Conference Session

Generative AI and Its Role in Industrial Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

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

Tagged Divisions

Industrial Engineering Division (IED)

Department of Engineering Fundamentals at the University of Louisville. He graduated from Furman University in 1992 with degrees in Computer Science and Philosophy. In 2008 he earned his Ph.D. in Computer Science Engineering from the University of Louisville. His research interest include cyber-security for industrial control systems and active learning. ©American Society for Engineering Education, 2025The Use of Generative AI for the Rapid Development of Qualitative Interview Transcripts for a Human-Centered Design ProblemAbstractThis paper describes how generative AI (i.e., ChatGPT) was used to rapidly develop fictitious,yet realistic, qualitative interview transcripts for industrial engineering

Conference Session

AI Integration in Engineering Economy Course

Collection

2025 ASEE Annual Conference & Exposition

Authors

Dani Fadda, University of Texas at Dallas; Oziel Rios, University of Texas at Dallas; P.l.stephan Thamban

Tagged Divisions

Engineering Economy Division (EED)

= 1000, Annual net income = +4000, n = 8years?” This example is Option A in the actual homework assignment shown in Fig. 1.When these prompts are entered in ChatGPT at the time of creating the assignment (September2024), the response includes a cash flow summary for years 0 to 8 and ChatGPT responds: “InExcel: =IRR(A1:A9) where A1 to A9 contains the cash flows.”The third prompt in the provided example is “What is the actual value?” This should yield i* =2.1%. However, ChatGPT responds: “you should find that the IRR (rate of return) isapproximately 9.65%.” This response by AI gives the professor an opportunity to explain in theexample the concepts of hallucinations and misalignment, where AI produces content that isuntrue or factually inaccurate

Conference Session

Engineering Technology Division (ETD) Technical Session 1

Collection

2025 ASEE Annual Conference & Exposition

Authors

Asif Ahmed, State University of New York, Polytechnic Institute; SATHVIKA KATIKANENI, State University of New York, Polytechnic Institute; Ashish Gunda, State University of New York, Polytechnic Institute

Tagged Divisions

Engineering Technology Division (ETD)

management and infrastructure resilience. This paper exploreshow generative AI can be leveraged by engineering educators to teach students advancedtechniques for wildfire prediction and geospatial analysis. Focusing on the use of generative AIin the classroom, the methodology demonstrates how students can engage with platforms likeGoogle Earth Engine to access and analyze satellite imagery and environmental datasets, such asMODIS Active Fire Detections and LANDSAT/Sentinel Burn Severity. By integrating generativeAI tools (e.g., ChatGPT, Gemini), educators can guide students through the process ofautomating code generation for wildfire location mapping, enhancing their problem-solvingskills and technical competence. The use of generative AI

Conference Session

Professional Papers

Collection

2025 ASEE Southeast Conference

Authors

Fazil T. Najafi, University of Florida; Vani Ruchika Pabba, University of Florida; Rajarajan Subramanian, Pennsylvania State University, Harrisburg, The Capital College; Sofia M Vidalis, Pennsylvania State University, Harrisburg, The Capital College

Tagged Topics

Diversity, Professional Papers

research-based assignments has been exploredless. This study investigates the efficiency and fairness of using AI, specifically ChatGPT, tograde theoretical understanding and research paper assignments in undergraduate and graduatecourses. The research was conducted in two phases. In the first phase, we assessed ChatGPT'sperformance in grading assignments, focusing on time efficiency, consistency, and gradingpatterns. We compared AI-assisted grading with traditional human grading methods in thesecond phase. We then analyzed variations in scores, potential biases, and feedback'sperceived usefulness. We conducted surveys to gather perceptions from both students andeducators regarding AI-based grading.The results indicated that AI-assisted grading

Conference Session

Harnessing AI and Collaborative Platforms to Personalize and Innovate K-12 STEM Curriculum

Collection

2025 ASEE Annual Conference & Exposition

Authors

Julie M. Smith, Institute for Advancing Computing Education; Monica McGill, Institute for Advanced Engineering; Jacob Koressel; Bryan Twarek

Tagged Divisions

Pre-College Engineering Education Division (PCEE)

Computer Science As a Test Case (Research to Practice)AbstractIntroduction: Because developing integrated computer science (CS) curriculum is aresource-intensive process, there is interest in leveraging the capabilities of AI tools, includinglarge language models (LLMs), to streamline this task. However, given the novelty of LLMs,little is known about their ability to generate appropriate curriculum content.Research Question: How do current LLMs perform on the task of creating appropriate learningactivities for integrated computer science education?Methods: We tested two LLMs (Claude 3.5 Sonnet and ChatGPT 4-o) by providing them with asubset of K-12 national learning standards for both CS and language arts and

Conference Session

ECE-Grading Approaches and Assessment Practices

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ilya Mikhelson, Northwestern University

Tagged Divisions

Electrical and Computer Engineering Division (ECE)

. However, there are a few restrictions: • The minimum version of ChatGPT is specified. • Catching ChatGPT making oversimplifications is insufficient. • No purposely incorrect questions are allowed.After finding a misconception, students have to analyze the error and provide a detailedexplanation of what makes ChatGPT wrong. Additionally, they have to explain the concept thatwould correct its misunderstanding.For documentation, students must provide a link to the conversation, so that it is impossible tofake. However, there is still room for academic dishonesty, as there is no way to know if the ideawas suggested by someone else.3 Results and DiscussionWhile the assessment strategy has been well received and enjoyed by students

Conference Session

First-Year Programs Division (FPD) Technical Session 13: Equity in Action - Identity, Mentorship, and Inclusion

Collection

2025 ASEE Annual Conference & Exposition

Authors

Varun K Kasaraneni, Gannon University; Lin Zhao, Gannon University; Longfei Zhou, Gannon University; Xiaoxu Ji, Gannon University

Tagged Divisions

First-Year Programs Division (FPD)

assignments. Thispaper presents the results of the surveys, analyzing students' initial perceptions, expectations, andexperiences with AI tools, as well as the strategies they employed to enhance their interactionswith ChatGPT during a structured assignment 2. MethodsThis study utilized a survey-based approach to examine the understanding, expectations, andperceived applications of artificial intelligence (AI) among first-year engineering and computingstudents. The survey was designed to capture the students’ initial perceptions prior to any formallecture on AI tools and their perspectives after engaging with the lecture content.The study included participants enrolled in an "Introduction to Engineering and Computing"course at xxxxxx University

Conference Session

Multidisciplinary Engineering Division (MULTI) Technical Session 8

Collection

2025 ASEE Annual Conference & Exposition

Authors

Resha Tejpaul, University of Minnesota ; Travis Henderson; Nicole Kennedy; Nikos Papanikolopoulos, University of Minnesota - Twin Cities

Tagged Divisions

Multidisciplinary Engineering Division (MULTI)

findings reflect broadertrends observed in higher education; for instance, a recent global survey indicated that 24% ofstudents employ AI tools daily, and over half utilize them at least weekly [7]. Our cohort, beingpart of a technology-oriented graduate program, appears even more inclined toward frequent useof GenAI.Figure 1: Distribution of self-reported frequency of GenAI tool usage among Robotics MS students(N=19). About 47.3% use GenAI tools “Frequently” (weekly) or “Almost Daily,” while a minority(about 5.3%) use them rarely or never.As shown in Figure 2, ChatGPT is the leading GenAI platform among students, with nearly 90%utilizing it for academic tasks. Google’s Bard is accessed by approximately 50% of students,serving as an alternative to

Conference Session

Mechanics Division (MECHS) Technical Session 1A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jacklyn Wyszynski, University of Pittsburgh; David Adam DeFrancisis, University of Pittsburgh; David Pabst, University of Pittsburgh; Lee Allen Dosse, University of Pittsburgh; Matthew M. Barry, University of Pittsburgh

Tagged Divisions

Mechanics Division (MECHS)

propulsion systems and Engineering Education. ©American Society for Engineering Education, 2025 ACE up your Sleeve: An Analysis of Student Generative AI Usage in an Engineering Statics CourseAbstractRapid technological advancements, including the emergence of computer-aided design andsimulation, have had a significant impact on the engineering industry. This, in turn, extends toengineering education, demonstrating a similar influential effect. The latest development to havesuch reverberations is the launch of a generative artificial intelligence (AI) chatbot known asChatGPT. ChatGPT utilizes a large language model (LLM) that trains the platform to understandand generate human-like responses

Collection

2025 ASEE PSW Conference

Authors

Siyuan Meng, University of Southern California

Tagged Topics

Diversity

support all students, with particular attention to hearing-impaired learners inAME308: Computer-Aided Design (CAD). Hearing-impaired students received standard OSASaccommodations, including 1.5x extended time for homework/exams and direct TA support,while AI tools were adopted to address pedagogical gaps. The course’s dynamic nature—evolvingthrough student interactions—rendered traditional notes inadequate. To bridge this gap, lecturerecordings and AI-generated summaries (created using ChatGPT) were provided to all students,benefiting those with hearing impairments, temporary absences, or diverse learning needs.The approach leveraged ChatGPT to transform Zoom subtitles and lecture materials intostructured previews, reviewed by TAs for accuracy

Collection

2025 Northeast Section Conference

Authors

Seyedeh Elham Kamali; Reihaneh Samsami

navigate these challenges carefully to educational frameworks represents a significant shift in fully utilize the benefits of AI while preparing students to the way engineering education is approached, with critically engage with the technology (Qadir, 2022). Generative AI tools like ChatGPT leading this The complexity of modern engineering problems transformation. These tools are not only reshaping demands that students not only possess technical curriculum design but are also enhancing the way knowledge, but also advanced problem-solving skills. AI educational content is delivered and interacted with by tools can be instrumental in developing

Collection

2025 Northeast Section Conference

Authors

Rachmadian Wulandana

Analysis (FEA) use AI tools, particularly version of ChatGPT to approximately 500,000 students andChatGPT, to support their learning. A survey examined whether faculty members across 23 campuses [4]. This initiative aimedAI tools help students understand concepts, solve problems to provide personalized tutoring and assist faculty withefficiently, and enhance convenience with 24/7 availability. It also administrative tasks. Similarly, Estonia launched a nationaladdressed their motivational impact, reliability, and concerns initiative in February 2025 to teach AI skills to high schoolabout dependency. The survey statements included questions students, partnering with tech companies like OpenAI

Collection

2025 Northeast Section Conference

Authors

Kalyan Khatry; Reihaneh Samsami

Gen applications in education [1]. In 2022, the public release ofAI (e.g., GPT-4 and DALL-E) have radically expanded AI LLMs like OpenAI’s ChatGPT demonstrated that AI couldcapabilities and access to teaching and learning [2]. Not only engage in sophisticated dialogue, solve complex problems,do these advances enable AI to assess learning data, but they and assist with content generation in real time. Soon after,also generate rich learning content and engage in natural multimodal models emerged that handle not just text but alsolanguage response, blurring the line between human andimagery and other data types, further broadening AI’s capacity enables educational interactions by explaining

Conference Session

Civil Engineering in the Age of AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Allen C Estes, California Polytechnic State University, San Luis Obispo; Peter Laursen, California Polytechnic State University, San Luis Obispo

Tagged Divisions

Civil Engineering Division (CIVIL)

on Python and creating the code for several assignedprograms, students are required to use ChatGPT or any other AI platform to create Python codefor a structural engineering application. As an embedded indicator for ABET Student Outcome7 (Lifelong Learning), students must learn and experiment with ChatGPT on their own. Assupport for Student Outcome 3 (Effective communication), students write an essay about theirresults, their AI experience, the learning strategies they applied, and the effectiveness andlimitations of using AI to write computer code. The students then use AI to rewrite their essayand comment on what they learned about the quality of their own writing.After running this exercise over three iterations of the ARCE 352 course

Conference Session

Civil Engineering in the Age of AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alex Campbell, Oklahoma State University; John J Phillips P.E., Oklahoma State University; Alan Kirkpatrick

Tagged Divisions

Civil Engineering Division (CIVIL)

least somewhat be preparingthem for AI in the workplace. However, a 2024 survey of College and University ChiefAcademic Officers by Inside Higher Ed found that only 14 percent have reviewed the curriculumto ensure it will prepare students for AI in the workforce. Considering this information, what canwe do as educators to ensure our students in fields related to structural engineering are preparedfor AI in the workplace?This paper will discuss the evaluation of current generative AI chatbots in their unmodifiedconfigurations for their use in fields related to structural engineering. Various generative AIchatbots, including ChatGPT, Claude, Gemini, CoPilot, and SE GPT will be asked the samequestions related to the field of structural engineering

Conference Session

Computers in Education Division (COED) Track 4.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Arezou Harraf; Yuetong Lin, Embry-Riddle Aeronautical University - Worldwide; A. Mehran Shahhosseini, Indiana State University

Tagged Divisions

Computers in Education Division (COED)

Worldwide in 2016 as an associate professor in the School of Engineering (formerly Department of Engineering and Technology).Dr. A. Mehran Shahhosseini, Indiana State University A. Mehran Shahhosseini is a Professor in the Department of Applied Engineering and Technology Management at Indiana State University. He has published over 65 articles in different journals and conferences ©American Society for Engineering Education, 2025 Leveraging AI-Based Tool to Guide Students on Literature Review: A Case StudyAbstractThis study aimed to compare the effectiveness of traditional literature review methods withAI-based search tools (ChatGPT 03Mini and Perplexity Pro Paid

Conference Session

Generative AI and Its Role in Industrial Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nadiye O. Erdil, University of New Haven

Tagged Divisions

Industrial Engineering Division (IED)

matching industry expectations.GAI, as defined by ChatGPT -a conversational AI model developed by OpenAI-, “refers to aclass of artificial intelligence models designed to create new content, such as text, image, audio,or video, that resembles human-generated data. These models "generate" content by learningpatterns from large datasets during their training process”. Given its capabilities and rapidadoption across industries, integrating GAI into technical training may become essential forpreparing students for the workforce.With this in mind, this research aims to identify Industrial Engineering (IE) areas with significantGAI activity and use these insights to explore how IE education can be enhanced to better equipgraduates for the evolving job

Conference Session

Computers in Education Division (COED) Track 2.D

Collection

2025 ASEE Annual Conference & Exposition

Authors

Melina O'Dell, University of Michigan; Andrew DeOrio, University of Michigan

Tagged Divisions

Computers in Education Division (COED)

course forum to use as prompts. Anexpert team of instructors evaluated each bot’s response for accuracy (hallucination) andhelpfulness. We used ChatGPT Pro as a baseline “generalist” chatbot.Overall, the specialist bots hallucinated less and were considered more helpful for studentquestions than the generalist bot. The best specialist bot was correct 80% of the time and helpful70% of the time. The generalist bot was correct 70% of the time and helpful only 26% of thetime. There were minimal performance differences between the specialist bots with varyingscopes.Our experience can guide educators using generative AI. First, a custom RAG chatbot is morehelpful than a general-purpose chatbot. Second, a single chatbot with a course-wide scope has

Conference Session

Technological and Engineering Literacy/Philosophy of Engineering Division (TELPhE) Technical Session 2

Collection

2025 ASEE Annual Conference & Exposition

Authors

Adeel Khalid, Kennesaw State University; Sanjeev Adhikari, Kennesaw State University

Tagged Topics

Diversity

Tagged Divisions

Technological and Engineering Literacy/Philosophy of Engineering Division (TELPhE)

Engineering Education, 2025 Educators’ Perspectives on the use of Generative AI Tools in Teaching and Educational Research in EngineeringAbstractSince the release of ChatGPT by OpenAI in November 2022, the integration of generative AI(genAI) into teaching and education has gained significant attention and experienced rapid growthwithin university engineering programs. This paper investigates the application of genAI inengineering education and research, focusing on the potential benefits and challenges of itsadoption. Specifically, the study: A) Analyzes how educators and students perceive and utilizegenAI and ChatGPT in engineering education; B) Explores the advantages, challenges, andlimitations associated with these technologies

Conference Session

Computers in Education Division (COED) Track 2.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Tammy Mackenzie, The Aula Fellowship; Lisa D. McNair, Virginia Tech; Rubaina Khan, University of Toronto; Animesh Paul, University of Georgia; Sreyoshi Bhaduri, Private Corporation

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

designs in mechanical engineering [65], making ethical choices during prototypingin time-sensitive situations such as hackathons [66], and learning disciplinary skills needed fordesign projects through personalized learning [67]. Lastly, a handful of papers explore howGenAI tools can give timely, relevant, and epistemic feedback during design. One exampleis the use of ChatGPT to analyze progress reports, instrumental to team collaborations, byrecommending readability improvements and clarifying complex ideas [68].3.3 PositionsOur review found 33 position papers revealing diverse viewpoints on its integration, eth-ical considerations, and potential applications of GenAI in EE. Specifically, these papersare where authors argue their stance on or

Conference Session

ELOS Technical Session 4: Empowering Student Learning Through Design, Integration, and Assessment

Collection

2025 ASEE Annual Conference & Exposition

Authors

Johannes Kubasch, University of Wuppertal; Dominik May, University of Wuppertal; Doha Meslem, Bergische Universität Wuppertal

Tagged Divisions

Experimentation and Laboratory-Oriented Studies Division (DELOS)

applications inlaboratory-based engineering education. The research questions of the systematic literaturereview correspond to the overall research questions (see Introduction). In the further course ofthe literature research, the research questions were broken down into key components and 11main keywords were defined. The keywords are “lecturers” and “students”, “NLP” and “AI”,“engineering education”, “potential”, “risk” and “limitations”, “feedback”, “competencies”and “laboratory”. In addition, synonyms “ChatGPT”, “gen AI”, “higher education”, “technicaleducation”, “advantages”, “changes”, “opportunities”, “critics”, “disadvantages”,“experiences”, “digital labs” and “online labs” were used, to name but a few. Search stringswere defined from the

Conference Session

DSAI Technical Session 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Julie M. Smith; Jacob Koressel; Sofia De Jesus, Carnegie Mellon University; Joseph W Kmoch; Bryan Twarek

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

textual data. For instance, we recently codedapproximately 10,000 state K-12 computer science standards, requiring over 200 hours of workby subject matter experts. If LLMs are capable of completing a task such as this, the savings inhuman resources would be immense.Research Questions: This study explores two research questions: (1) How do LLMs compare tohumans in the performance of an education research task? and (2) What do errors in LLMperformance on this task suggest about current LLM capabilities and limitations?Methodology: We used a random sample of state K-12 computer science standards. We comparedthe output of three LLMs – ChatGPT, Llama, and Claude – to the work of human subject matterexperts in coding the relationship between each state

Conference Session

ME Division Technical Session 2 - Harnessing AI and Machine Learning to Transform ME Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Harrison Zimmerman Brown, Worcester Polytechnic Institute; Reza Ebadi, Worcester Polytechnic Institute

Tagged Topics

Diversity

Tagged Divisions

Mechanical Engineering Division (MECH)

ThermodynamicsAbstractGenerative artificial intelligence (GenAI) has become ubiquitous. Convincing languagecomplemented by constant modifications and upgrades have made GenAI models, such asOpenAI’s ChatGPT, an appealing tool to address complex problems. According to a survey byIntelligent.com nearly a third of college students in AY 2022-2023 used ChatGPT for schoolworkand 77.4% of them were likely to recommend using it to study to another student. Despite theirappeal, these models have proven flawed in answering technical prompts. Their convincinglanguage may entice the user to trust the responses without verifying them. For example, theauthors failed to retrieve accurate thermodynamics properties of some common substances fromthree publicly available models (OpenAI’s

Conference Session

Generative AI and Its Role in Industrial Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

THOMAS AMING'A OMWANDO, Simpson University; Adel Alhalawani, Rose-Hulman Institute of Technology; Ashutosh Khandha, University of Delaware; Bhavana Kotla, The Ohio State University

Tagged Topics

Diversity

Tagged Divisions

Industrial Engineering Division (IED)

Biomechanics, Medical Devices, Clinical Imaging and Bioinstrumentation.Dr. Bhavana Kotla, The Ohio State University Visiting Assistant Professor, Department of Engineering Education, College of Engineering, The Ohio State University ©American Society for Engineering Education, 2025 Assessing the Impact of Generative AI in Developing and Using Grading Rubrics for Engineering CoursesAbstractEngineering education is rapidly integrating generative artificial-intelligence (GenAI) tools thatpromise faster, more consistent assessment—yet their reliability in discipline-specific contextsremains uncertain. This mixed-methods study compared ChatGPT-4, Claude 3.5, and PerplexityAI across

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

Tagged Divisions

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

Innovations and Inclusion in Pre-Service and Pre-College STEM Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Christine Liebe, Colorado School of Mines; Sabina Anne Schill, Colorado School of Mines; C. Estelle Smith, Colorado School of Mines; Jesan Ahammed Ovi, Colorado School of Mines

Tagged Divisions

Pre-College Engineering Education Division (PCEE)

STEM majors to reconnect with and definehuman talents and abilities to solve human problems and develop technological solutions.IntroductionGenerative Artificial Intelligence (GenAI) offers tools to transform K-12 science, engineering,technology, and mathematics (STEM) education. Teachers can use GenAI technology such asChatGPT to supplement their teaching methods or create content such as course outlines andquizzes; students can use it to help with homework and to receive formative feedback on theirwork [1, 2]. ChatGPT is a large-lanuage model (LLM) chatbot; it generates human-like textresponses based on training from a large amount of data [3]. A March 2023 survey of 1,002 K-12teachers found that over half of respondents (51%) reported using

Conference Session

Computers in Education Division (COED) Track 2.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Venkata Alekhya Kusam, University of Michigan - Dearborn; Zheng Song, University of Michigan - Dearborn; Khalid Kattan, University of Michigan - Dearborn; Bruce R Maxim, University of Michigan - Dearborn

Tagged Divisions

Computers in Education Division (COED)

, zhesong, bmaxim, kkattan}@umich.edu Department of Computer and Information Science, University of Michigan-Dearborn, USAAbstractThis paper presents an investigation into the use of Generative AI (GenAI), specifically ChatGPT,to automate quiz generation in higher education by conducting a case study in a graduateArtificial Intelligence (AI) course. The study aims to compare the quality and relevance ofAI-generated quizzes with manually created ones, addressing a critical question in computerscience education: Can Generative AI effectively support educators in creating assessments thatalign with course learning objectives?We conducted the study in a graduate-level AI course, which involved 47 students, one instructorand one

Conference Session

Civil Engineering Division (CIVIL) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Priyantha Wijesinghe, University of Vermont; Holly Ann Buckland Parker, University of Vermont; Ethan Zachary Stein, University of Vermont

Tagged Divisions

Civil Engineering Division (CIVIL)

civil engineering and architecture professions[2], while the second article explores the integration of human and artificial intelligence in civilengineering [3]. In their analysis, the instructor asked the students to discuss how GenAI couldenhance efficiency and safety in the field and the ethical challenges associated with GenAI use.Students were asked to critically assess the AI-generated output, verify facts, and document theirinteractions with the GenAI tool by providing all prompts. Additionally, students were asked toprovide a brief reflective analysis of the AI tools they used, such as ChatGPT, and how theseinfluenced their learning. II. The Course and the AssignmentsStructural Steel Design is a senior-level elective and one of

Conference Session

Mechanics Division (MECHS) Technical Session 1A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Dave Kim, Washington State University-Vancouver; Charles Riley P.E., Oregon Institute of Technology

Tagged Divisions

Mechanics Division (MECHS)

, particularly alarge language model (LLM), in writing education, the systematic studies related to the ethicaluse of GAI are limited. While grounded in the ethical adaptation of GAI in grading and feedbackfor engineering lab writing, we focus on GAI’s capability to assist with engineering lab reportassessment. Lab report grading is time-consuming for lab instructors and teaching assistants.Moreover, constructing impactful feedback can be challenging for many reasons. In this pilotstudy, we used Copilot and ChatGPT 4o to conduct evaluation and feedback on student labreports of past courses when the instructors did not use generative AI technologies. The studyspace was limited to the two engineering labs in two institutions: strength of materials

Conference Session

Faculty Development: Grading and Artificial Intelligence

Collection

2025 ASEE Annual Conference & Exposition

Authors

Deborah Moyaki, University of Georgia; Nathaniel Hunsu, University of Georgia

Tagged Divisions

Faculty Development Division (FDD)

equitable and effective AI integration. By carrying out thiscomprehensive review, our study will guide future research, inform policy decisions, and supportthe development of AI-enhanced educational practices that are both innovative and inclusive.AppendicesAppendix A Database Search Term Using Inclusion Criteria Number ERIC (Via (Belief* OR attitude* OR perception* OR behavior OR teaching 116 EBSCO) OR assessment OR pedagogy OR practice* OR challenge* OR adoption OR implementation) AND (Engineer*) AND (("generative artificial intelligence" OR "generative AI" OR "Gen AI") OR (chatbots OR ChatGPT OR "Microsoft Copilot" OR Gemini or LLaMA) OR (LLM