生成式人工智能:开创智能能源系统研究和教育的新范式

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaojie Lin , Zheng Luo , Liuliu Du-Ikonen , Xueru Lin , Yihui Mao , Haoyu Jiang , Shuai Wang , Chongshuo Yuan , Wei Zhong , Zitao Yu
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引用次数: 0

摘要

促进低碳能源系统作为全球可持续发展目标的核心至关重要。作为低碳转型的一部分,智能能源系统一直是人工智能(AI)与能源科学交叉的一个活跃的研究和教育领域。这是一个新兴领域,随着新知识的不断涌入,研究和教育面临着新的挑战。在这一过程中,生成式人工智能(GAI)在教育和研究活动中发挥着至关重要的作用。然而,GAI对智能能源系统研究和教育的影响却很少被讨论。特别是,与研究相比,它对教育的影响很少被讨论。GAI重塑了研究过程以及教师和学生在课程中的角色。这一观点提供了对智能能源系统中正在进行的研究和教育范式转变的见解。这一视角综合了智能能源系统领域现有的“科学GAI”和“教育GAI”实践研究。在研究中,从宏观和微观两个层面探讨了GAI的影响。在教育方面,这一视角考察了与传统方法相比,人工智能驱动的教学方法如何解决智能能源系统教学的挑战。这一观点可能有利于讨论人工智能重塑的能源科学研究和教育。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative artificial intelligence: Pioneering a new paradigm for research and education in smart energy systems
Promoting low-carbon energy systems as a centerpiece of global sustainable development goals is essential. As part of this low-carbon transition, smart energy systems have been an active area of research and education, where artificial intelligence (AI) intersects with energy science. It is an emerging area where research and education face new challenges as new knowledge keeps coming in. During this process, generative artificial intelligence (GAI) plays a critical role in education and research activities. However, GAI's impact on smart energy systems research and education is less discussed. Especially, its impact on education is rarely discussed when compared to research. GAI reshapes both the research process and the roles of teachers and students in the course. This perspective offers insights into the ongoing research and education paradigm shifts observed in the smart energy system. This perspective synthesizes existing studies on "GAI for Science" and "GAI for Education" practices in the field of smart energy systems. In research, the impact of GAI is discussed from both macro and micro levels. In education, this perspective examines how a GAI-driven teaching approach addresses the challenges of teaching smart energy systems compared to the traditional approach. This perspective could benefit the discussion of GAI-reshaped research and education in energy science.
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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