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生成式人工智能对人类学习的承诺和挑战。

Promises and challenges of generative artificial intelligence for human learning.

机构信息

Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia.

Department of Behavioral and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg.

出版信息

Nat Hum Behav. 2024 Oct;8(10):1839-1850. doi: 10.1038/s41562-024-02004-5. Epub 2024 Oct 22.

DOI:10.1038/s41562-024-02004-5
PMID:39438686
Abstract

Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation and evaluation of human learning. Here the authors examine the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology and human-computer interaction. GenAI promises to enhance learning experiences by scaling personalized support, diversifying learning materials, enabling timely feedback and innovating assessment methods. However, it also presents critical issues such as model imperfections, ethical dilemmas and the disruption of traditional assessments. Thus, cultivating AI literacy and adaptive skills is imperative for facilitating informed engagement with GenAI technologies. Rigorous research across learning contexts is essential to evaluate GenAI's effect on human cognition, metacognition and creativity. Humanity must learn with and about GenAI, ensuring that it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.

摘要

生成式人工智能(GenAI)有可能改变人类学习的传授、培养和评估方式。本文作者从学习科学、教育技术和人机交互的综合视角,审视了将 GenAI 作为一种人类学习工具的整合,探讨了其从承诺到挑战的各个方面。GenAI 有望通过扩展个性化支持、使学习材料多样化、提供及时反馈以及创新评估方法来增强学习体验。然而,它也带来了模型缺陷、道德困境以及传统评估方式被颠覆等关键问题。因此,培养人工智能素养和适应能力对于促进人们明智地参与 GenAI 技术至关重要。在各种学习环境中开展严谨的研究对于评估 GenAI 对人类认知、元认知和创造力的影响至关重要。人类必须与 GenAI 共同学习并了解其特性,确保其成为追求知识和创新的有力盟友,而不是削弱我们智力能力的拐杖。

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本文引用的文献

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Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews: Comparative Analysis.幻觉发生率和 ChatGPT 与 Bard 用于系统评价的参考准确性:比较分析。
J Med Internet Res. 2024 May 22;26:e53164. doi: 10.2196/53164.
2
Creativity in the age of generative AI.生成式人工智能时代的创造力。
Nat Hum Behav. 2023 Nov;7(11):1836-1838. doi: 10.1038/s41562-023-01751-1.
3
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models.ChatGPT在美国医师执照考试中的表现:使用大语言模型进行人工智能辅助医学教育的潜力。
生成式人工智能在可持续毒理学中的应用、益处及挑战综述
Curr Res Toxicol. 2025 Apr 21;8:100232. doi: 10.1016/j.crtox.2025.100232. eCollection 2025.
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J Biomech Eng. 2015 Feb 1;137(2):024701. doi: 10.1115/1.4029235. Epub 2015 Jan 26.
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Self-regulated learning: beliefs, techniques, and illusions.自我调节学习:信念、技巧和幻象。
Annu Rev Psychol. 2013;64:417-44. doi: 10.1146/annurev-psych-113011-143823. Epub 2012 Sep 27.
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Simulation-based learning in nurse education: systematic review.基于模拟的护理教育学习:系统评价。
J Adv Nurs. 2010 Jan;66(1):3-15. doi: 10.1111/j.1365-2648.2009.05240.x.