is evident in three areas, on different levels.	(这一点可从三个不同层面得以体现。)
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.	(首先，大众对于主体能动性普遍存在认知混淆。在探讨 “人工智能做了什么” 时，必须严格区分语法层面的主、宾语，与现实意义上的行为主体和作用对象。人工智能这类动态复合型人造产物，其存在、功能与发展完全依托于人类的主体行为，无论其运行表现多么贴合预设目标。我们不应赋予人工智能带有浓厚社会人文色彩的语义内涵，比如将学习、思考、决策等能力归之于 AI。对技术的描述应当使用专业术语，而非拟人化的感性标签。将人工智能拟人化的做法，实则是回避了 “机器是否会产生自我意识乃至道德良知” 这一核心问题，还会夸大自动化技术趋于完美的可能性；即便人们普遍秉持先验观点，认定机器绝无可能拥有意识，此类误导依然存在。这种表述偏差如今已不再是无伤大雅的疏漏，因为这项技术的核心目标是提升效率 —— 也就是强化支配力。受其设计逻辑与运行机制影响，微小的问题、故障与风险也会随之被放大。过去仅停留在猜想层面、无关紧要的假设，如今可能引发重大且深远的现实后果。因此，我们必须审慎推敲用语，确保表述严谨恰当。)
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’.	(其次，相较于社会以往接纳各类技术融入的过程，如今具备全新效能与影响力的技术落地节奏愈发仓促、流程也大幅简化。此处用 “落地” 只是委婉说法，因为整个过程已然缺失了应有的社会规范与伦理考量。技术评估、技术伦理、应用信息学等相关学科，以及理解这类新兴技术所需的综合素养，均未被列为主流必修内容。这也就意味着，绝大多数民众实际上对人工智能一无所知。)
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，用机器替代人类劳动本就是技术研发的既定目标。但对此必须进行合理规划，并配套相应举措，兼顾全流程的整体平衡，而非只着眼于投入与收益。前文谈及劳动与生产时所阐述的道理，如今同样适用于语言领域，而这套准则早在数十年前就该应用于经济领域：我们应当坦诚面对一个问题 —— 机器取代传统劳动力后所创造的增值价值，其来源与分配机制究竟如何。)
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.	(这类典型局面需要依靠制度与结构性方案来破解，以此保障相关政策目标落地、实现长效发展。谈及人类素养，人工智能素养是一个贴切的喻称。它并非简单延伸读写、计算的本义，而是要求人们在本体论、经济与基础设施的整体框架下，以完备的学识理解软硬件的符号运作与实体运行。这也正是教育体系改革所要直面的核心任务。跨学科应用课程本就应当作为所有领域（医疗、经济、历史、哲学等）教育的最优范式，予以保留或重建；而人工智能本身作为一个社会领域，又与上述学科深度交融，它完全可以成为推动教育变革的最终落脚点。)
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).	(人类区分能力所及、行为可为、义务所系三者边界的判断力正受到冲击。我们需要重新研读经典中关于审慎行事与务实处世的传统智慧：立足正当初衷行正确之事；做到知行合一；谈及人工智能的本质与内涵时，做到言出由衷、表里如一。从责任维度来看，恪守规范、审慎行事，是数字素养在道德、法律与伦理层面的顶层核心要求。)
'2.5 Pedagogy: How to learn from being prepared for ‘AI’'	(2.5 教学论：面向人工智能时代的预备性学习)
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.	(将人工智能与教学论相关的话语、概念及制度论述，对接当下的实践经验与知识体系，实质上就是协调科学与人性，以此助力人格化培育式学习的开展。)
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.	(这一领域最基础的认知，源自整体性应用学习理论，同时结合以大脑发育为研究对象的神经科学 —— 大脑是人类开展学习的生理载体。人类的学习是融合个体特质与社会因素的综合性体验过程。面对零散的多学科知识，哲学能够阐释其整体实践内涵，帮助我们理解其中要义。)
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).	(谈及学习，原初体验会本能地将具有感知能力的个体与外部环境联结起来。这一过程并非中立行为，而是奠定个人学习历程与认知发展轨迹的基调。这种联结的品质，决定了无意识感知的广度、个体主观的学习方式，以及人在判断时所具备的直觉感悟。具体而言，原初体验是次生经验的前提。次生经验是一系列关联认知活动所构成的意识塑造过程：它依照认知规律组织运作，并借助知性与理性能力，依托概念与原理完成逻辑梳理。)
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.	(现代教育学之父约翰・海因里希・裴斯泰洛齐（1746-1827）提出，学习的外部联结连续体，是 “做中学”实践的系统性延展：从观察模仿，到实操体验，再到职场实践，是人与现实环境循序渐进的联结过程。裴斯泰洛齐也曾历经实践挫折，他由此摆脱神学与技术至上的教条思想，转而认识到数学可作为解读人性的工具，并总结出劳动式学习模式（区别于单纯的生产劳作）。正因如此，他的教育理论时至今日，依然能为人类探究 “如何借助人工智能、认识人工智能” 的最优学习方式，提供参考与思路。)
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.	(他就此阐释道：人工智能所运用的算法、架构与运行逻辑，都应当服务于人类本然的需求。“我的方法不过是复刻自然最朴素的运行规律。”……“人类脑海中每一个深刻的感官认知，都会衍生出一系列与之相近的次生观念，进而将本质相通的事物关联起来。由此，你对事物内在本质的理解会变得愈发广博、敏锐且深刻。” 这类教育能够培养人们的技术素养，帮助人类真正掌控技术。)
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，神经生物学研究以顺应大脑规律的学习为理论，阐释了学习行为内在的关联机制。这一点对人工智能研究颇具参考价值：人工智能的基础神经网络模型，借鉴了生物原型的逻辑自洽性、美感特征、普遍联结能力与可塑性，同时也依托提示词来设定行为导向。由此可见，生物模型与技术模型在研究思路上存在大量共通之处。我们可以对此展开探究，厘清二者的概念关联与发展脉络，最终落脚于人类福祉等核心目标。)
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.	(合理的教学模式兼具效率与实效。它以育人为本，在尽可能减少负面连带影响的同时，最大限度地助力个人成长。从 “顺应大脑规律的学习” 这一神经生物学视角来看，配套课程与教学体系会规避有损认知、误导学习者的干预方式，顺势强化人类与生俱来的学习机制。值得注意的是，顺势强化不同于模拟或简单关联：它主动把控有目的的因果逻辑，无需刻意虚构，也不必依托繁复理论。针对人体有机机能，这类教学充分利用神经可塑性这一学习特质，旨在将教育作为一门人文技艺，引导人们善用各类技术。在理论层面，它摒弃封闭僵化的概念、空想式思潮，也拒绝借羞辱或 “极致苛求式的强硬手段” 达成教育目标。超人类主义支持者所设想的躯体改造手段，便属于此类不当方式。)
‘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.	(“顺应大脑规律的学习” 并非单纯地呵护大脑，反过来也能让我们在求索进取的过程中，感受到身心的从容舒展。它整合人的各项能力，构筑成一个自然的学习核心。这种学习模式首先会唤醒人与环境互动中愉悦的稳态体验，融合人基于本能需求的潜意识反应，以及对因果关系、自身力量与事物关联的初步认知体验。)
‘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.’	(所有新生儿都具备一套先天行为反应模式。当寒冷、饥饿、口渴等因素打破身体稳态时，人体中枢应激系统被激活，这类行为反应也会随之触发。婴儿最早的联想学习体验之一，便是通过自身行动意识到压力源是可以掌控的，这一过程会对大脑发育产生深刻的烙印作用。)
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，顺应大脑规律的学习，将培养人追求愉悦的本能，并以此作为积极且核心的教育主旨。这种学习模式顺应生命成长规律、依托人的主观意愿开展，而非借助无生命外物机械加持，或是采用强制塑造的方式。它让好奇心摆脱贪欲、恐惧与狭隘功利心的束缚，从而助力终身学习。最终帮助人们树立理性心态、掌握相关素养，形成驾驭技术的真正能力。)
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.	(一如掌上计算器、笔记本电脑等过往工具问世时那般，本体认知的混淆与便捷带来的诱惑，会考验整个社会能否成熟地驾驭创新、谋求长远福祉。舆论、市场力量与监管重心，往往从长远福祉转向短期噱头。就此而言，在技术研发与落地环节，远见与责任意识仍未凝聚起足够力量，也未能形成体系化思路，支撑起理性的探讨。)
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’.	(麻省理工的大学一项针对人工智能应用于教育的早期调研数据警示：认知挫败感乃至大脑机能退化等问题已显现，潜藏巨大风险。澳大利亚多位教育未来领域学者就此作出回应，他们表示：人工智能确实会产生负面影响。学生大多会将深度思考、主动钻研的学习任务交由人工智能完成，进而引发元认知惰性。但和计算器一样，人工智能本可以、也应当协助我们完成以往无法实现的工作，而这一过程依旧需要人们深度参与。举例来说，我们可以布置任务，让学生借助人工智能撰写详尽教案，再通过口试考核这份教案的质量与教学合理性。)
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’.	(这场探讨有助于褪去话题的夸张色彩、普及相关认知并重新梳理讨论方向，同时倡导人们全面理解这一实践领域。当借助技术完成的任务具备实际价值，且能让深度参与变得有理有据（参与本身并非最终目的），相关探讨便不再只是一味辩解。结合哲学思辨的社会视角，能帮助我们从人文角度重新厘清人工智能的本质与实际作用。我们还可以加强相关学科乃至行业、管理机构之间的联动，让创新置于合理的社会监管之下，从而在应用与管控人工智能的过程中，维护经济秩序、社会公平与人的尊严。)
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.	(技术相关的模糊表述叠加商业炒作，会削弱教师用心倾听、共情学生的敏锐度。学生需要真诚暖心、切实有效的引导，学会分清何时依靠自身思考、观察与实践，何时借助工具。在谈论技术与自我时，人们渐渐不再使用严谨客观的表达，比如混淆体验学习与娱乐游戏、过程表现与成果指标、等同与相似等概念。想要让人工智能真正服务于人，最核心的联结之道，便是回归对语言的探究。语言是深度内嵌的知识体系，涵盖自然语言、社会语言、数理语言与生物语言，它们交融共生，共同构筑起人类文明。)
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.	(回归质朴的哲学表达：秉持科学的语言观，方能做到言其所知，知其所言，明其方式，晓其初衷。意图与行动、理论与实践、名称与实物之间的统一并非自然而然，而前文提及的各类文明载体，恰好能够维系这份协调一致。)
'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.	(我们能从本次探讨中收获哪些启示？ 第一，关于认知与期许背后的心态。本次研究并非源于恐惧，而是秉持理性的希望与理想主义。我们坚信，人们能够形成对人工智能全面完整、兼具社会价值与规范引导意义的正确认知。 第二，关于语言。语言是承载象征意义的鲜活体系，尤其依托其数理特性，能够贯通自然、技术与文化。 第三，关于社会。社会是认知形成的场域与实践阵地，它不只是市场或是被治理的对象，更是探索真知的参与者。 第四，关于学习。学习是在既有知识体系之上，兼具创造性与趣味性的拓展过程。主体意识根植于语言之中，也让教学得以因材施教。 第五，关于技术。技术是人类创造的工具集合，在发明之初，人们并未完全预见其特性与衍生影响。因此我们必须学会以人为本、驾驭技术：留出时间沉淀成长，同时探索多元化应用思路。 第六，关于心理层面。我们要树立自立、乐观、共情与协作的心态，首先相信人类自身，切勿将人格投射到机器之上。要明白：我们可以理性认可机器的功能与运行概率，但信任并不能施加于机器。工程师值得信赖，他们研发的成果也有望被可靠使用。 第七，关于教育学。教育要做好准备，持续应对已知问题与未知挑战，从而在日趋复杂的环境中，科学开展风险相关决策。 第八，关于人文关怀。我们必须提升专注力，重新重视无处不在的人的价值。 跨学科综合能力，能够帮助社会理性看待人工智能，并作出负责任的行动。简言之，我们要厘清事物本质、明确价值准则，让针对各类人工智能问题的现实判断拥有坚实依据。)
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.	(作为这份纲领性简述的收尾，关于人工智能在教学领域的价值，核心要点总结如下：我们亟需开展一场跨越不同文化、围绕技术、学习与语言展开的深度创新性探讨。数十年来，人们始终忽视一项核心诉求：未能全面统筹整合各类知识的良性发展与负面隐患，也长期陷入体制单一化误区，普遍漠视语言在科学当中的作用。如今，人工智能技术兼具数理架构与语言内核，其现实应用价值，已然超越过去六十年来深刻影响全球经济的信息技术与生物技术。事实上，经济是科学与教育交汇的场域。理想状态下，二者应当协同并进；反之，则会加剧人类与自身造物之间的隔阂，催生不公。)
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.	(学习理论的核心，在于我们作为技术发展的推动者，想要成为怎样的人（这属于人类学范畴）。人工智能在教学中所能发挥的最优作用，取决于我们如何界定人与技术的关系：是使用技术，还是受制于技术。而这首先取决于我们是否敢于走向心智成熟。我们是坚信人的自身价值，还是在自己创造的事物面前心生自卑？由此，人工智能问题最终落脚于道德、认知与行事态度。)
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