Kompetenzprofile von Ingenieurinnen und Ingenieuren im digitalen Wandel

Heidling, Eckhard; Neumer, Judith

doi:10.1007/978-3-662-62866-9_7

Cited by 2 publications

(14 citation statements)

References 2 publications

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“…Scientific studies, as well as reports of associations, were considered and results from German-speaking as well as English-speaking countries were used. In total, nine different sources [2,3,4,5,6,7,8,9,10] were examined, which are also mostly based on literature surveys and qualitative interviews themselves.…”

Section: Methodsmentioning

confidence: 99%

“…The value of digital innovation and data-driven processes lies in the intersection of disciplines. Thus, more than ever, strong interdisciplinary learning is required, integrating competencies across the boundaries of disciplines [2,3].…”

Section: Motivationmentioning

confidence: 99%

“…Recent studies have investigated changing competence requirements in the engineering discipline [2,3,4,5], competence requirements for developing and working with AI [6,7,8,9], and general future skills [10]. However, there is currently no work on which competencies are required across the boundaries of disciplines to work and develop solutions at the intersection of AI and engineering.…”

Section: Motivationmentioning

confidence: 99%

“…Developing novel, innovative solutions and questioning the "status quo" of processes and ideas. Furthermore, some authors explicitly state the understanding of AI technologies, in particular Machine Learning (ML) [2,6,7,8]. In this context, it is important to understand the mathematical foundations, such as statistical knowledge, as well as the benefits and limitations of models [2,8].…”

Section: Creativitymentioning

confidence: 99%

“…Furthermore, some authors explicitly state the understanding of AI technologies, in particular Machine Learning (ML) [2,6,7,8]. In this context, it is important to understand the mathematical foundations, such as statistical knowledge, as well as the benefits and limitations of models [2,8]. Furthermore, understanding and using the currently available tools and libraries is mentioned [8].…”

Section: Creativitymentioning

confidence: 99%

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The use of Artificial Intelligence (AI) in engineering is on the rise and comes with the promise of cost reductions and efficiency gains. However, classical engineers often lack the necessary skills to implement data-driven solutions. At the same time, computer scientists lack the required understanding of engineering systems. Thus, we need to extend the current set of competencies of engineers across the boundaries of disciplines to include competencies of Artificial Intelligence as well as skills necessary for interdisciplinary work. In this paper, we propose a competence profile of a so-called AI Engineer that combines the expertise of AI systems in the context of engineering. Based on perspectives from literature and interviews with experts from industry and research, we highlight the most important set of competencies across the professional, methodological, social, and selfcompetencies. The contributions of our paper can act as a reference point for developing and advancing future engineering curricula. Furthermore, it serves as a guide for professional self-development.

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Section: Methodsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%