Conference Session

Computers in Education Division (COED) Track 3.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Randy McDonald, Texas A&M University; Salvatore Enrico Paolo Indiogine; Nasiha Lachaud, Texas A&M University; Wei Lu, Texas A&M University; Mohammad Affan Khokhar

Tagged Divisions

Computers in Education Division (COED)

was used only in a few instances, but given the focus of the course, in mostcases, it did not meet the expectations of the professor.Background and Related LiteratureThe impact of ChatGPT has led to a significant increase in awareness and experimentation withgenerative AI tools among educators since its release in November 2022 [1]. As generativeartificial intelligence technologies have emerged onto the landscape of higher education, therehas been a healthy research interest in how students are using AI to promote their success inclasses, how faculty might integrate AI into their teaching, and how staff employees, in general,might use AI to work more efficiently [2]. The use of generative AI in all these areas isconsiderably nascent and needs

Conference Session

ERM Technical Session: Improving Assessment in Engineering Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

David Coulter Jangraw, University of Vermont; Anneliese Marie Shoudt; Courtney D Giles, University of Vermont

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods Division (ERM)

hypotheses for future research.A preliminary qualitative analysis of Ticket Home student exit surveys and TA Ticket Homesummaries used ChatGPT to identify common themes. The surveys were first manuallyanonymized by removing names, then entered into ChatGPT with the prompt “Please summarizethese responses to the question: . List the most common themes and how manyresponses mentioned each of them.” While a similar approach has been used successfully forthematic analysis before [20], our approach involves different data and prompts; it shouldtherefore be considered preliminary and subject to a more extensive validation. To assess theapproximate accuracy of this approach, a human rater manually identified the four most commoncodes identified by ChatGPT

Conference Session

Engineering Libraries Division (ELD) Technical Session 3

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kristina Bloch, University of Louisville; Campbell R Bego, University of Louisville

Tagged Divisions

Engineering Libraries Division (ELD)

an ongoing collaboration between anEngineering Fundamentals professor and the Engineering Librarian at a largesuburban university in the Southeast. In this study, a purposive sample of sixstudents in an Introduction to Engineering course participated in semi-structuredinterviews regarding the student experience of course- integrated GenAI research intheir class.Researchers utilized Charmaz’s constructivist grounded theory to analyze the data[13]. ChatGPT-3.5 was not utilized in the analysis or in writing this article. Thisstudy was approved by the Institutional Review Board (Reference Number794713) at the University of LouisvilleParticipantsAll study participants were enrolled in the Introduction to Engineering course atthe University of

Conference Session

Curriculum Development and Pedagogical Innovations

Collection

2025 ASEE Annual Conference & Exposition

Authors

William H Guilford, University of Virginia

Tagged Divisions

Biomedical Engineering Division (BED)

according topredefined rubric criteria, which aligned with levels of DPK. Each student's pre and postresponses were evaluated for the presence or absence of rubric categories. ChatGPT providedconsistency in applying the rubric across the dataset and sped the analysis. Further, for eachrubric criterion, ChatGPT highlighted specific examples and patterns of alignment ormisalignment with rubric, allowing for manual validation of the results.Statistical comparisons were performed using SPSS. Scores were ordinal and not normallydistributed. Therefore, a Wilcoxon Signed Ranks test was used for comparison of pre-post paireddata. A Mann-Whitney test was used to compare beginning of semester scores between the twosemesters.ResultsBefore the curriculum

Conference Session

First-Year Programs Division (FPD) Work-in-Progress 3: Integration of Math, Computing, and AI in First-Year Courses

Collection

2025 ASEE Annual Conference & Exposition

Authors

Bishweshwor Rimal, University of Texas at Austin; Sneha Ballabh, University of Texas at Austin; Nina Kamath Telang, University of Texas at Austin

Tagged Divisions

First-Year Programs Division (FPD)

articlesummarizing the impact of ChatGPT on a variety of engineering assessments, the authorsconcluded that introductory-level programming assessments can be very accurately solved byChatGPT, and that instructors must add complex features to their assessments to deter studentcheating [7].However, there is no published research on the usage of generative AI to offer customizedquestion banks and explanations of course concepts based only on course materials, for anintroductory computing course, within the learning management system, thereby providing apersonalized learning experience tailored to each student’s needs. The primary innovation in ourstudy lies in ensuring that the generated questions remain entirely focused on the course content,strictly avoiding

Conference Session

Innovative Learning Tools and Visualizations in ECE Curriculum

Collection

2025 ASEE Annual Conference & Exposition

Authors

Raiyan Ishmam, University of Toronto; Salma Emara, University of Toronto; Hamid S Timorabadi P.Eng., University of Toronto

Tagged Divisions

Electrical and Computer Engineering Division (ECE)

used search en-gines—primarily Google—or increasingly, LLM-based services like ChatGPT. While Googleprovides reliable but fragmented information, ChatGPT can produce more coherent, user-friendlysummaries but may occasionally “hallucinate” or fabricate content. Users relying on ChatGPTmust manually verify any suggested sources.Despite these limitations, ChatGPT still offers a valuable starting point for discovering potentiallyrelevant books, especially for non-expert users. We leveraged ChatGPT-4o to generate candidateground truth data, then applied rigorous human verification to mitigate misinformation. Our pro-cedure was as follows: • We issued arbitrary queries (main query plus one or more topics) to ChatGPT, asking it to identify

Conference Session

Focus on the First Year

Collection

2025 ASEE Annual Conference & Exposition

Authors

Elin Jensen, Lawrence Technological University

Tagged Divisions

Civil Engineering Division (CIVIL)

the qualitative analysis was to determine if a thematic comparison of the studentteam deliverables show the progression of the project evolution from initial concept ideas toconcrete project solution. The three deliverables were: • Preliminary Project Proposal (Text-Heavy) • Impact Statement (Text-Heavy) • Final Presentation (Image-Heavy)Thematic analysis of the project deliverables from one (Team 4) of the seven teams wasconducted using OpenAI's ChatGPT, an AI language model, to identify and compare emergingthemes across the project stages [12]. After uploading the deliverables (student names wereremoved) ChatGPT extracted the text from the documents. ChatGPT was prompted to comparethe evolution of ideas over the project duration. In

Conference Session

ENT-1: Innovative Approaches to Student Engagement and Belonging in Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

Andrea T Kwaczala, Western New England University; Andrea Davis, Western New England University; Heidi Ellis, Western New England University

Tagged Topics

Diversity

Tagged Divisions

Entrepreneurship & Engineering Innovation Division (ENT)

-confidence in their individualskills in oral communication, specifically related to presentations, but these results requireadditional research to confirm these findings.Using AI to Assess Student Outcomes: Co-Pilot and ChatGPT were both used to evaluate pitchtranscripts using the Grading Rubric. The results of AI-evaluations were compared to the facultyevaluations using the same grading rubric. This was limited to transcripts of the pitch as anyonline video-based platform for video analysis was by paid subscription only. In identifyingavailable AI tools, some interesting subscription-based options we discovered. These tools focusspecifically on video analysis of body language and pitch performance, including uSpeek(Sarang, 2023) and Bodha (Cadet

Conference Session

Faculty Development Division (FDD) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Yen-Lin Han, Seattle University; Wan D. Bae, Seattle University; Julie Homchick Crowe, Seattle University; Matthew John Rellihan, Seattle University

Tagged Divisions

Faculty Development Division (FDD)

, health and wellness, and human biology. She received her PhD from the University of Washington.Matthew John Rellihan, Seattle University ©American Society for Engineering Education, 2025 Lessons Learned- Facilitating conversations around Generative AI and its Impact on Society among faculty from different disciplines in a Jesuit UniversityBackgroundSince the arrival of ChatGPT, generative AI has continued to shake up higher educationinstitutions. Many institutions have scrambled to identify strategies and set policies for teachingand learning for faculty and students. One important fact to pay attention to is that generative AIimpacts all disciplines—not only those with

Conference Session

NSF Grantees Poster Session I

Collection

2025 ASEE Annual Conference & Exposition

Authors

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

Tagged Topics

NSF Grantees Poster Session

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

Conference Session

AI in the Engineering Management Classroom

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ekaterina Koromyslova, South Dakota State University; Bishnu karki, South Dakota State University; Prafulla Salunke, South Dakota State University; Carrie Steinlicht, South Dakota State University; Gary Anderson, South Dakota State University

Tagged Topics

Diversity

Tagged Divisions

Engineering Management Division (EMD)

prompt to AI. Thus, a lack of effective communications skills can compromise thequality of the generated output if the question is not clearly formulated, and the prompts are notrefined or elaborated. Moreover, without an expert to evaluate the generated solution, there is adanger that the solution is based on incorrect or biased information [16]. Unless the decisionmakers are able to critically evaluate the generated solutions, they may make costly mistakes.Farrokhnia, Banihashem, Noroozi, and Wals [17] completed a SWOT analysis of ChatGPT – agenerative AI tool which is commonly used in higher education by instructors and students. Theyidentified the following weaknesses and threats of generative AI: • Lack of deep understanding of the

Conference Session

ERM Technical Session: Developing Engineering Competencies II

Collection

2025 ASEE Annual Conference & Exposition

Authors

Manish Malik SFHEA, MIET; Julie-Ann Sime FHEA, MBPsS, Lancaster University, UK

Tagged Divisions

Educational Research and Methods Division (ERM)

learning within engineering education. The synergies here could benefit the community andhelp address the challenges related to team working [8]-[11].An initial search on the ERIC database, restricted to just the last three years, revealed 329records that met our search criteria as defined later in the paper. The number is large enough totest GenAI assisted automation in the shortlisting and selection process. This automation wascarried out using Generative Artificial Intelligence (GenAI) tools such as ChatGPT® andNotebookLM®. The paper makes a methodological contribution in using a combination of: anovel approach of using synthetically generated abstracts for title and abstract shortlisting; use ofNotebookLM® for extracting data; and also using

Conference Session

New Engineering Educators (NEE) Technical Session 3 - Professional and Faculty Development

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jiawei Gong, Pennsylvania State University, Behrend College; Debalina Maitra, Kennesaw State University

Tagged Divisions

New Engineering Educators Division (NEE)

Advances in Materials and Processing 2.0 0 Technologies Wiley Solar RRL 7.9 123 Advanced Energy Materials 27.8 149 Advanced Functional Materials 19 96We asked the generative AI to generate a list of journals that accept literature review articlesfocused on PSC along with impact factors and publishing companies. ChatGPT provided theinitial list of suggested journals, after which our team of undergraduate researchers manuallyreviewed each journal. They verified the impact factor, assessed whether the

Conference Session

Engineering Ethics Division (ETHICS) Technical Session - Case Studies

Collection

2025 ASEE Annual Conference & Exposition

Authors

Mary Kay Camarillo, University of the Pacific; Luke S. Lee P.E., University of the Pacific; Yadhira Garcia Ruiz, University of the Pacific

Tagged Divisions

Engineering Ethics Division (ETHICS)

Study EvaluationGenAI was used to produce engineering ethics case studies. The following case studies wereused: Hurricane Katrina, Deepwater Horizon/Macondo Well Blowout, and Flint Michigan WaterCrisis. These case studies are well-known, routinely used in ethics courses, and described in themost recent edition of the textbook [21] that we use.The following GenAI tools were used: ChatGPT-4o, Gemini 1.5 Pro, and Microsoft Copilot.First, a simple prompt was used (Table 1). Then, a more detailed prompt was used that was basedon ABET [18] and the “CARE” case study evaluation method [19]. Detailed prompts wereentered using a single prompt in one chat session and then, in another chat session, the detailedprompt was used again where each question was

Conference Session

First-Year Programs Division (FPD) GIFTS Session 1: Human-Centered and Project-Based Innovation in First-Year Engineering Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ethan E Danahy, Tufts University

Tagged Divisions

First-Year Programs Division (FPD)

Mahabharata) ●​ Chinua Achebe (Nigeria) ●​ Sun Wukong (Monkey King, China, from "Journey to ●​ Jane Austen (England) the West") ●​ Hans Christian Andersen (Denmark) ●​ Aladdin (Middle Eastern, from "One Thousand and ●​ Khalil Gibran (Lebanon) One Nights") ●​ William Shakespeare (England) ●​ Elizabeth Bennet (England, by Jane Austen) ●​ Anna Karenina (Russia, created by Leo Tolstoy)Generative AI ToolsText Generation: Microsoft Copilot, Open AI’s ChatGPT, Google’s Gemini, Anthropic’s Claude,Perplexity AIImage Generation: Microsoft Copilot, Open AI’s ChatGPT, Canva, PIXLR, OpenArt.aiNote: rapidly changing innovation in the generative AI

Conference Session

First-Year Programs Division (FPD) GIFTS Session 1: Human-Centered and Project-Based Innovation in First-Year Engineering Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Michael Shteynberg, Northeastern University; Andrew L Gillen, Northeastern University; Kaylla Cantilina, Northeastern University

Tagged Divisions

First-Year Programs Division (FPD)

sub-branch of artificial intelligence that uses machinelearning. It allows machines to understand, analyze, and generate responses that are easy forhumans to understand. NLP already facilitates the interactions between our students and all sortsof artificial intelligence like chatbots (ChatGPT), smart assistants (Siri), and more. Calls formore integration of artificial intelligence into education grow louder by the day. For instance, aspecial committee was established in the US to make recommendations, including around AI ineducation [1]. Outside of academia, regular interaction with AI tools is becoming commonplacein industry. Scholars have already outlined a plethora of opportunities and concerns aroundapplying this technology in the

Conference Session

Computers in Education Division (COED) Track 4.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Madison Melton, University of North Carolina at Charlotte; Mohsen M Dorodchi, University of North Carolina at Charlotte

Tagged Divisions

Computers in Education Division (COED)

, images, and music [7],using deep learning models like GANs and transformers to generate original data by learningpatterns from training datasets. Unlike traditional machine learning, which primarily analyzes orclassifies existing data [8], GenAI models, such as OpenAI’s GPT, leverage large languagemodels (LLMs) trained on vast text datasets [9, 10]. Beyond LLMs, models like GANs, VAEs,and diffusion models further expand GenAI’s capabilities, with applications spanning NLP [11],art creation [12], and game design [13]. The release of ChatGPT quickly raised concerns aboutacademic integrity and student overreliance, potentially hindering learning due to its accessibility[14, 15]. However, these concerns have also driven interest in leveraging GenAI

Conference Session

Computing and Information Technology Division (CIT) Technical Session 8

Collection

2025 ASEE Annual Conference & Exposition

Authors

Danushka Bandara, Fairfield University; Djedjiga Belfadel, Fairfield University

Tagged Divisions

Computing and Information Technology Division (CIT)

education evolves to meet the needs of an increasingly technology-drivenworld, these conventional approaches face growing challenges.Simultaneously, the advent of generative artificial intelligence (GenAI) tools, such as ChatGPT, Claude,and Gemini, has brought transformative changes to the educational landscape. When ChatGPT was firstintroduced in 2023, it affected a fundamental shift in the role of the educator. These tools providestudents with powerful capabilities, including generating content, simplifying complex concepts, andautomating problem-solving processes. While GenAI tools have immense potential to enhance learningand teaching experiences, they also pose significant challenges, particularly in the context ofassessments (Swiecki,et al

Conference Session

Biomedical Engineering Division (BED) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xianglong Wang, University of California, Davis; Tiffany Marie Chan, University of California, Davis; Angelika Aldea Tamura, University of California, Davis

Tagged Topics

Diversity

Tagged Divisions

Biomedical Engineering Division (BED)

], rapidimprovement in the performance of these products, as reflected by one faculty’s experience ofPerplexity AI scoring 80% on their multiple choice-based engineering quiz, accentuated the needfor BME educators and students to improve AI literacy and cultivate responsible use of AI.ML algorithms are computer programs that improve their performance with more experience(data) [8]. Therefore, problems in the data used to train ML algorithms, such as demographicbiases, can be reflected in the performance of ML algorithms. In a BME context, GPT-4, whichpowers ChatGPT and Perplexity AI, showed strong ethnic biases when assigning medicalconditions such as HIV/AIDS [9], while GPT-4 and Gemini (also powers AI-enabled notebook,NotebookLM) showed negative perception

Conference Session

ME Division 11: Beyond the Build: Communication, Collaboration, and Reflection

Collection

2025 ASEE Annual Conference & Exposition

Authors

David R Mikesell P.E., Ohio Northern University; J. Blake Hylton, Ohio Northern University; Joshua Gargac, Ohio Northern University

Tagged Divisions

Mechanical Engineering Division (MECH)

areas. F08 did well overall, but should beencouraged to improve his or her timeliness in reviewing team submissions. Figure 2. Capstone advisor survey results for F02 Figure 3. Capstone advisor survey results for F08Answers to the free response questions on advisor strengths and areas for improvement weresubmitted to ChatGPT for analysis and summary. The data was first anonymized by replacingstudent and faculty names with a random-ordered research ID, changing gendered pronouns to“they,” and redacting identifying comments that could not otherwise be removed.The prompt given to ChatGPT was as follows: “Assume the role of an experienced highereducation administrator. You are reviewing student

Conference Session

Computers in Education Division (COED) Track 2.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Animesh Paul, University of Georgia; VINCENT OLUWASETO FAKIYESI, University of Georgia; Md Ulfat Tahsin, The Ohio State University; Lexy Chiwete Arinze, Purdue University at West Lafayette (COE); Sreyoshi Bhaduri, Private Entity

Tagged Divisions

Computers in Education Division (COED)

deconstructed the question using the Population, Concept, and Context (PCC)framework, a widely used approach in systematic literature reviews [7]. The PCC guidelines for this review arePopulation – engineering educators and students; Concept – utilization of generative models (e.g., GenerativeAI, ChatGPT, GPT); Context – formal and informal engineering education settings.3.2. Identifying Relevant StudiesThe search strategy is structured into concept lines, following the approach outlined in [8] for scopingreviews, which is designed to identify and include articles, conference papers, and gray literature relevant tothe research question. For the scope of our project, we define this as an "Aspect": An aspect is an element ordimension of the research

Conference Session

Materials Division (MATS) Technical Session 6

Collection

2025 ASEE Annual Conference & Exposition

Authors

Anurag Srivastava, Texas A&M University; Sayyad Basim Qamar, Texas A&M University; Saira Anwar, Texas A&M University; Bilal Mansoor, Texas A&M University

Tagged Divisions

Materials Division (MATS)

students so that they could provide constructive feedback on the writingaspects of the submission. The feedback was then collected and provided to thecorresponding group of students so that they could incorporate the feedback and improve thequality of their submissions.However, using Generative AI tools such as ChatGPT is changing how students writeassignments [17]. Using AI tools has its benefits as well as problems [18]. While the AI toolmay help students streamline their writing, a major issue can be that the AI tool makes thereport look very generic and provides incorrect technical information [19], which isdetrimental to the quality of the written work [20], [21]. To deal with the issue of the use ofAI tools, a special lecture is given to the

Conference Session

Engineering Libraries Division (ELD) Technical Session 3

Collection

2025 ASEE Annual Conference & Exposition

Authors

Laurie Borchard, California State University Maritime Academy; Amber Janssen, California State University Maritime Academy; William W. Tsai, California State University Maritime Academy

Tagged Divisions

Engineering Libraries Division (ELD)

, and combustion.However, broader interest in machine learning has been sparked by the release of generative AI(GAI) tools in the past few years. Large language models (LLMs) such as ChatGPT or GoogleGemini have brought machine learning to the creation of text. Similarly, GAI tools for imagecreation, such as DALL-E or Adobe Firefly, allow for the creation of images based on text-basedqueries. In education, this has sparked a great deal of interest among students and faculty in theethical application of this technology in the classroom. In “AI in Higher Ed: Hype, Harm, orHelp,” Anthology, an educational technology company, surveyed university student leadersabout student use and perception of AI [4]. Over 30% of university leaders believed the

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jason Daniel Christopher, U.S. Air Force Academy; Vincent Italo Bongioanni, United States Air Force Academy; Lauren V Scharff, U.S. Air Force Academy

Tagged Divisions

Mechanical Engineering Division (MECH)

calculator of the 1970s, and then to computer-based software andcomputational methods. Wolfram Alpha made a large step forward in the ability to solve a varietyof problems and explain the steps to learners everywhere. 2 Now, AI, and specificallylarge-language models (LLMs) such as ChatGPT provide the next evolution in solving complexproblems while showing detailed commentary on every step and calculation made. But AI’sability to aid an engineers in their endeavor to solve the world’s technical challenges is muchmore broad. A brief review of AI’s definition, emergence, and varied types is appropriate.2.1 Artificial Intelligence DefinitionA term as broad as ‘artificial intelligence’ is bound to have many definitions, most with significantoverlap

Conference Session

Computers in Education Division (COED) Track 2.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Stephany Coffman-Wolph, Ohio Northern University; Abigail Clark, Ohio Northern University; J. Blake Hylton, Ohio Northern University; Bryan Alan Lutz, Ohio Northern University; Gabriel Mott, Ohio Northern University

Tagged Divisions

Computers in Education Division (COED)

limitations. First, AI tools, such as ChatGPT andCopilot are relatively new, and constantly evolving in their abilities. Secondly, the sampledstudent work represented a small portion of available data and thus is not representative of thewhole set. Finally, while every effort was made to ensure that the evaluators were consistent withthe application of the rubric, it is simply not realistic to expect people with varying expertise tobe completely consistent. While these limitations were important to acknowledge, they do notlimit the importance of this work. We see potential not only for optimizing writing support butalso for fostering student-involved negotiations on how AI can aid the writing process.MethodsContextThis study occurred at a small

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Peter Jamieson, Miami University; Ricardo Ferreira, Universidade Federal de Viçosa; José Nacif, Universidade Federal de Viçosa

Tagged Divisions

Computers in Education Division (COED)

, personalized online learning experiences. We evaluate the effectiveness of this methodthrough a series of case studies and provide guidelines for instructors to leverage these technologiesin their courses.1 IntroductionLarge Language Models (LLMs) and their emerging skills provide educators with new capabilitiesto improve our teaching and save time. LLMs like ChatGPT have emerged as powerful tools thatcan assist in creating educational content and interactive learning experiences [1].For digital system design and computer architecture, traditional education often relies on expen-sive hardware, specialized software, and physical laboratory spaces. These requirements can limitaccess to hands-on learning experiences, particularly for students in

Conference Session

Architectural Engineering Division (ARCHE) Technical Session 2

Collection

2025 ASEE Annual Conference & Exposition

Authors

Miguel X. Rodriguez-Paz, Tecnologico de Monterrey (ITESM); Jorge A. Gonzalez-Mendivil, Tecnológico de Monterrey; Israel Zamora-Hernandez; Gibrán Sayeg-Sánchez, Tecnologico de Monterrey (ITESM); Luis Horacio Hernandez Carrasco P.E., Tecnologico de Monterrey (ITESM); Martha Elena Nunez, Tecnologico de Monterrey (ITESM); Graciela Donaxii Rodriguez Paz, Tecnologico de Monterrey (ITESM)

Tagged Divisions

Architectural Engineering Division (ARCHE)

: Students work in teams to start preparing their solutionto the challenge or the solution to a given assignment. It includes team work. These sessions can be upto one-third of the total number of sessions in the classroom.(4) Assignments for the Digital Transformation: Students work on set of problems, a beam-design orresearch papers on the biography of certain important engineer or researcher such as Euler orCastigliano. Artificial Intelligence tools such as Google Assistant or ChatGPT can be used to researchhistorical facts but not for the the solution of problems.(5) Did student attend the session? a) Yes, and student needs more help. Then it should make an appointment to meet professor online.If student is confident on the topic and

Conference Session

Leveraging AI and Computational Tools for Enhanced Learning

Collection

2025 ASEE Annual Conference & Exposition

Authors

Betul Bilgin, The University of Illinois at Chicago; Christopher V.H.-H. Chen, Columbia University; Stephanie Butler Velegol, The Pennsylvania State University

Tagged Divisions

Chemical Engineering Division (ChED)

outcomes. Recent studieshighlight the ability of generative AI tools to create dynamic course content, automate routinetasks, and provide real-time, adaptive feedback to students [1-3]. These features are particularlyvaluable in addressing the challenges of large class sizes and diverse student needs, making AI apromising tool for scaling high-quality education.In chemical engineering education, where problem-solving and quantitative reasoning are integral,AI tools like ChatGPT and discipline-specific software have shown promise in assisting withcomplex calculations, modeling, and conceptual understanding. For instance, AI-driven platformscan simulate chemical processes and provide students with interactive learning opportunities,enhancing their

Conference Session

Tech Session 3: Emerging Trends in Engineering Education: AI, Clean Energy, and Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Matthew Yukio Takara, Carnegie Mellon University; Fethiye Ozis P.E., Carnegie Mellon University; Allison E. Connell Pensky, Carnegie Mellon University

Tagged Divisions

Environmental Engineering & Sustainability Division (ENVIRON)

purposes have been steadily developed in the decades since, with the majority of AIEdresearch focusing on instructor-side AI tools used for administrative tasks such as automatedgrading, feedback, and content creation [16–20]. There are some cases of AI tools developed forlearner use, but they are more application-specific and are very different from the modern, morerobust generative AI tools of the present study [21, 22]. Recent years have seen the emergence ofpowerful generative AI tools, such as OpenAI’s ChatGPT and Google’s Gemini (formerly Bard)released in late 2022 and early 2023, respectively. These tools are examples of powerful largelanguage models (LLMs) capable of interpreting human language inputs and generating outputsresembling

Conference Session

First-Year Programs Division (FPD) Technical Session 4: Fostering Belonging - Identity, Self-Efficacy, and Retention

Collection

2025 ASEE Annual Conference & Exposition

Authors

Brian Patrick O'Connell, Northeastern University; Kathryn Schulte Grahame, Northeastern University; Richard Whalen, Northeastern University; Constantine Mukasa, Northeastern University; Susan F Freeman, Northeastern University

Tagged Topics

Diversity

Tagged Divisions

First-Year Programs Division (FPD)

using ChatGPT for high-level analysis. Datasets weremanually formatted to ensure consistent wrangling by the AI, using standardized key phrases andstructured formatting to enhance the AI's ability to parse and interpret the information accurately. Thisstudy implemented a simplistic segmentation, considering each sentence as a single statement, toimprove reliability and repeatability. This process was systematically repeated and refined by utilizingsubsets of the data with established qualities until preprocessing consistently achieved accurate parsingfollowing emerging best practices [23].Throughout the analysis, refinements were made to prompts and categorizations, ensuring alignmentwith the nuances of each reflection. Reanalysis occurred in