Conference Session

Construction Engineering Division (CONST) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sanjeev Adhikari, Kennesaw State University; Arbaaz Hussain Syed; Sandeep Langar, The University of Texas at San Antonio; Rachel Mosier P.E., Oklahoma State University

Tagged Divisions

Construction Engineering Division (CONST)

(GANs) and Large Language Models(LLMs) such as ChatGPT, has profoundly transformed how architects, engineers, andconstruction professionals conceive, plan, and execute projects [12]. Generative AI, likeChatGPT, plays a significant role in generating designs and layouts, making the constructionprocess more optimal. AI can create designs using both text and visuals, providing flexibility tomodernize workflows. It enhances the visualization of design intent and communication amongstakeholders (clients, designers, general contractors, and others), ensuring that stakeholders canmeet owner expectations. AI models can analyze a larger dataset of existing designs, methods,and materials, saving designers time in data gathering. This is both cost and

Conference Session

Biomedical Engineering Division (BED) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alex Nelson Frickenstein, University of Oklahoma

Tagged Divisions

Biomedical Engineering Division (BED)

Instructor Responses Showing how AI can be used to conduct initial research into a I think that students could improve at their use of sequential prompts. subject and find appropriate sources. I think students would benefit from [the] use of more diverse AI tools Asking AI for ideas of how to start problems or explain the current (i.e., in addition to ChatGPT). I think that students should use AI flow in circuitry to better understand how they interact. more to generate practice problems and summaries from lectures Have [AI] write more of the [project] document. Most of the work is (i.e., to help them study at their own pace and be more self-directed busy work, wanting a specific

Conference Session

AI in the Engineering Management Classroom

Collection

2025 ASEE Annual Conference & Exposition

Authors

Philip Appiah-Kubi, University of Dayton; Khalid Zouhri, University of Dayton; Yooneun Lee, University of Dayton

Tagged Divisions

Engineering Management Division (EMD)

respondentsexpress their lack of readiness to accept AI integration for performance monitoring and workloadassignment. Thus, since many engineering students are eventually going to graduate and becomeengineering managers who may utilize AI tools, engineering educators and researchers mustcontinue to explore ways to enhance students’ familiarity and proficiency with AI systems.LimitationsThis exploratory study utilized a limited sample in a randomized survey. Therefore, additionalwork is needed before the findings can be generalized.References[1] A. Kovari, "Explainable AI chatbots towards XAI ChatGPT: A review," Heliyon, vol. 11, no. 2, p. e42077, 2025/01/30/ 2025, doi: https://doi.org/10.1016/j.heliyon.2025.e42077.[2] M. V. Pusic and R. H

Conference Session

AI Integration in Engineering Economy Course

Collection

2025 ASEE Annual Conference & Exposition

Authors

Raymond L. Smith III, East Carolina University; Ricky T Castles, East Carolina University; Emily Fuller Sondergard

Tagged Topics

Diversity

Tagged Divisions

Engineering Economy Division (EED)

disciplines[15]. However, the growing influence of generative AI tools like ChatGPT has also raised concernsregarding academic integrity and appropriate use, particularly among younger learners [16].Rather than viewing AI solely as a threat to traditional education models, recent efforts advocate for itsresponsible integration to enrich learning environments [11]. Strategies such as developing custom AIchatbots aligned with educational objectives offer pathways to maintain academic rigor while leveragingthe strengths of AI technologies [17]. At the forefront of this movement, work presented at the AmericanSociety for Engineering Education (ASEE) has demonstrated the effective use of custom generative AIchatbots as course resources [18], [19

Conference Session

Liberal Education/Engineering & Society Division (LEES) Technical Session 3: Identity, Professionalization, and Belonging II

Collection

2025 ASEE Annual Conference & Exposition

Authors

Clay Walker, University of Michigan; Mariel Krupansky, University of Michigan; Robin Fowler, University of Michigan; Kenneth M. Alfano, University of Michigan; Colleen Hart, University of Michigan

Tagged Divisions

Liberal Education/Engineering & Society Division (LEES)

expertise [1] and to develop ideas [2]. Findings from early studies afterthe public release of ChatGPT have found that students see GenAI as a useful but limited tool[3-6]. GenAI tools saturate digital writing ecologies and continue to gain power with eachiteration, yet student use of GenAI remains an understudied aspect of generative AI uptake inhigher education literacy [7]. Engineering education has unique features (e.g., coding,calculations, design processes, technical communication) and deserves its own empiricalresearch on student writing practices in relation to GenAI, not yet done to our knowledge.Additionally, it is still unclear how generative AI technologies will shape the engineeringeducation landscape as students grapple with the

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ibukun Samuel Osunbunmi, Pennsylvania State University; Taiwo Raphael Feyijimi, University of Georgia; Lexy Chiwete Arinze, Purdue University at West Lafayette (COE); Viyon Dansu, Florida International University; Bolaji Ruth Bamidele, Utah State University; Yashin Brijmohan, Utah State University; Stephanie Cutler, The Pennsylvania State University

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

AI toolsare trained when using the tool. Some questions were posed to encourage critical thinking, such asexamining the data used for AI training, the reliability of AI outputs, and strategies for fine-tuningAI tools. This reflective process aimed to help participants balance human judgment with AIassistance effectively.Furthermore, the participants were introduced to Bloom's taxonomy as a framework for developingAI literacy [13], progressing from foundational knowledge acquisition to the creation of originalwork (See Figure 1). Practical sessions involved the use of resources like ChatGPT, Scholarly,Elicit, and Consensus as AI as a tutor and for aiding literature reviews and syntheses. Similar AIworkshops have been held by the facilitator

Conference Session

Computing and Information Technology Division (CIT) Technical Session 10

Collection

2025 ASEE Annual Conference & Exposition

Authors

Donggil Song, Texas A&M University; ANNE LIPPERT, Prairie View A&M University

Tagged Divisions

Computing and Information Technology Division (CIT)

. Wade, “Using writing to develop and assess critical thinking,” Teaching of psychology, vol. 22, no. 1, pp. 24-28, 1995.[3] A. Leahy, M. Cantrell, and M. Swander, “Theories of creativity and creative writing pedagogy,” The handbook of creative writing, pp. 11-23, 2014.[4] L. Van Ockenburg, D. van Weijen, and G. Rijlaarsdam, “Learning to write synthesis texts: A review of intervention studies,” Journal of Writing Research, vol. 10, no. 3, pp. 401-428, 2019.[5] C. G. Berdanier, and M. Alley, “We still need to teach engineers to write in the era of ChatGPT,” Journal of Engineering Education, vol. 112, no. 3, pp. 583-586, 2023.[6] V. A. Burrows, B. McNeill, N. F. Hubele, and L. Bellamy, “Statistical evidence for

Conference Session

Graduate College Industry Partnerships

Collection

2025 ASEE Annual Conference & Exposition

Authors

Reem Khojah, University of California, San Diego; Alyssa Catherine Taylor, University of California San Diego

Tagged Topics

Diversity

Tagged Divisions

College Industry Partnerships Division (CIP)

, et al. ‘A domain-specific next-generation large language model (LLM) or ChatGPT is required for biomedical engineering and research.’ Annals of biomedical engineering 52.3 (2024): 451-454.”.[3]​ “ASEE PEER - Impact of AI Tools on Engineering Education.” Accessed: Jan. 13, 2025. [Online]. Available: https://peer.asee.org/impact-of-ai-tools-on-engineering-education?utm_source=chatgpt.com[4]​ “ASEE PEER - Revolutionizing Engineering Education: The Impact of AI Tools on Student Learning.” Accessed: Jan. 13, 2025. [Online]. Available: https://peer.asee.org/revolutionizing-engineering-education-the-impact-of-ai

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Marshall Ismail, Worcester Polytechnic Institute; Devin Kachadoorian, Worcester Polytechnic Institute; Sahil Mirani, Worcester Polytechnic Institute; D. Matthew Boyer, Clemson University; Tim Ransom, Clemson University; Ahmet Can Sabuncu, Worcester Polytechnic Institute

Tagged Divisions

Computers in Education Division (COED)

, challenges in assessment persist, including the ethical considerations of dataprivacy and the potential biases in interpreting user feedback. Addressing these issues requirestransparent methodologies and a commitment to refining the design of AI-driven educationaltools based on evidence-based practices [14]. Through rigorous assessment, AI chatbots can beoptimized as transformative tools in engineering education.AI Chatbot As mentioned, a chatbot is a chat-based algorithm that uses natural language processing(NLP) algorithms to converse with the user. OpenAI’s ChatGPT is an example of a chatbotbecause it uses both natural language processing and proprietary algorithms to communicate withusers in a conversation-like manner. The algorithm

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Rawan Adnan Alturkistani, Virginia Tech Department of Engineering Education; Mohammed Seyam, Virginia Polytechnic Institute and State University

Tagged Divisions

Computers in Education Division (COED)

of GenAI but the most sorted types based on the input and outputformats are eleven types as shown in Table[3], which are Text-to-Text, Text-to-Image, Text-to-3D, Text-to-Audio, Text-to-Video, Text-to-Code, Text-to-Scientific text, Text-to-Chemical Formula, Text-to-Synthetic data, Text-to-Algorithm and Image-to-Text [38]. Thereare also some subtypes such as Image-to-3D, Image or Video-to-3D, Text-to-Video, Image-to-Science, Text-to-Speech, Speech-to-Text, and Speech-to-Speech. We will talk about eachtype correspondingly [39]. Text-to-Text is the most well-known type of all that generates texts based on textinputs. An example of this type is ChatGPT. To generate a text response, we need to usemachine learning, and existing data in

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Joshua Coriell, Louisiana Tech University; Ankunda Kiremire, Louisiana Tech University; Krystal Corbett Cruse, Louisiana Tech University; William C. Long, Louisiana Tech University

Tagged Divisions

Computers in Education Division (COED)

Mechanical Engineering Department at Louisiana Tech University. She is also the Director of the Office for Women in Science and Engineering at Louisiana Tech.William C. Long, Louisiana Tech University ©American Society for Engineering Education, 2025WIP: Evaluating Programming Skills in the Age of LLMs: A HybridApproach to Student AssessmentAbstractThe advent of large language models (LLMs), such as OpenAI’s ChatGPT, has augmented thechallenge of assessing student understanding and ensuring academic integrity is maintained onhomework assignments. In a course with a heavy focus on programming, it is common to have asignificant portion of the grade be determined by such assignments. When an LLM is promptedwith the

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nadya Shalamova, Milwaukee School of Engineering; Olga Imas, Milwaukee School of Engineering; James Lembke; Maria Pares-Toral, Milwaukee School of Engineering; Derek David Riley, Milwaukee School of Engineering; Daniel Bergen, Milwaukee School of Engineering

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

Intelligence (AI) is no longer a subject of science fiction or a niche for specializedindustries. AI permeates everyday life, impacting how people work, communicate, and solveproblems locally and globally [1]. AI applications in higher education have grown significantlyin recent years, as evidenced by the adoption of AI-driven instructional design tools andapplications (e.g., Khan Academy's Khanmigo, ChatGPT for Education, MagicSchool), AI-enabled scientific literature search engines (e.g., Semantic Scholar, Consensus), collaborativeapplications (e.g., MS Teams), smart AI features in learning management systems (e.g., Canvas),and AI-based assistants (e.g., Grammarly, Canva).The widespread infusion of generative AI (GenAI) specifically marked a new

Conference Session

Faculty Development Division (FDD) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gadhaun Aslam, University of Florida; Idalis Villanueva Alarcón, University of Florida

Tagged Topics

Diversity

Tagged Divisions

Faculty Development Division (FDD)

bepresented as a lightning talk.Keywords—Faculty Professional Development, Mentor, Mentee, Faculty, EngineeringIntroductionThere is a growing discourse on faculty professional development within the field of engineeringto improve pedagogical practices within engineering and to enhance students’ learning [1], [2],[3], [4]. With a major shift in technological advancements within education due to large languagemodels (ChatGPT, Claude, etc.), the focus of teaching should not only be on lecture content butalso on effective didactic approaches [5], [6]. It has been found that the classroom environmenthas a profound impact on student success and learning [7]. Additionally, there is limited literatureon transparent communication of engineering faculty with

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

Tagged Topics

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

Tagged Topics

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

ERM WIP V: Assessing & Developing Competencies in Engineering Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jason J Saleem, University of Louisville; Edward James Isoghie, University of Louisville; Jeffrey Lloyd Hieb, University of Louisville; Thomas Tretter, University of Louisville

Tagged Divisions

Educational Research and Methods Division (ERM)

summaries inaddition to standard quantitative anthropometric data tables to support their work on a designproblem focused on workstation design. We used generative AI (i.e., ChatGPT) to produce 10fictitious interview transcripts as a starting point, adjusting the prompts as needed to constructrealistic looking interviews. After editing the transcripts to introduce more variability anddistinction across the 10 interview transcripts, intentional “design seeds” were planted within theinterview texts for students to potentially discover during their qualitative analysis. Our goal wasto have recurrent design seeds (e.g. comments about the absence of adequate lumbar support forthe desk chair), appearing across multiple interview transcripts in a variety

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

Tagged Topics

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

Games & Competitions for Civil Engineering Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Anthony Battistini, Angelo State University

Tagged Divisions

Civil Engineering Division (CIVIL)

teach His disciples,who in turn have passed these stories down for over 2000 years. Even ChatGPT lists “storytelling”as its number 2 strategy when asked how to make engineering videos more engaging [11].When considering the competition for students’ attention, it is no wonder that traditional coursesfall short of engaging the students’ interest. Therefore, the work in progress seeks to challenge thenorm by combining the technical and historical content with the dramatic story-telling elements ofa fictional novel. The goal is for students to want to read the textbook, to want to come to class,and to be inspired to pursue their own creativity within engineering. For without creativity,innovations in engineering will not take place.The Rise and

Conference Session

Biological and Agricultural Engineering Division (BAE) Technical Session 1

Collection

2025 ASEE Annual Conference & Exposition

Authors

Woongbin Park, Purdue University; Yunjin Lim, Korea Institute for Curriculum and Evaluation; Jung Han, Purdue University; Hyeree Cho, Purdue University; Seokyoung Kwon; Juhyun Kim, Seoul Metropolitan Office of Education

Tagged Divisions

Biological and Agricultural Engineering Division (BAE)

troubleshoot power issues on their own. The high school students’ use of ChatGPT forefficient problem-solving highlights how technology was leveraged. "While building the smart farm, we faced an issue with insufficient power supply. Specifically, we couldn’t operate the LCD, motor, and water pump simultaneously. To solve this, we separated the power supply into one unit powered by Arduino and another using an external power source." (M5) "When writing code for the smart farm device, errors often occurred. To solve these efficiently, we often used ChatGPT to debug and optimize the code." (H2)Advantages of smart farming Teachers highlighted the advantages of the smart farm compared to more structuredmodels or hydroponics. Teacher 1

Conference Session

Engineering Libraries Division (ELD) Technical Session 4

Collection

2025 ASEE Annual Conference & Exposition

Authors

Anne E Rauh, Syracuse University; Amy S. Van Epps, Harvard University; John J Meier, Pennsylvania State University

Tagged Divisions

Engineering Libraries Division (ELD)

in the search process. At this point, authorsmanually rejected or excluded additional articles that did not meet the topic of the managementof evidence synthesis services in libraries. The resulting list of articles selected is included inAppendix 1.One author manually reviewed the abstracts of each article. If the article included information onsystematic review services, training, or skill development, the author then read or skimmed eacharticle. If the article did not mention those aspects, it was discarded. Another author loaded smallgroups of articles into an institutional subscription to ChatGPT-4o in a closed university researchenvironment to produce summaries of the works. The team members then met to discuss theirfindings and the

Conference Session

Graduate Education, Artificial Intelligence

Collection

2025 ASEE Annual Conference & Exposition

Authors

Wei Lu, Texas A&M University; Behbood Ben Zoghi P.E., Southern Methodist University

Tagged Divisions

Continuing, Professional, and Online Education Division (CPOED)

, that faculty don’t feel hesitate to utilize, and that can serve as aknowledge base and point faculty into the correct direction if needed. Potential Solution Compare Different Options As AI technology evolves everyday, new tools become available at the speed of light. An initial search of AI-powered knowledge base management tools revealed that: there are tools such as Perplexity and ChatGPT that allows team collaboration with Pro account subscriptions; integrated, large sized enterprise-oriented, safety enhanced tools such as Microsoft products (e.g. Azure AI); and more comprehensive, off-the-shelf tools such as Document 360

Conference Session

Innovative Learning Tools and Visualizations in ECE Curriculum

Collection

2025 ASEE Annual Conference & Exposition

Authors

Cyrus Habibi, University of Wisconsin - Platteville; Tina Alaei

Tagged Divisions

Electrical and Computer Engineering Division (ECE)

of course mapping and alignment is neither challenging nor time-consumingwith the assistance of ChatGPT. By providing the course coverage, outcomes, and content,ChatGPT was able to generate units, lessons, and related assignments efficiently. However, fine-tuning is necessary to align the generated lessons and units with the specific teaching materialsand objectives of the course.Future work could focus on refining the modular approach by incorporating more interactive andhands-on activities to address feedback regarding engagement. Additionally, expanding the useof this structured alignment method across other disciplines or multi-disciplinary courses couldvalidate its broader applicability. Continuous enhancement through feedback loops

Conference Session

Engineering Ethics Division (ETHICS) Technical Session - Ethics in ML/AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jenny Tilsen, Bucknell University; Robert M Nickel, Bucknell University; Stewart Thomas, Bucknell University; Sarah Appelhans, Lafayette College; Alan Cheville, Bucknell University

Tagged Divisions

Engineering Ethics Division (ETHICS)

each other’s STEMtells and offer feedback on how to improve the STEMtell. 3. STEMtellers rewrite their STEMtell based on the feedback received in their groups. 4. Step 4 was an additional step and suggested by Author 2, to specifically engage with the context of STEMtelling in a machine learning course. In this Step, students were asked to upload their STEMtells into a LLM of their choice (ChatGPT, Claude, etc.), with the following prompt: “First, summarize each story. Second, assess the quality of these stories and provide suggestions on how to improve the stories based on story structure, sensory details, and other components of a story. Third, provide feedback on how factual

Conference Session

Computers in Education Division (COED) Track 6.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gabriel Beal, zyBooks, A Wiley Brand; Chi Yan Daniel Leung, zyBooks, A Wiley Brand; Joe Mazzone, zyBooks, A Wiley Brand; Chelsea L Gordon, zyBooks, A Wiley Brand; Yamuna Rajasekhar, zyBooks, A Wiley Brand

Tagged Divisions

Computers in Education Division (COED)

is struggling and resorts to outside assistance to complete the work.‬‭Introduction‬‭ tudent cheating on programming homework assignments in introductory‬S‭computer science courses is a long standing trend [1-4], a problem that‬ ‭widespread access to large language models has substantially exacerbated such as‬ ‭ChatGPT. A survey from 2023 found that 30% of students frequently used GenAI‬ ‭tools for completing assignments [5]. Many academics are expressing concern‬ ‭that this may largely undermine learning processes and decrease academic‬ ‭integrity [6].‬‭ ow that advanced LLMs can generate content that is relatively indistinguishable‬N‭from human created content [7-11], cheating detection has become much more‬ ‭difficult. Research

Conference Session

Liberal Education/Engineering & Society Division (LEES) Technical Session 6: LEES Works in Progress

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gary P. Halada, Stony Brook University; Lori Scarlatos, Stony Brook University

Tagged Topics

Diversity

Tagged Divisions

Liberal Education/Engineering & Society Division (LEES)

. "Beyond Colonial Hegemonies: Writing Scholarship andPedagogy with Nya ̄yasutra." Rhetorics Elsewhere and Otherwise: ContestedModernities, Decolonial Visions, 169-195 (2019).13 OpenAI, “Is ChatGPT Biased?”, https://help.openai.com/en/articles/8313359-is-chatgpt-biased14 Teboho Pitso, “Invitational Pedagogy: An Alternative Practice in DevelopingCreativity in Undergraduates”, in Booth, Shirley, and Laurie Woollacott."Introduction to the Scholarship of Teaching and Learning." The Scholarship ofTeaching and Learning in Higher Education–On Its Constitution andTransformative Potential, 2015.15 Riegle-Crumb, Catherine, Barbara King, and Yasmiyn Irizarry. "Does STEMstand out? Examining racial/ethnic gaps in persistence across postsecondaryfields

Conference Session

Engineering Libraries Division (ELD) Technical Session 4

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jason Cerrato MA, MSLIS, PhD Student, Brown University

Tagged Divisions

Engineering Libraries Division (ELD)

exploration of a varietyof tools, including, but not limited to, Scite.AI and Perplexity (as RAG-based informationretrieval tools), Elicit (within a systematic review context), ChatGPT and Claude (as morecommonly known LLM ‘bots’), as well as integrated AI features of commonly known tools, 9such as the Web of Science and Primo discovery AI features (classed as AI ‘assistants’ orCo-pilots).Cross-Sections: A Survey of Learning Community Membership & Interests(AY ‘23-24) Over the Learning Community’s initial year of programming (academic year 2023-2024), the planning committee actively solicited feedback and insights from the groupregarding topics of

Conference Session

Computing and Information Technology Division (CIT) Technical Session 3

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kwansun Cho, University of Florida; Umer Farooq, Texas A&M University; Minje Bang, Texas A&M University; Saira Anwar, Texas A&M University

Tagged Topics

Diversity

Tagged Divisions

Computing and Information Technology Division (CIT)

examples forclarity and engagement in conceptually hard courses such as programming. Also, similar to priorliterature [33], this study highlights that student satisfaction is coupled with clarity andengagement with the material. AI-based Large Language Models such as ChatGPT can enhancestudents’ engagement with pre-class materials by providing interactive explanations,personalized feedback, and intelligent tutoring support tailored to individual learning needs [35].The study's results must be viewed in the light of some limitations and future directions. First,the study was based on self-reported student perceptions of two types of videos. Future studiescould consider other measures, such as time spent on each video and a performance measureafter

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

Tagged Topics

Diversity

Tagged Divisions

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

ENT-8: Mentorship, Creativity, and Ethics in Academic Entrepreneurship

Collection

2025 ASEE Annual Conference & Exposition

Authors

Christina McGahan, Vanderbilt University; Charleson S Bell, Vanderbilt University; Deanna Meador, Vanderbilt University; Christopher Harris, Vanderbilt University; HD McKay, Vanderbilt University, Management Library; Yiorgos Kostoulas, Vanderbilt University; Kevin Galloway, Vanderbilt University; Philippe M. Fauchet, Vanderbilt University; David A. Owens, School of Engineering, Vanderbilt University; Sharon M. Weiss, Vanderbilt University

Tagged Topics

Diversity

Tagged Divisions

Entrepreneurship & Engineering Innovation Division (ENT)

in nano-makerspace, intellectual property strategy 4 Structured lab in nano-makerspace (I), case study with nanoscience entrepreneur (II) 5 Structured lab in nano-makerspace (II), team management, project idea brainstorming 6 Structured lab in nano-makerspace (III), computer-aided design 7 Project selection, identifying project value proposition and customer segment, project BMC check-in, identifying project prototype fabrication approach 8 Market landscape and customer relationships for project, library databases and ChatGPT 9 Storytelling, project BMC check-in, student-led

Conference Session

ERM Technical Session: Faculty Influences on Student Support

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gadhaun Aslam, University of Florida; Yuxuan Wang, University of Florida; Idalis Villanueva Alarcón, University of Florida; Edwin Marte, University of Florida

Tagged Divisions

Educational Research and Methods Division (ERM)

/neutral.For this categorization purpose, the researchers manually classified the emotions into biggercategories. ChatGPT-4 was used as a secondary resource to categorize different emotions under abigger umbrella of emotion. For example, in model 4, the emotions like anger, remorse,annoyance, disapproval, and disgust were all categorized as ‘Anger’ to be able to compare it withresults from other models. This categorization is shown in Appendix A. For this study, students(474, 81.4%) include all undergraduate and graduate students while professors (84, 14.4%)include full professors, associate professors, assistant professors, adjunct professors, academicadvisors, and lecturers. Out of the remaining 24 participants, 2 had already graduated and theothers