Conference Session

NSF Grantees Poster Session II

Collection

2025 ASEE Annual Conference & Exposition

Authors

Justin L Hess, Purdue University at West Lafayette (COE); Robert P. Loweth, The University of North Carolina at Charlotte; Udeme Idem, Purdue University at West Lafayette (COE)

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NSF Grantees Poster Session

,responsible, and nondiscriminatory uses of AI in education, including the impact AI systems haveon vulnerable and underserved communities.” Accordingly, there is a need to develop AI resourcesfor educational contexts (including engineering design) that bring clarity regarding AI’sresponsible and ethical use therein. Undergirding our project design is our belief that GenerativeAI can assist students in making more novel, inclusive, and ethical associations across domains.Pilot Observations of AI Use in Engineering Design CoursesThe first two authors have piloted use of ChatGPT to support students in our design courses. Thispilot work serves as the foundation for our RFE study. We found that the use of Generative AI inengineering courses is subject to

Conference Session

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ahatsham Hayat, University of Nebraska, Lincoln; Sharif Wayne Akil, University of Nebraska, Lincoln; Helen Martinez, University of Nebraska, Lincoln; Bilal Khan, Lehigh University; Mohammad Rashedul Hasan, University of Nebraska, Lincoln

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Diversity, NSF Grantees Poster Session

ChatGPT and Google’s Gemini, for the early prediction of studentperformance in STEM education, circumventing the need for extensive data collection orspecialized model training. Utilizing the intrinsic capabilities of these pre-trained LLMs, wedevelop a cost-efficient, training-free strategy for forecasting end-of-semester outcomes based oninitial academic indicators. Our research investigates the efficacy of these LLMs in zero-shotlearning scenarios, focusing on their ability to forecast academic outcomes from minimal input.By incorporating diverse data elements, including students’ background, cognitive, andnon-cognitive factors, we aim to enhance the models’ zero-shot forecasting accuracy. Ourempirical studies on data from first-year college

Conference Session

NSF Grantees Poster Session II

Collection

2025 ASEE Annual Conference & Exposition

Authors

Fatemeh Karimi Kenari, University of North Carolina at Charlotte; yasaswi bhumireddy, University of North Carolina at Charlotte; Xiaoliang Yan, Georgia Institute of Technology; Mahmoud Dinar, University of North Carolina at Charlotte; Shreyes N Melkote, Georgia Institute of Technology

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NSF Grantees Poster Session

tracks learners’ progress, i.e., it adjusts future responses based onconversation history, and account for the user's existing knowledge. The Adviser alsoincorporates user-level personalization, dynamically adjusting language and the depth ofinformation to align with different user levels. Additionally, Knowledge Retrieval AugmentedGeneration (RAG) [8] integrates knowledge retrieval from manufacturing documents withLarge-Language-Model’s generation capabilities (ChatGPT in this case) to provide contextuallyrelevant responses. Manufacturing documents are divided into smaller chunks of 500 words.Each chunk is transformed into a numerical representation (embedding), capturing semanticinformation for similarity-based retrieval. Figure 1 shows the

Conference Session

NSF Grantees Poster Session I

Collection

2025 ASEE Annual Conference & Exposition

Authors

Lifford McLauchlan, Texas A&M University - Kingsville; David Hicks, Texas A&M University-Kingsville ; Mehrube Mehrubeoglu, Texas A&M University - Corpus Christi

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NSF Grantees Poster Session

more systems include IoT-related control, communications andfunctionality; IoT-based projects, course materials and exercises should introduce or makestudents or end-users aware of potential cybersecurity issues, threats and concerns [10]-[14].Recent advances in AI have led to more readily available open-source machine learningframeworks and APIs, such as Gemini Developer API [15] or PyTorch [16], as well as many toolssuch as ChatGPT [17].Artificial Intelligence and CybersecuritySenior capstone course design projects should address cybersecurity issues and threats [18]. Aspart of the electrical engineering capstone course at Texas A&M University-Kingsville during theFall 2024 semester, students were tasked to perform a whole system mapping

Conference Session

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Yanxia Jia, Arcadia University; Tiantian Wang, The University of Texas at San Antonio; Chaomei Chen, Drexel University; Yu-Fang Jin, The University of Texas at San Antonio

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NSF Grantees Poster Session

grant data in a CSV format through a converting tool. This feature enables the creationof a network of clusters based on keywords and/or terms (noun phrases) extracted from titles andabstracts of REU awards. Specifically, for each REU award, two different approaches wereadopted to extract terms and keywords. Terms were extracted from the titles and abstracts usingCiteSpace. Technical keyword phrases focusing on research contents of REU awards wereextracted by use of ChatGPT Application Programming Interface (API). Subsequently, anetwork of clusters was created based on the extracted terms and keywords. These clusters revealthe main topics of all REU projects in the dataset.Based on the above-mentioned clusters generated from REU and WoS

Conference Session

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

John Clay, University of Texas at Austin; Xingang Li, University of Texas at Austin; Molly H Goldstein, University of Illinois Urbana-Champaign; H. Onan Demirel, Oregon State University; Darya Zabelina; Charles Xie; Zhenghui Sha, University of Texas at Austin

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NSF Grantees Poster Session

socioeconomic status [16]), whichmay negatively impact design performance. Additionally, the limits of human cognition begin tobe tested as the number and complexity of trade-offs, constraints, and user needs that must beconsidered grows [4], [13]. Finally, traditional/manual design approaches are resource intensivedue to the amount of time required for creating preliminary designs, and for manually correctingpotential errors made by the human designer during these tasks.Figure 1. (a) Genetic algorithms exploring possible solutions for renewable solar-energy systemsin the Aladdin CAD software [8]; (b) Variational autoencoders for structure-aware designgeneration [9]; (c) CAD model generation using large language models, such as ChatGPT [10].Thus

Conference Session

NSF Grantees Poster Session II

Collection

2025 ASEE Annual Conference & Exposition

Authors

David Lattanzi, George Mason University

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NSF Grantees Poster Session

–74. doi: 10.1007/978-1-4842-2256-0_3.[6] “Presentations.AI - ChatGPT for Presentations.” Accessed: Jan. 15, 2025. [Online]. Available: https://www.presentations.ai/

Conference Session

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ryan Hare, Rowan University

Tagged Topics

NSF Grantees Poster Session

implementation of more automatedsystems in a classroom helps to free up instructor time and resources, and to help raise overallclassroom performance.To achieve an automated educational support system that can stand without instructorintervention, intelligent tutoring systems (ITSs) offer a valuable avenue of research [2]. Thesesystems are well-established in the field, but have seen a surge in development in recent years dueto advancements in large language models like ChatGPT [3], better artificial intelligence methods[4], wider technology adoption, and the recent boom in e-learning [5]. However, a key aspect ofcomputer- or web-based ITSs often remains unaddressed; they are boring.For ITSs to function properly, it is necessary to perform regular

Conference Session

NSF Grantees Poster Session II

Collection

2025 ASEE Annual Conference & Exposition

Authors

Shana Lee McAlexander, Duke University; Catherine Brinson, Duke University; Richard J. Sheridan, Duke University; Junhong Chen, University of Chicago; Jennifer Nolan, University of Chicago

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NSF Grantees Poster Session

. Option for judging competition 15 min Total 2 hours2.2 Ideation and screening. Next, teams were asked to brainstorm project ideas and articulate aresearch approach. Students are tasked with generating at least five project ideas that appliedmachine learning to materials science questions. They had the option to source ideas fromexisting literature, through ChatGPT prompts, and through curated lists of priority research areaslike The Materials Genome Initiative Challenges [10]. Teams then screened their ideas givingpriority to those which had the greatest potential impact and that they could accomplish as a teamand within the scope of a year

Conference Session

NSF Grantees Poster Session II

Collection

2025 ASEE Annual Conference & Exposition

Authors

Harpreet Auby, Tufts University; Namrata Shivagunde, University of Massachusetts Lowell; Anna Rumshisky, University of Massachusetts Lowell; Milo Koretsky, Tufts University

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NSF Grantees Poster Session

, pp. 219–244, 2016, doi: 10.1002/jee.20116.[4] M. D. Koretsky, B. J. Brooks, and A. Z. Higgins, “Written justifications to multiple- choice concept questions during active learning in class,” Int. J. Sci. Educ., vol. 38, no. 11, pp. 1747–1765, Jul. 2016, doi: 10.1080/09500693.2016.1214303.[5] E. A. Alasadi and C. R. Baiz, “Generative AI in education and research: Opportunities, concerns, and solutions,” J. Chem. Educ., vol. 100, no. 8, pp. 2965–2971, Aug. 2023, doi: 10.1021/acs.jchemed.3c00323.[6] D. Baidoo-Anu and L. O. Ansah, “Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning,” J. AI, vol. 100, no. 8

Conference Session

NSF Grantees Poster Session I

Collection

2025 ASEE Annual Conference & Exposition

Authors

Tamara Powell Tate, University of California, Irvine; Beth Harnick-Shapiro, University of California, Irvine; Mark Warschauer, University of California, Irvine; Waverly Tseng, University of California, Irvine

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Diversity, NSF Grantees Poster Session

California, IrvineAuthor NoteTamara P. Tate https://orcid.org/0000-0002-1753-8435Daniel Ritchie https://orcid.org/ 0000-0002-7110-8882Mark Warschauer https://orcid.org/0000-0002-6817-4416Correspondence concerning this article should be addressed to Tamara Tate, University ofCalifornia, Irvine, 3200 Education, University of California, Irvine, CA 92697. Email:tatet@uci.eduWriting and communication are crucial to engineers, taking up more than half their workinghours [1] [2]. However, too few engineers have the writing and communication skills requisitefor today’s information society [3]. Within this context, new generative artificial intelligence(AI) tools such as ChatGPT and other large language models (“AI writing tools”) pose bothopportunities and

Conference Session

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Grace Lynn Baldwin Kan-uge; Carol S Stwalley P.E., Purdue University, West Lafayette; Robert Merton Stwalley III P.E., Purdue University, West Lafayette

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NSF Grantees Poster Session

Conference Session

NSF Grantees Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Corey T Schimpf, University at Buffalo, The State University of New York; Shanna R. Daly, University of Michigan; Leslie Bondaryk, The Concord Consortium; Jutshi Agarwal, University at Buffalo, The State University of New York; Carolyn S Giroux; Stephanie L. Harmon, PIMSER, Eastern Kentucky University; Enqiao (Annie) Fan, University at Buffalo, The State University of New York; Jacqueline Handley, Purdue University, West Lafayette; A Lynn Stephens, The Concord Consortium

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NSF Grantees Poster Session

of AI techniques and methods toward supporting learning or educational goals.There is a long history of AI being used to support learners from intelligent tutoring systems that trackstudents learning through series of problems and provide custom problem delivery and supports[31], [32],[33] to the more recent use of large-language model, such as ChatGPT, to generate content or support forstudents (e.g., [34]).While AI has been used extensively in some education areas such as math [35], [36], [37] and science[38], [39], it has been used relatively less in design education. Most of the work that does focus on usingAI to support design education tends to examine highly constrained design problems, such as the designof a gear or shaft (e.g., see