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Volume 58 Issue 1
January 2026
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Citation: LIU Chuang and LIU Sijia. Conceptual Engineering: A Way of Understanding Mind and Intelligence in the Future[J]. Academic Monthly, 2026, 58(1): 11-19. shu

Conceptual Engineering: A Way of Understanding Mind and Intelligence in the Future

  • This paper discusses a significant recent shift in the field of philosophy-the transition from conceptual analysis to conceptual engineering. This represents a move from "armchair philosophy" to an approach that incorporates empirical sciences The paper argues that, in the era of artificial intelligence, conceptual engineering has become particularly crucial. Many traditional concepts, such as intelligence, knowledge, consciousness, autonomy, and value, have become ambiguous in both intension and extension. The method of conceptual engineering involves clarifying the reasonable connotations and practical extensions of concepts across various applied domains, using "family resemblance" as an integrative principle to "refine and process" concepts in need of expansion. This results in new representational tools for thinking, reasoning, judging, communicating, and disseminating ideas. In the field of artificial intelligence, conceptual engineering will help clarify the essential distinctions between natural and artificial intelligence, appropriately apply relevant concepts to reflect on and evaluate new phenomena and issues arising from human-machine interaction, and thereby aid in recognizing and leveraging the respective strengths of natural and artificial intelligence.
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    1. [1]

      WANG Qian . AI-Generated Content and Copyright Liability: Paradoxes and Resolutions. Academic Monthly, 2026, 58(8): 118-132.

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      LIN Xifen ,  FU Zhangyi . How Does Public Trust in the Judiciary Shape Acceptability of Judges' AI-assisted Adjudications——An Empirical Study Based on Questionnaire Data. Academic Monthly, 2026, 58(8): 133-144.

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      YU Jianxing ,  LIU Yuxuan . Beyond the Outside Observer: Repositioning the Social Sciences in the Era of Large Language Models. Academic Monthly, 2026, 58(7): 77-89.

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      YING Qi . Hegel's Conception of Reconciliation and the Quarrel of the Ancients and the Moderns. Academic Monthly, 2026, 58(7): 23-31.

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      YOU Yang . How Does AI Understand Language Meaning. Academic Monthly, 2026, 58(8): 16-28.

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      HE Da'an ,  LI Huaizheng . Digital Adjustment Mechanism in the Application of Artificial General Intelligence. Academic Monthly, 2026, 58(7): 54-63.

    7. [7]

      WAN Yong ,  LI Yalan . Realizing the Function of Copyright Law in the Era of Artificial Intelligence. Academic Monthly, 2026, 58(7): 104-116.

    8. [8]

      ZHANG Yi . Cognitive Transformation in Historical Research in the Age of AI——“Fluency-Induced Occlusion” in LLM-Assisted Reading of Diplomatic Manuscripts. Academic Monthly, 2026, 58(8): 29-40.

    9. [9]

      XU Xiangdong . Consciousness and The Moral Status of AI Agents. Academic Monthly, 2026, 58(8): 5-15.

    10. [10]

      CHEN Yinchi ,  WU Siqi . Interaction and Integration between Buddhist and Daoist Hagiographies in Medieval China. Academic Monthly, 2026, 58(7): 157-169.

    11. [11]

      QIN Zhongliang . Historical Views and Historical Facts: The Evolution of Perceptions of Fanzhen since the Song Dynasty. Academic Monthly, 2026, 58(7): 181-194.

    12. [12]

      SHEN Yifei . Theoretical Genealogies of the Emotional Economy and the Chinese Context. Academic Monthly, 2026, 58(7): 129-142.

    13. [13]

      ZHANG Jun ,  FANG Hongsheng . Intergovernmental Fiscal Relations and Economic Growth. Academic Monthly, 2026, 58(8): 70-90.

    14. [14]

      ZHOU Mujun . Between Theory and Empirical Facts: Habermas and the Sociological Debate on the Public Sphere. Academic Monthly, 2026, 58(8): 157-170.

    15. [15]

      LU Mingjun . Simulacra, Pattern Recognition, and Computational Formalism——Reflections on the Application of AI in Artistic Practice and Art History Research. Academic Monthly, 2026, 58(8): 41-55.

    16. [16]

      ZHAO Lin ,  ZHANG Boyu ,  LIANG Hua . The Relationship Between Monopoly and Innovation in Information Product Markets——A Growth Network Perspective. Academic Monthly, 2026, 58(7): 64-76.

    17. [17]

      LIAO Han . Popular Religion and the Formation of Local Culture——Research on the Belief in Ge Xian'gong in Northeastern Jiangxi since the Song and Yuan Dynasties. Academic Monthly, 2026, 58(7): 195-205.

    18. [18]

      RAN Gaoran . The Evolution and Game of Property Systems——On the Paradigm Choice for Data Property. Academic Monthly, 2026, 58(7): 117-128.

    19. [19]

      LI Ming ,  XU Haiqi ,  XU Changtuo . A Statistical Framework and Measurement Results for Public Finance Investing in People. Academic Monthly, 2026, 58(8): 91-105.

    20. [20]

      YANG Rui . Five Planet Conjunctions: Reinterpretation of the Celestial Phenomenon in 1007. Academic Monthly, 2026, 58(8): 207-216.

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        Conceptual Engineering: A Way of Understanding Mind and Intelligence in the Future

        Abstract: This paper discusses a significant recent shift in the field of philosophy-the transition from conceptual analysis to conceptual engineering. This represents a move from "armchair philosophy" to an approach that incorporates empirical sciences The paper argues that, in the era of artificial intelligence, conceptual engineering has become particularly crucial. Many traditional concepts, such as intelligence, knowledge, consciousness, autonomy, and value, have become ambiguous in both intension and extension. The method of conceptual engineering involves clarifying the reasonable connotations and practical extensions of concepts across various applied domains, using "family resemblance" as an integrative principle to "refine and process" concepts in need of expansion. This results in new representational tools for thinking, reasoning, judging, communicating, and disseminating ideas. In the field of artificial intelligence, conceptual engineering will help clarify the essential distinctions between natural and artificial intelligence, appropriately apply relevant concepts to reflect on and evaluate new phenomena and issues arising from human-machine interaction, and thereby aid in recognizing and leveraging the respective strengths of natural and artificial intelligence.

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