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

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

Tagged Divisions

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

GSD 5: Mentorship

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jacqueline E McDermott, Purdue University at West Lafayette (COE); Nathan Tompkins, Wabash College

Tagged Topics

Diversity

Tagged Divisions

Graduate Studies Division (GSD)

mentees and randomly sorting each column independently to create a random butrepresentative set of mentees. The mentees were then manually paired to create the seeds formentoring groups. Sample student data was generated using a combination of generative AI(ChatGPT) and previous student data. Random student names were generated in ChatGPT byasking for a list of 100 random names from all ethnicities in the US with likely ethnicity and genderfor each name (ChatGPT only returned 98 entries). Gender was classified as Man or Woman with2% of the list being randomly classified with a gender of More to account for transgender and non-binary students. Sample student ethnicity was classified as one of: African American/Black, EastAsian/Asian American (e.g

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Md. Yunus Naseri, Virginia Polytechnic Institute and State University; Vinod K. Lohani, Virginia Polytechnic Institute and State University; Manoj K Jha P.E., North Carolina A&T State University; Gautam Biswas, Vanderbilt University; Caitlin Snyder; Steven X. Jiang, North Carolina A&T State University; Caroline Benson Sear, Virginia Polytechnic Institute and State University

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

required by employers. As more data and analytical methods becomeavailable, more aspects of the economy, society, and daily life will become dependent on data-driven decision-making. Recognizing this shift, the National Academies of Sciences (2018)emphasizes that academic institutions must prioritize developing "a basic understanding of datascience in all undergraduates" to prepare them for this new era [1]. This is particularly crucial forSTEM graduates, who must develop varying levels of expertise in working with data – the abilityto understand, interpret, and critically evaluate data, as well as to use data effectively to informdecisions. The recent emergence of large language models (LLMs) such as ChatGPT, which arebecoming increasingly

Conference Session

ENT-4: Experiential Approaches to Developing Entrepreneurial Mindsets in Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

Stephanie G Wettstein, Montana State University - Bozeman

Tagged Divisions

Entrepreneurship & Engineering Innovation Division (ENT)

could focus on performing the jigsaw activities without thealumni present or the seminar series to see if the change in EM is similar and a larger sample sizeof students would benefit the study.AcknowledgmentsI would like to acknowledge the Robert D. and Patricia E. Kern Family Foundation, Inc. and thetask force of leaders representing the Engineering Unleashed Faculty Development communitywho selected me for the KEEN Fellowship and provided the grant funds for the activities.Additionally, I would like to thank Dr. Douglas Hacker who performed the statistical analysesreported within. During the preparation of this work, I used ChatGPT in order to improve thereadability and concision of the document. After using ChatGPT, I reviewed and edited

Collection

2025 Northeast Section Conference

Authors

Elyas Irankhah; Sashank Narain; Kelilah L. Wolkowicz

students to learn about lab safety through machine learning [36]. They would select objects deemed safe for a chemistry lab and train the model to classify items, thus directly engaging with the concept of AI learning and classification. The Ask Me Anything (AMA) booth featured a ChatGPT-powered chatbot limited to discussing child-friendly

Conference Session

Professional Papers

Collection

2025 ASEE Southeast Conference

Authors

Shenghua Wu, University of South Alabama; Min-Wook Kang, University of South Alabama; John Cleary, University of South Alabama; Lisa LaCross, University of South Alabama

Tagged Topics

Diversity, Professional Papers

writing In-class activity2.1 Week 1: First In-person Meeting Activity: Setting Up Your Goal2.1.1 Use of MentimeterIn the first in-person class, the course expectations are introduced. A Mentimeter is used to makethe session interactive and engaging. The following questions are asked during the first meeting,allowing students to see their responses in real-time: How are you today? Use one word todescribe how you feel now. How do you rate your current writing skill? (0-100 points). Howmany journal articles (not including conference presentations) have you published so far? Whatare your expectations for this course? Have you used AI (e.g. ChatGPT) in your academic work?Which area(s) do you find challenging when starting to write? How are

Conference Session

Sustainability and Social Responsibility

Collection

2025 ASEE Annual Conference & Exposition

Authors

Erick S. Vasquez-Guardado, University of Dayton; Megan Morin, North Carolina State University at Raleigh

Tagged Divisions

Chemical Engineering Division (ChED)

Effects of Alcohol in Heat Transfer Fluid Flow and Heat Transfer Principles in ClimateIn addition to obtaining the “top-ranked” micromoments, we also examine the students’suggestions for future efforts. Examining the answers to question P5: What can be improved forfuture student-led micromoment presentations? and using AI (ChatGPT 4.0), five general themeswere obtained, including: “Alignment with class material, guidance and resources, timing andaccessibility, engagement and interaction, and open-ended creativity with practical constrains.” Ofsignificance in the alignment with class material, we found that presentations should connectdirectly with class topics to enhance understanding and relevance of the content. Also, studentsnoted that

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Tayo Obafemi-Ajayi, Missouri State University; Naomi L Dille, Missouri State University; Dhanush Bavisetti, Missouri State University; Sherrie Ilene Zook

Tagged Divisions

Computers in Education Division (COED)

for teaching 6th-grade Missouri math standards, incorporating project-based learning with LEGO Mindstorm cars and coordinate plane activities. These activities provide hands-on,engaging ways to connect coding with math standards, fostering both computational thinking and mastery ofgrade-level concepts. This provided a framework to implement advanced ML knowledge into STEM education bydeveloping practical methodologies to teach complex ML principles through easily accessible tools. For the high school students, an intentional choice was made to utilize Scratch, rather than Python for program-ming due to the influence of AI tools (such as ChatGPT) that can provide the entire Python code script. Scratchseemed a little more foolproof, as

Conference Session

DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sri Krishna Chaitanya Velamakanni, Pennsylvania State University; Suman Saha, Pennsylvania State University

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

ethical concerns, biases, andover-reliance on AI, which could undermine critical thinking and equitable access to education.E. Microlearning, AI-Driven Feedback, and Student EngagementAI-generated feedback has also emerged as a key enabler of personalized education. Escalantedemonstrated that AI tools provide concise, actionable guidance, aligning with the principles ofbite-sized learning [28]. Similarly, studies such as KOGI's application in programming educationand insights from ChatGPT in first-year engineering courses emphasize the value of modular,on-demand support in enhancing educational outcomes [29]. These works collectively reinforcethe importance of tailored educational resources, such as microlearning videos, in addressing thespecific

Conference Session

Engineering Education Methods and Reflections

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jad El Harake, Vanderbilt University; gina yu, Vanderbilt University; Kaden Jorge Tro; Jonathan Ehrman, Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA

Tagged Topics

Diversity

Tagged Divisions

Student Division (STDT)

perceived growth and development of the student. In the latter case, manualcoding of the responses revealed which specific skills were acquired by the student and identifiedby the mentor but not by the student response, leading to a positive score discrepancy, or theareas which mentors identified as having room for improvement, leading to a negative scorediscrepancy.When considering the thematic content of all responses rather than focusing on those whichpresented with score discrepancies, coding and tallying of responses was complemented with theaid of the LLM ChatGPT (OpenAI, CA, USA). The use of LLMs in content analysis has beenpreviously shown to have good agreement with human results [12], [13]. In this study, ChatGPTwas prompted to identify

Conference Session

Liberal Education/Engineering & Society Division (LEES) Technical Session 8: Communication and Liberal Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Amanda Dawn Hilliard, The Johns Hopkins University

Tagged Topics

Diversity

Tagged Divisions

Liberal Education/Engineering & Society Division (LEES)

-5ct7-54du.[13] S. A. Athaluri, S. V. Manthena, V. S. R. K. M. Kesapragada, V. Yarlagadda, T. Dave, and R. T. S. Duddumpudi, “Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References,” Cureus, Apr. 2023, doi: 10.7759/cureus.37432.[14] A. E. Greene, Writing Science in Plain English, Chicago, IL, USA: The University of Chicago Press, 2013.[15] G. R. Hess and E. N. Brooks, “The Class Poster Conference as a Teaching Tool,” Journal of Natural Resources and Life Sciences Education, vol. 27, no. 1, pp. 155–158, 1998, doi: 10.2134/jnrlse.1998.0155.[16] J. Schimel, Writing Science: How to Write Papers that Get Cited and Proposals

Collection

2025 Northeast Section Conference

Authors

PS. Dhanasekaran

ensuring its responsible and equitable use [9].can also widen societal and digital inequalities. AI offersnew learning opportunities but may also put II. CHALLENGESmarginalized students at risk. Unsurprisingly, teachers There is growing concern that the widespread use ofview AI as both a "pal and rival" [2]. computers in education may harm students' physical In recent times, concerns have grown in health, contributing to repetitive strain injuries, eyeacademic settings regarding the use of text-generative strain, obesity, and other related conditions. Asartificial intelligence (AI) tools like ChatGPT, Bing, and computers become

Collection

2025 Northeast Section Conference

Authors

Jorge Paricio Garcia; Paul Spirito

potential for inappropriateeffectiveness of market and user research, by leveraging access and misuse of personal or sensitive information, even ingenerative AI tools like ChatGPT, Bard, CoPilot, or Vizcom. the sketching stages, as well others, like inadvertent release ofDesigners and trained engineers can quickly gather and patient data, or de-identification of raw data input for AIsynthesize vast amounts of market and consumer data, algorithms [14]. Wearable healthcare devices are capable ofrevealing opportunities and overlooked user needs, which continuous data recording and can collect extensive patientwould later lead to ethnographic interviews and additional

Collection

2025 Northeast Section Conference

Authors

Haneen Alzahrani; Arthur C. McAdams

[6] Daniel, R. (2021). Exploring creativity through artists’ reflections. Creativity Studies, 14(1), 48-61. https://doi.org/10.3846/cs.2021.1120[7] Dwivedi, Y. K., Tiwari, P., Rana, P. L., Sharma, P. K., Singh, P. K., & [17] Barnett, C. (2003). Culture and democracy: Media, space, andKapoor, J. (2023). Opinion Paper: "So what if ChatGPT wrote it?" representation. Edinburgh University Press.Multidisciplinary perspectives on opportunities, challenges, and implications [18] Benjamin, W. (2008). The work of art in the age of mechanicalof generative conversational AI for research, practice, and policy. reproduction (J. A. Underwood

Conference Session

Full Papers IV

Collection

FYEE 2025 Conference

Authors

James Nathaniel Newcomer, Virginia Polytechnic Institute and State University; David Gray, Virginia Polytechnic Institute and State University; Alice Hyunna Noble, Virginia Polytechnic Institute and State University; Devin Erb, Virginia Polytechnic Institute and State University; Annabel Bass, Virginia Polytechnic Institute and State University

Tagged Topics

FYEE 2025

capture how frequently pairs of codes appeared within the samestudent response. For this analysis, we isolated the 24 codes related to students’professional goals and examined their relationship to students’ intended majors. Majorswith limited representation in the dataset—including biological systems engineering,building construction, construction engineering management, ocean engineering, andmaterial science engineering—were excluded to avoid unreliable clustering. We thenused k-means clustering, assisted by ChatGPT, to identify patterns in the distribution ofgoal codes across majors. Based on silhouette scoring (= 0.193), six clusters wereidentified. For each cluster, we extracted the most frequent goal codes associated withthe majors it

Conference Session

Professional Papers

Collection

2025 ASEE Southeast Conference

Authors

Marino Nader, University of Central Florida

Tagged Topics

Diversity, Professional Papers

, DOI: 10. 1080/105112506008661663. Fask, A., Englander, F., & Wang, Z. (2014). Do online Exams Facilitate Cheating? An Experiment Designed to Separate Possible Cheating from the Effect of the Online Test Taking Environment. J Acad Ethic, 12:101–112 DOI 10.1007/s10805-014-9207-14. Charlesworth, P., Charlesworth, D.D., & Vician, C. (2006) Students’ Perspectives of the influence of Web- Enhanced Coursework on Incidences of Cheating, Journal of Chemical Education, vol. 83 No.9.5. Chegg Inc., website https://www.chegg.com, accessed on November 4th, 2024.6. ChatGPT 4o, https://chat.openai.com, accessed on November 4th, 2024.7. Coure Hero, website www.coursehero.com, accessed on November 4th, 2024.8. Nader, M

Conference Session

First-Year and Experiential Learning for Women Engineers

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jie Sheng, University of Washington, Tacoma; Justin Wang, The Overlake School

Tagged Topics

Diversity

Tagged Divisions

Women in Engineering Division (WIED)

curriculum of our Computer Engineering program and require only basicknowledge of physics and calculus. For Arduino code, templates will be given and explained, soattendees can focus on the key concepts like A/D and D/A conversions, circuit modeling andperformance, feedback control, as well as Proportional-Integral-Derivative (PID) controller.The organizing faculty worked with the recruiting staff and started the preparation in latesummer of 2024. ChatGPT found a name for the workshop as ‘Circuit Breaker: Women inEngineering’; and Nov. 15 was chosen for this half-day hands-on free workshop from 12 to 4pm.We chose Nov. 15 since it was a Friday when community colleges in the area usually don’t offerclasses in the afternoon. Also, the Autumn quarter is

Conference Session

ME Division 6: Innovative Simulation and Extended Reality Techniques

Collection

2025 ASEE Annual Conference & Exposition

Authors

Osama Desouky, Texas A&M University at Qatar; Marwa AbdelGawad, Hamad Bin Khalifa University

Tagged Divisions

Mechanical Engineering Division (MECH)

understanding and experience.Additionally, applying robust statistical methods is essential for tracking and analyzing studentperformance over time, ensuring that the effectiveness of the VR interventions can be measured andrefined for future improvement. IX. Suggested Survey QuestionsPlease rate your agreement with each statement using the following scale:*These questions were formatted and formulated with the help of ChatGPT: • 1 - Strongly Disagree • 2 - Disagree • 3 - Neutral • 4 - Agree • 5 - Strongly AgreeA. Learning and Understanding 1. The VR activities enhanced my understanding of complex mechanical concepts. 2. VR helped me visualize engineering problems better than traditional methods

Conference Session

ELOS Technical Session 3: Advancing Engineering Competencies: From Labs to Writing

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gautom Kumar Das, University of Maryland Baltimore County

Tagged Divisions

Experimentation and Laboratory-Oriented Studies Division (DELOS)

allowed to use generative AI tools (e.g., ChatGPT) during anystage of the writing process or they could choose not to use them. If AI assistance was used,students were asked to include the following information in the Appendix of their reports: theprompt(s) used, and other details on how the AI-assisted content was incorporated or revised.This information was collected to ensure the accuracy of the report content and the authenticityof references.2.2 Instructor’s AssessmentA total of 48 draft reports (i.e., first submission) were evaluated for this study. Reports in whichstudents self-reported the Checklist were analyzed further for this study.3. Results and DiscussionAs mentioned earlier, the primary goal of this study was to evaluate the

Conference Session

ELOS Technical Session 1: Integrating AI, VR, and MR in Engineering Lab Experiences

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jessica Ohanian Perez, California State Polytechnic University, Pomona; Yitong Zhao, California State Polytechnic University, Pomona; Juliana Lynn Fuqua, Cal Poly Pomona

Tagged Divisions

Experimentation and Laboratory-Oriented Studies Division (DELOS)

question and wanted a simple answer. When that happened, they wanted to be able to turn off the AI temporarily or permanently. They coped by totally muting it. A better solution would be for the AI to have a feature like Alexa or Siri in which users can easily say “hey, stop”. That’s essential, according to students. • How the AI was responding to the surrounding speech. AI occasionally responded to not direct questions so if the student was talking through the lab as they completed the assignment, the AI would respond to a question that was not asked. • Students felt that the AI was trained on ChatGPT. Students were asking history questions to it, and it was answering with somewhat relevant

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

John William Hassell, OU Polytechnic Institute; Christopher Freeze, The University of Oklahoma; Ahmed Ashraf Butt, The University of Oklahoma; H. Glen McGowan III, Google; William Ray Freeman

Tagged Divisions

Computers in Education Division (COED)

educators.By removing technical barriers while maintaining pedagogical quality, we aim to support moreefficient and effective assessment creation processes across engineering disciplines. Future workwill focus on measuring this impact through detailed evaluation of system adoption patterns andeducational outcomes.References[1] J. Hassell, "Best Practices for Using Generative AI to Create Quiz Content for the CanvasLMS," 2024 ASEE Midwest Section Conference, ASEE, 2024.[2] S. Willison, "Things we learned about LLMs in 2024," SimonWillison.net, Dec. 31, 2024.[Online]. Available: https://simonwillison.net/2024/Dec/31/llms-in-2024/.[3] J. Yang et al., "Harnessing the Power of LLMs in Practice: A Survey on ChatGPT andBeyond," ACM Transactions on Knowledge

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ahmed Ashraf Butt, University of Oklahoma; Saira Anwar, Texas A&M University; Asefeh Kardgar, Texas A&M University

Tagged Divisions

Computers in Education Division (COED)

limitation was that we used a general-purpose GPT-4 model without any fine-tunedhuman annotator. Fine-tuning GPT-4 with human-annotated LO evaluation based on theSMART criteria may improve the LLM's performance. The third limitation was that although weused the SMART criteria, its criteria needed to be refined and evaluated by educational experts.This process will help us to design better guidelines for evaluating learning objectives. Lastly,we only used 1 LLM model (i.e., GPT-4) to evaluate LOs. Therefore, exploring the efficacy ofother LLM models and comparing their ability to assess LOs is necessary.References:[1] E. Kasneci et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Learn. Individ