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

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Design Methodologies 2

Tagged Division

Design in Engineering Education

Page Count

18

DOI

10.18260/1-2--35326

Permanent URL

https://peer.asee.org/35326

Download Count

71

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Paper Authors

biography

Cheng Chen University of Georgia Orcid 16x16 orcid.org/0000-0001-9017-9195

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Cheng Chen is a first-year Ph.D. student at the University of Georgia supervised by Dr. Morkos. Cheng received his bachelor from Central College of BUPT in Beijing and a master's degree from Florida Institute of Technology. His doctoral research interest is in using heuristic methods to study and understand the evolution of requirement networks in industrial system design. He also studies the impact of AI on engineering design education.

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Toluwalase Oluwagbemileke Olajoyegbe University of Georgia

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Toluwalase is a graduate researcher whose interest lies in product development and industrial design. His primary research focus looks at advancements in manufacturing methods and engineering education. He has experience in the automotive industry working as a Prototype Build Engineer at Fait Chrysler Automobiles and holds a B.S. in Mechanical Engineering from the Florida Institute of Technology.

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Beshoy Morkos University of Georgia

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Beshoy Morkos is an associate professor in the College of Engineering at the University of Georgia. His lab currently performs research in the areas of system design, manufacturing, and their respective education. His engineering design research focuses on developing computational representation and reasoning support for managing complex system design. The goal of Dr. Morkos’ design research is to fundamentally reframe our understanding and utilization of system representations and computational reasoning capabilities to support the development of system models which help engineers and project planners intelligently make informed decisions at earlier stages of engineering design. On the engineering education front, Dr. Morkos’ research explores means to enhance engineering education, improve persistence in engineering, and address challenges in senior design education. Dr. Morkos’ research is supported by government [National Science Foundation (NSF), Office of Naval Research (ONR), United States Navy, NASA Jet Propulsion Laboratory (JPL)] and industry [Blue Origin, Lockheed Martin, Sun Nuclear, Northrop Grumman, Rockwell Collins, PTC, Alstom].

Dr. Morkos received his Ph.D. from Clemson University. In 2014, he was awarded the ASME CIE Dissertation of the year award for his doctoral research. He graduated with his B.S. and M.S in Mechanical Engineering in 2006 and 2008 from Clemson University and has worked on multiple sponsored projects funded by partners such as NASA, Michelin, and BMW. His past work experience include working at the BMW Information Technology Research Center (ITRC) as a Research Associate and Robert Bosch Corporation as a Manufacturing Engineer. Dr. Morkos was a postdoctoral researcher in the Department of Engineering & Science Education at Clemson University performing NSF funded research on engineering student motivation and its effects on persistence and the use of advanced technology in engineering classroom environments. Dr. Morkos’ research thrust include: design automation, design representations, computational reasoning, systems modeling, engineering education, design education, collaborative design, and data/knowledge management.

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Abstract

This paper serves as a survey of the impact Artificial Intelligence has had and will have on engineering design education. Artificial intelligence has helped designers realize solution and concepts that were once not attainable or considered. The ability for artificial intelligence to develop solutions that are Pareto Optimal yet not reachable by human designers has opened the doors to computational design approaches. Computational Design approaches such as generative design – whereby an artificial intelligent agents makes design decisions based on historical or live data – will have a significant role in how designers interact and utilize computational resources. Currently, little is known about how to incorporate artificial intelligence in design education courses. Further, most educators implore students to seek out solutions instead of using computational resources to determine an optimal solution that would otherwise not be attainable. While there is no debate on the positive impact of artificial intelligence on yielding design solution, there is a fundamental research gap in understanding how and when artificial intelligence tools should be used in design education. Similar to the paradigm shifts experienced with the advancement of technology over the years, from calculators to computers to coding, Artificial Intelligence will create a paradigm shifts in the way students are taught – and the way they learn. This paper will present a survey of research (both separate and intersectional) performed in the artificial intelligence and computational design domains to domains in an attempt to predict what tomorrow’s design environment will look like and how this will impact how we educate future designers.

Chen, C., & Olajoyegbe, T. O., & Morkos, B. (2020, June), The Imminent Educational Paradigm Shift: How Artificial Intelligence will Reframe how we Educate the Next Generation of Engineering Designers Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35326

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