人工智能对工程学与教的影响

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education Pub Date : 2025-07-30 DOI:10.1109/TE.2025.3584007

Pablo Guedes;Eduardo Abranches Silva Lopes;P. F. Ribeiro;A. C. Zambroni de Souza

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引用次数: 0

摘要

本文探讨了人工智能（AI）、教育、工程的交叉领域，以及它们所带来的相关风险。它突出了人工智能在教育中的变革潜力，实现了个性化学习和创新教学方法。在工程领域，人工智能为流程优化、设计创新和虚拟实验室提供了机会，可以增强工程实践和教学。为了更好地理解人工智能是如何被使用和感知的，我们还研究了一项关于人工智能和学术数据库研究的调查，以更好地了解公众是如何看待这项新技术的，以及这些领域的出版趋势是什么。本文强调了我们在社会中实施人工智能可能面临的问题，并提供了人工智能的潜力、风险以及在教育、工程和社会中负责任实施的必要性的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The Impact of Artificial Intelligence on Learning and Teaching of Engineering

This article explores the intersecting domains of artificial intelligence (AI), education, engineering, and the associated risks they entail. It highlights the transformative potential of AI in education, enabling personalized learning and innovative teaching methodologies. In the field of engineering, AI offers opportunities for process optimization, design innovation, and virtual laboratories, that can enhance both the practice and the teaching of engineering. To better comprehend how AI is used and perceived, a survey concerning AI and research in academic databases are also examined to better understand how the public is perceiving this new technology and what are the trends in publishing within these fields. This article highlights the problems we may face with the implementation of AI in our society and provides insights into AI’s potential, risks, and the need for responsible implementation in education, engineering, and society.

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来源期刊

IEEE Transactions on Education 工程技术-工程：电子与电气

CiteScore

5.80

自引率

7.70%

发文量

审稿时长

1 months

期刊介绍： The IEEE Transactions on Education (ToE) publishes significant and original scholarly contributions to education in electrical and electronics engineering, computer engineering, computer science, and other fields within the scope of interest of IEEE. Contributions must address discovery, integration, and/or application of knowledge in education in these fields. Articles must support contributions and assertions with compelling evidence and provide explicit, transparent descriptions of the processes through which the evidence is collected, analyzed, and interpreted. While characteristics of compelling evidence cannot be described to address every conceivable situation, generally assessment of the work being reported must go beyond student self-report and attitudinal data.