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

WORKSHOP III: From Ideas to Action: Integrating Entrepreneurial Mindset in FYE Programs

Collection

FYEE 2025 Conference

Authors

Kaitlin Mallouk, Rowan University; J. Blake Hylton, Ohio Northern University; Jack Bringardner, Colorado School of Mines; Krista M Kecskemety, The Ohio State University; Cassie Wallwey, Virginia Polytechnic Institute and State University; Andrew Charles Bartolini, University of Notre Dame

Tagged Topics

FYEE 2025

that can be applied to any activity to facilitate EM integration‬ ‭●‬ ‭A strategy to address stakeholder concerns‬‭AI Acknowledgement‬‭This proposal was originally written as two separate proposals by the workshop presenters with‬‭no support from generative AI. ChatGPT was used to combine the two original proposals into a‬‭single proposal and the resulting description was edited for clarity and accuracy.‬

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

Collection

2025 Rocky Mountain Section Conference

Authors

John M. Santiago

andresearchers are turning to emerging technologies and pedagogical frameworks that blend technicalinstruction with creativity, curiosity, and value creation—hallmarks of the entrepreneurial mindset. Thispaper presents a scalable approach for integrating AI-assisted multimedia tools, specifically Camtasia,into engineering and STEAM education to foster the development of an entrepreneurial mindset alignedwith the Kern Entrepreneurial Engineering Network (KEEN) framework.Leveraging AI tools such as ChatGPT, DALL·E, and text-to-speech technology AI software, educators andstudents can rapidly generate engaging instructional scripts, visuals, and voiceovers. These elements aresynthesized into compelling narrative-driven learning modules using Camtasia. This

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

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Bobby F Hodgkinson, University of Colorado Boulder

Tagged Divisions

Experimentation and Laboratory-Oriented Studies Division (DELOS)

identifying effective strategies for algorithm design 2 . This method isparticularly useful in computational contexts, where understanding the “why” behind code is ascrucial as the “how” 2 . In addition, AI tools, such as ChatGPT, can be used as an educationalresource to support learning and research, but educators need to be proficient in their use tointegrate them effectively 6,3 . However, AI cannot replace key higher order skills, as was shownwhen analyzing AI-generated laboratory reports in chemistry, which highlighted severaldeficiencies, such as inability to maintain consistency, generate references, and suggestexperimental errors 3 .In the realm of computational thinking, algorithmic explanations can serve as a powerful meansof instruction

Collection

2025 ASEE -GSW Annual Conference

Authors

Brinley Boyett, Texas A&M University; Robert Harold Lightfoot Jr, Texas A&M University

evolving. In recent years, the development of advancedlarge language models like ChatGPT offer educators powerful tools to enhance teaching practicesand improve classroom experiences. While these tools offer considerable benefits, AI can performtasks such as generating essays, writing code, or solving problems, allowing students to bypassactive learning and rely on the tool to complete their work for them. Previous research hasexplored how instructors and students feel about integrating AI into the classroom as anotherresource. Moving forward, the goal of this study is to build on existing findings and offer newinsights into the perceived benefits and limitations of integrating AI into education by focusing onstudent perceptions of its impact on

Conference Session

Computers in Education Division (COED) Track 2.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Griffin Pitts, University of Florida; Viktoria Medvedeva Marcus, University of Florida; Sanaz Motamedi, University of Florida

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

ChatGPT [1], Google’s Gemini [2],and Microsoft’s Copilot [3] have gained widespread student adoption due to their free access andease of use. This expansion has occurred amid varying acceptance [4–6] and trust [7] in digitallearning technologies across student populations through the COVID-19 pandemic and into thepresent day. Approximately one-third (35.4%) of students reported regular usage of ChatGPT,while 47% expressed concern about AI’s impact in education [8]. Additionally, 60% reported thattheir instructors or schools had not yet provided guidelines for ethical or responsible AI tool use [8].As students increasingly use available online AI assistants, researchers have concurrently devel-oped specialized educational AI tools designed

Conference Session

Computing and Information Technology Division (CIT) Technical Session 1

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alireza Kavianpour, DeVry University, Ontario,CA

Tagged Divisions

Computing and Information Technology Division (CIT)

Engineering Education (ASEE), Portland, OR, USA, 2024.[2]- Cherniak, E., et al. “Artificial intelligence programming”. Psychology Press, 2014.[3]- Winston, H. Artificial intelligence, 3rd ed. Addison-Wesley Longman Publishing Co.,Inc.1984.[4]- Phillips, T., et al. “Exploring the use of GPT-3 as a tool for evaluating text-basedcollaborative discourse”, Companion Proceedings 12th intl. Conf. on learning Analytics &Knowledge, 2022.[5]- Modern Mind Publications, Generative AI for Beginners Made Easy: Master ArtificialIntelligence and Machine Learning Fundamentals, Learn Creative AI, and Enhance Your SkillsISBN-13: 979-8320061238, Modern Mind Publication, 2024.[6]- Felix, V. ChatGPT for Beginners: Prompt Engineering Made Easy, 2024.[7]- Robert, C

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Daniel Kane, Utah State University; Wade H Goodridge, Utah State University; Linda Davis Ahlstrom, Utah State University; Oenardi Lawanto, Utah State University; Michaela Harper, Utah State University; Cassandra J McCall, Utah State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

publications have been recognized by leading engineering education research journalsat both national and international levels. Dr. McCall has led several workshops promoting the inclusionof people with disabilities and other minoritized groups in STEM. She holds B.S. and M.S. degrees incivil engineering with a structural engineering emphasis. ©American Society for Engineering Education, 2025 WIP: Understanding Patterns of Generative AI Use: A Study of Student Learning Across University CollegesIntroductionDue to the relatively recent introduction of AI to academia, facilitated by the development andrelease of popular generative AI systems such as ChatGPT, few studies have examined theeffects of AI use on

Collection

2025 Northeast Section Conference

Authors

Lina H. Kloub, University of Connecticut; Vraj Patel; Tina Huey

artificial intelligence (ai): Understanding the potentialalready known to the respondents, despite the possibility of benefits of chatgpt in promoting teaching and learning,” SSRN, 2023.response (acquiescence) bias. [Online]. Available: https://ssrn.com/abstract=4337484 Finally, the rapid evolution of AI technologies poses chal- [3] P. A. Barrett, A., “Not quite eye to a.i.: student and teacher perspectives on the use of generative artificial intelligence in the writing process,”lenges in capturing a static picture of instructor attitudes. AI Int J Educ Technol High Educ, vol. 20, 2023.models and tools

Collection

2025 Rocky Mountain Section Conference

Authors

Eric Bogatin; John Lettang

experienceswith other teams, to ask instructors for help, and to use the Internet as a resource.Recently, we have seen teams leverage AI tools such as ChatGPT, Google Gemini, andClaude.When they ask an instructor for additional resources, like double-sided tape, the answer isyes. Sometimes, when they want to use a microcontroller other than the Atmega 328provided, the answer is no, and they have to develop a plan B.The kitWe have used various starting kits for the autonomous cars. One from Adafruit is typical ofour kits: Mini Round Robot Chassis Kit - 2WD with DC Motors : ID 3216 : Adafruit Industries,Unique & fun DIY electronics and kits. This kit is about $20. Other similar kits are availablefrom ebay, such as this one: Avoidance Tracking Smart

Conference Session

GIFTS II

Collection

FYEE 2025 Conference

Authors

Osman Sayginer, Temple University; Cory Budischak, Temple University

Tagged Topics

Diversity, FYEE 2025

-a = 0.25 -x = 4 ::Numeric Precision:: -x = 0 What is the density of water at room temperature in kg/m³? ::Numeric Precision:: [!Numeric!] [1000+-5] How many km in a marathon? ::Numeric Range:: [!Numeric!] [42.2+-0.1] What is the typical range of efficiency (%) for a modern gas turbine? ::Numeric Range:: [!Numeric!] [30 40] Normal body temp in Celsius? [!Numeric!] [36.5 37.5]Figure 2. Example of an input prompt used to generate quiz questions (left) and the resultingoutput generated by ChatGPT (right).Preliminary Results and DiscussionInitial trials

Conference Session

Entrepreneurship & Engineering Innovation Division (ENT) Poster Session

Collection

2025 ASEE Annual Conference & Exposition

Authors

Keilin Jahnke, University of Illinois Urbana-Champaign

Tagged Divisions

Entrepreneurship & Engineering Innovation Division (ENT)

future role of generative AI in creativity and design, how you might utilize what you have learned in the course, and reflections on what you are learning in the course and the creative process. You should plan on writing at least a few paragraphs in this section every week.Figure 2. Descriptions for the sections that constitute the Foundational Creativity part of the Creativity Portfolio. Part 2: AI + Creativity Now is the time to use AI. For this section, please use your preferred generative AI tool (examples include Microsoft Copilot, Gemini, and ChatGPT) and write down which one(s) you used. Please record every prompt and output (yes, these sections will be long). Make sure your prompts are