The debate over whether "thinking machines" could replace human intellectual labor has existed in both public and expert discussions since the mid-twentieth century, when the concept and terminology of Artificial Intelligence (AI) first emerged. For decades, this idea remained largely theoretical. However, with the recent advent of Generative AI - particularly Large Language Models (LLMs) - and the widespread adoption of tools such as ChatGPT, the issue has become a practical reality. Many fields that rely on human intellectual effort are now being reshaped by AI tools that both expand human capabilities and challenge the necessity of certain forms of work once deemed uniquely human but now easily automated. Education, somewhat unexpectedly, faces a pivotal responsibility: to devise long-term strategies for cultivating human skills that will remain relevant in an era of pervasive AI in the intellectual domain. In this context, we identify the limitations of current AI systems - especially those rooted in LLM technology - argue that the fundamental causes of these weaknesses cannot be resolved through existing methods, and propose directions within the constructivist paradigm for transforming education to preserve the long-term advantages of human intelligence over AI tools.

翻译：自二十世纪中叶人工智能（AI）的概念与术语首次出现以来，关于“思维机器”能否取代人类智力劳动的争论便存在于公众与专家讨论中。数十年来，这一观点主要停留在理论层面。然而，随着近期生成式人工智能——尤其是大语言模型（LLMs）——的出现，以及诸如ChatGPT等工具的广泛采用，这一问题已成为现实。许多依赖人类智力努力的领域正被AI工具重塑，这些工具既扩展了人类能力，也对某些曾被视为人类独有、如今却易于自动化的工作形式提出了挑战。教育领域，在某种程度上出乎意料地，面临着关键责任：制定长期策略，培养在智力领域AI普及时代仍具价值的人类技能。在此背景下，我们指出了当前AI系统——尤其是基于LLM技术的系统——的局限性，论证了这些弱点的根本原因无法通过现有方法解决，并在建构主义范式内提出了教育转型的方向，以保持人类智力相对于AI工具的长期优势。