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    回归与重构:智能时代的新知识观 ——再与陈丽教授等商榷

    Returning and Reconstructing: A New View of Knowledge in the Intelligent Age-A Discussion with Professor Chen Li and Others

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【作      者】:

王 竹 立


【关 键 词 】:

新知识观; 回归论知识观; 重构主义; ChatGPT; 人机合作学习; 复合脑


【栏      目】:

理论探讨


【中文摘要】:

近年来,关于新知识观的讨论日趋热烈。陈丽教授等提出的“回归论知识观”深刻揭示了网络时代知识的回归现象,具有重大的理论和实践意义,但也存在某些值得商榷的问题与局限。人工智能聊天机器人ChatGPT的诞生,对人类的知识和学习带来多方面的影响。为探讨智能时代知识观发生的变化,文章将回归论知识观与重构主义知识观进行了比较分析,指出回归与重构是智能时代知识变化的双向趋势。认为硬知识的重要性进一步下降,软知识的重要性进一步上升;事实性知识和程序性知识的重要性下降,原理性知识的重要性上升;与个人关系不大的知识重要性下降,与个人关系密切的知识重要性上升;外语类知识的重要性下降,本土语言知识的重要性上升;联通在学习中的意义下降,零存整取式学习的价值提升。未来人类将以与生成式AI组成“复合脑”的方式进行人机合作式学习。


【英文摘要】:

In recent years, the discussion about the new view of knowledge has become increasingly heated. The return theory of knowledge proposed by Professor Chen Li and others has profoundly revealed the phenomenon of knowledge regression in the network era, which has great theoretical and practical significance, but there are also some problems and limitations that are worth discussing. The birth of ChatGPT, an artificial intelligence chatbot, has brought about various impacts on human knowledge and learning. To explore the changes occurring in the view of knowledge in the intelligent era, this paper compares and analyzes the return theory of knowledge and the reconstructionist view of knowledge, pointing out that regression and reconstruction are two-way trends of knowledge changes in the intelligent era. It is believed that the importance of hard knowledge will further decline, while the importance of soft knowledge will further rise; the importance of factual and procedural knowledge will decline, and the importance of principled knowledge will rise; the importance of knowledge unrelated to personal relationships will decline, and the importance of knowledge related to personal relationships will rise; the importance of foreign language knowledge will decline, and the importance of native language knowledge will rise; the significance of connectivity in learning will decline, and the value of accumulating knowledge bit by bit will increase. In the future, humans will conduct human-machine cooperative learning by forming a "composite brain" with generative AI.

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