Training and Preparing Tomorrow’s Workforce for the Fourth Industrial Revolution
Michael Max Bühler, Thorsten Jelinek, Konrad Nübel
Technical University of Munich
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
We call for a paradigm shift in engineering education. We are entering the era of the Fourth Industrial Revolution (“4IR”), accelerated by Artificial Intelligence (“AI”). Disruptive changes affect all industrial sectors and society, leading to increased uncertainty that makes it impossible to predict what lies ahead. Therefore, gradual cultural change in education is no longer an option to ease social pain. The vast majority of engineering education and training systems, which have remained largely static and underinvested for decades, are inadequate for the emerging 4IR and AI labour markets. Nevertheless, some positive developments can be observed in the reorientation of the engineering education sector. Novel approaches to engineering education are already providing distinctive, technology-enhanced, personalised, student-centred curriculum experiences within an integrated and unified education system. We need to educate engineering students for a future whose key characteristics are volatility, uncertainty, complexity and ambiguity (“VUCA”). Talent and skills gaps are expected to increase in all industries in the coming years. The authors argue for an engineering curriculum that combines timeless didactic traditions such as Socratic inquiry, mastery-based and project-based learning and first-principles thinking with novel elements, e.g., student-centred active and e-learning with a focus on case studies, as well as visualization/metaverse and gamification elements discussed in this paper, and a refocusing of engineering skills and knowledge enhanced by AI on human qualities such as creativity, empathy and dexterity. These skills strengthen engineering students’ perceptions of the world and the decisions they make as a result. This 4IR engineering curriculum will prepare engineering students to become curious engineers and excellent collaborators who navigate increasingly complex multistakeholder ecosystems.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
工程Engineering Education and Curriculum Development
Engineering Education and Pedagogy · Engineering Education and Global Impact
参考文献 66
此处列出前 3 条
引用本文 130
按被引量排序,此处列出前 3 条