Conference Session

Design in Engineering Education Division (DEED) - Use of Technology in Design Education

Collection

2024 ASEE Annual Conference & Exposition

Authors

Can Uysalel, University of California, San Diego ; Zachary Fox, University of California, San Diego; Maziar Ghazinejad, University of California, San Diego

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Diversity

Tagged Divisions

Design in Engineering Education Division (DEED)

at the forefront of STEM education experiencing the first tides of thischange. An example of such a trend is the course Design of Machine Elements, a mainstay ofMechanical and Aerospace Engineering (MAE) curricula, which embodies many algorithms thatintegrate a combination of scientific topics and industry protocols. In this work in progress, weassigned a class of 62 MAE machine design students to write computer codes that implementseveral required inputs to generate design parameters for shafts used for specific powertransmission parameters. The students were also asked to explore the applicability of an openartificial intelligence interface, such as ChatGPT, to help develop a multi-step design code. Aftergenerating and verifying the AI

Conference Session

Design in Engineering Education Division (DEED) - Use of Technology in Design Education

Collection

2024 ASEE Annual Conference & Exposition

Authors

David Prohofsky, South Dakota School of Mines and Technology; Micah Lande, South Dakota School of Mines and Technology

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Design in Engineering Education Division (DEED)

and making processes to their work. He is interested in the intersection of designerly epistemic identities and vocational pathways. Dr. Lande received his B.S. in Engineering (Product Design), M.A. in Education (Learning, Design and Technology) and Ph.D. in Mechanical Engineering (Design Education) from Stanford University. ©American Society for Engineering Education, 2024 Affordances of Large Language Models in Design ActivityAbstractLarge language models and AI tools such as ChatGPT have possible benefits within designprocess and design activity across design courses in higher education. With the advent and rise inuse of large language models (LLM’s) we seek to better understand the

Conference Session

Design in Engineering Education Division (DEED) - AI and Digital Futures in Design Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Daniel D. Jensen, Westmont College; William Johnston Allison; Camila Rapalo; Mark Rogers; Gregory Reich, Air Force Research Laboratory, Aerospace Systems Directorate; Landon Thomas Vanderhyde

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Design in Engineering Education Division (DEED)

content across a broad spectrum (e.g., texts, images, orprogramming code) for various domains based on basic user prompts” (13). There are a varietyof AI-T that have GenAI capabilities. LLMs such as ChatGPT, OpenAI, and Google Bard can beused to create unique natural language texts for research paper summaries or outlines forexample (8). Meneske explains, “Midjourney and DeepBrain AI are diffusion models that cancreate diagrams (e.g., concept maps), images, and videos from textual or visual inputs.Engineering education, in particular, can benefit from integrating and utilizing generative AItechnologies to improve instructional resources, develop new technology-enhanced learningenvironments, reduce instructors’ workloads, and provide students with

Conference Session

Design in Engineering Education Division (DEED) Technical Session 1

Collection

2023 ASEE Annual Conference & Exposition

Authors

Yun Wang, Undergraduate at University of Illinois Urbana-Champaign; Colin P. Lualdi, University of Illinois Urbana-Champaign; Lawrence Angrave, University of Illinois Urbana-Champaign; Guru Nanma Purushotam

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Diversity

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Design in Engineering Education Division (DEED)

integrated into ScribeAR butother integration projects are possible. For example, ESPnet [21] is also a common speech to textplatform that is an end-to-end speech processing toolkit. It includes various applications such asspeech recognition, text-to-speech, speech translation, and speech enhancement.Recent advances in Large Language Models will also provide new opportunities for inclusiveconversational approaches. For example, a student project might use the new ChatGPT API thatas of May 2023 is now available as part of Microsoft Azure cloud services, to providesummarization or other textual transformation of a transcript [24]t.8. AcknowledgmentsWe thank the VR@Illinois program and the Department of Physics Graduate Office at theUniversity of

Conference Session

Design in Engineering Education Division (DEED) - Best in DEED

Collection

2024 ASEE Annual Conference & Exposition

Authors

Wangda Zhu, University of Florida; Rui Guo, University of Florida; Yuanzhi Wang, Cornell University; Wanli Xing, University of Florida; Eddy Man Kim, Cornell University; Chenglu Li, The University of Utah

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Design in Engineering Education Division (DEED)

can be integrated into CAD education and implies future directions forAI-supported design tools.Introduction In today's educational settings, Generative AI (GAI) has had a significant influence on the fields ofScience, Technology, Engineering, and Mathematics (STEM) education (Cooper, 2023). Among thesetechnological advancements, text-to-text models like ChatGPT have been particularly prominent, ashighlighted by Lo (2023). Furthermore, the impact of GAI extends into design and design education, wherethe advent of image-generative technologies such as Midjourney, DALL-E, and Stable Diffusion marks asignificant shift (Burlin, 2023). These technologies not only streamline the design process but also make iteasier for students to express

Conference Session

Design in Engineering Education Division (DEED) - AI and Digital Futures in Design Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Daniene Byrne Ph.D., Stony Brook University

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Diversity

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Design in Engineering Education Division (DEED)

ofcomputing but nearly every field of science and human endeavor[5]”. Some in the industry haveframed them as the first steps toward Artificial General Intelligence (AGI), meaning systems thatthink more like humans in numerous ways. Like humans, AGI will have the ability to ‘think’about many things across many domains, requiring different recall of datasets and intuition.This literature survey describes how policies around responsible governance are taking shape asstrong AI technologies emerge, and public interaction with them expands exponentially. InNovember of 2022, the first generative AI (GenAI) ChatGPT, created by OpenAI, was widelyreleased to the public. Earlier versions had been in development and were tested and used foryears but the public

Conference Session

Design in Engineering Education Division (DEED) - Emerging and Sustainable Design Practices

Collection

2025 ASEE Annual Conference & Exposition

Authors

Russell K. Marzette Jr., The Ohio State University; Bhavana Kotla, The Ohio State University; Cal King, The Ohio State University

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Design in Engineering Education Division (DEED)

method to find survivors that have access to their phones and can connect to an automatedinternet source. Second, it's useful in hazardous situations like the Turkey earthquake, likementioned before. Third, it offers quick response to people, minimized lives blasting theaftermath.”Sub-theme 4: NoveltyThis sub-theme highlights participants acknowledging the novelty and uniqueness of their createdsolutions (e.g., how the product stands out).“Our project, Flowware, stands out by using ChatGPT API to offer smart, personalized financialmanagement while displaying your finances through react flow, creating a dynamic, real-timemap of your money.”Theme 2: Design and ApplicationThe design and application theme includes challenges, design considerations

Conference Session

Design in Engineering Education Division (DEED) - Best in DEED

Collection

2025 ASEE Annual Conference & Exposition

Authors

L'Nard E.T. Tufts II, Stanford University; Alessandra O. Napoli, Stanford University; Shima Salehi, Stanford University; Anna Lisa Boslough, Stanford University

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Design in Engineering Education Division (DEED)

efficacy.AcknowledgmentsWe utilized resources from Stanford University's "AI Playground" to explore and validate ourapproaches to incorporating AI tools into the feedback generation process. Through this portal,Anthropic's Claude.ai, version 3.5-Sonnet, helped automate the analysis of student reflectiveresponses by identifying general themes, common omissions, unique realizations, and evidenceof reflective practice. OpenAI's ChatGPT, version 4o, helped generate reflection scores forstudent responses, providing a quantitative measure of reflective practice. We thank thedevelopers of these tools which are available using the links below.https://aiplayground-prod.stanford.eduhttps://claude.aihttps://chat.openai.comReferences[1] T. Anderson and J. Shattuck, "Design

Conference Session

Design in Engineering Education Division (DEED) - Innovative Assessment Strategies in Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jessie Cortez, Texas A&M University; Joanna Tsenn, Texas A&M University

Tagged Divisions

Design in Engineering Education Division (DEED)

sampleBackground sections responding to the following problem statement: The McDonnell Douglas DC-10's outward-opening cargo door has a faulty locking mechanism that, upon failure, causes the door to open and the plane to explosively decompress. The project sponsor, a representative of McDonnell Douglas, has asked the design team to redesign the aft cargo door to prevent accidental opening during flight.Each background section was written with the aid of ChatGPT to simulate problems withrhetorical appropriateness and formatting and organization observed in students’ backgroundresearch sections in previous semesters. For example, the technical writing coordinator promptedChatGPT to write a background research section focused on types of cargo doors

Conference Session

Design in Engineering Education Division (DEED) - Team-Based and Experiential Learning

Collection

2025 ASEE Annual Conference & Exposition

Authors

Prarthona Paul, University of Toronto; Anipreet Chowdhury, University of Toronto; Loura Elshaer, University of Toronto; Anushka Sethi, University of Toronto; Hamid S Timorabadi P.Eng., University of Toronto

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Design in Engineering Education Division (DEED)

project-based courses. Theexisting pre-trained models did not yield good enough results; therefore, we decided to train ourown. We extracted sample tasks from 200 syllabi from engineering project-based courses. Someof these are publicly available syllabi, from real engineering courses from different NorthAmerican Universities, while others are of fictional engineering courses developed by generativeLLM tools, such as ChatGPT and Microsoft Copilot based on the formats of the real syllabi.These extracted tasks were then labelled with their corresponding classes, which were used totrain a RoBERTa model. This model performed better than the pre-trained models, as it had anF1 score closer to the requirements for the project (outlined in Appendix A