The Imminent Educational Paradigm Shift: How Artificial Intelligence will Reframe how we Educate the Next Generation of Engineering Designers

Chen, Cheng; Olajoyegbe, Toluwalase; Morkos, Beshoy

doi:10.18260/1-2--35326

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…Chen et al (2020) [32] Sheds light on the transformative influence of AI in engineering design education, enabling the realization of previously unattainable solutions and concepts.…”

Section: Ai In Design and Collaborationmentioning

confidence: 99%

Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis

Bahroun,

Anane,

Ahmed

et al. 2023

Sustainability

174

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In the ever-evolving era of technological advancements, generative artificial intelligence (GAI) emerges as a transformative force, revolutionizing education. This review paper, guided by the PRISMA framework, presents a comprehensive analysis of GAI in education, synthesizing key insights from a selection of 207 research papers to identify research gaps and future directions in the field. This study begins with a content analysis that explores GAI’s transformative impact in specific educational domains, including medical education and engineering education. The versatile applications of GAI encompass assessment, personalized learning support, and intelligent tutoring systems. Ethical considerations, interdisciplinary collaboration, and responsible technology use are highlighted, emphasizing the need for transparent GAI models and addressing biases. Subsequently, a bibliometric analysis of GAI in education is conducted, examining prominent AI tools, research focus, geographic distribution, and interdisciplinary collaboration. ChatGPT emerges as a dominant GAI tool, and the analysis reveals significant and exponential growth in GAI research in 2023. Moreover, this paper identifies promising future research directions, such as GAI-enhanced curriculum design and longitudinal studies tracking its long-term impact on learning outcomes. These findings provide a comprehensive understanding of GAI’s potential in reshaping education and offer valuable insights to researchers, educators, and policymakers interested in the intersection of GAI and education.

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“…Chen et al (2020) [32] Sheds light on the transformative influence of AI in engineering design education, enabling the realization of previously unattainable solutions and concepts.…”

Section: Ai In Design and Collaborationmentioning

confidence: 99%

Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis

Bahroun,

Anane,

Ahmed

et al. 2023

Sustainability

174

View full text Add to dashboard Cite

show abstract

“…As industry 4.0 (Jiao et al, 2021), the future of work and the potential for human-technology collaboration has received growing attention, there has been increased interest in how AI and associated smart technologies can be used to support design, both in terms of practitioners (Zhang et al, 2021;Song et al, 2020;Khan and Awan, 2018) and learners (Schimpf et al, 2019;Chen et al, 2020). LaDDR platforms open up new opportunities for integrating AI support into design platforms.…”

Section: Technology and Training Challengementioning

confidence: 99%

“…LaDDR platforms open up new opportunities for integrating AI support into design platforms. Much of the existing work that incorporate some form of AI assistance into a design platform have AI that operate on artifact permutations or the state space of design artifacts (e.g., Khan and Awan, 2018;Schimpf et al, 2019;Chen et al, 2020). In contrast, a LaDDR platform could inform AI systems by providing designers' action stream, representing their design activities.…”

Section: Technology and Training Challengementioning

confidence: 99%

Large data for design research: An educational technology framework for studying design activity using a big data approach

Schimpf¹,

Goldstein²

2022

Front. Manuf. Technol.

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The complexity of design problems compels the collection of rich process data to understand designers. While some methods exist for capturing detailed process data (e.g., protocol studies), design research focused on design activities still faces challenges, including the scalability of these methods and technology transformations in industry that require new training. This work proposes the Large Data for Design Research (LaDDR) framework, which seeks to integrate big data properties into platforms dedicated to studying design practice and design learning to offer a new approach for capturing process data. This technological framework has three design principles for transforming design platforms: broad simulation scope, unobtrusive logging and support for creation and analysis actions. The case is made that LaDDR platforms will lead to three affordances for research and education: capturing design activities, context setting and operationalization, and research design scalability. Big data and design expertise are reviewed to show how this approach builds on past work. Next, the framework and affordances are presented. Three previously published studies are presented as cases to illustrate the ways in which a LaDDR platform’s affordances manifest. The discussion covers how LaDDR platforms can address the aforementioned challenges, including advancing human-technology collaboration and how this approach can be extended to other design platforms.

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“…Often, correlations are manually determined by experts based on their heuristic knowledge. By automating this process, engineers and designers would make AI-assisted decisions to provide better designs 6 .…”

Section: Introductionmentioning

confidence: 99%

Bridging the Knowledge Gap Between Design Requirements and CAD - A Joint Embedding Approach

Chen,

Wei,

Morkos

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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Cheng holds a Ph.D. in mechanical engineering from the University of Georgia and has published numerous papers on topics such as computational design, geometric modeling, and engineering education. He is always seeking innovative approaches to fill knowledge gaps and to assist in solving complex design issues. He is currently working on several projects to develop various natural language models for requirement management. Cheng is passionate about applying his domain expertise to improve STEM education, with an emphasis on how AI can be incorporated into design practices.

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The Imminent Educational Paradigm Shift: How Artificial Intelligence will Reframe how we Educate the Next Generation of Engineering Designers

Cited by 3 publications

References 19 publications

Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis

Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis

Large data for design research: An educational technology framework for studying design activity using a big data approach

Bridging the Knowledge Gap Between Design Requirements and CAD - A Joint Embedding Approach

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