Conference Session

Educational Research and Methods Division (ERM) Technical Session 22

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mohammad Faraz Sajawal, University of Oklahoma; Javeed Kittur, University of Oklahoma

Tagged Divisions

Educational Research and Methods Division (ERM)

Paper ID #43991Examining Students’ Beliefs on the Use of ChatGPT in EngineeringMohammad Faraz Sajawal, University of OklahomaDr. Javeed Kittur, University of Oklahoma Dr. Kittur is an Assistant Professor in the Gallogly College of Engineering at The University of Oklahoma. He completed his Ph.D. in Engineering Education Systems and Design program from Arizona State University, 2022. He received a bachelor’s degree in Electrical and Electronics Engineering and a Master’s in Power Systems from India in 2011 and 2014, respectively. He has worked with Tata Consultancy Services as an Assistant Systems Engineer from 2011–2012 in

Conference Session

Educational Research and Methods Division (ERM) Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mohammad Faraz Sajawal, University of Oklahoma; Javeed Kittur, University of Oklahoma

Tagged Divisions

Educational Research and Methods Division (ERM)

Education, 2024Factors Influencing Undergraduate Engineering Students’ Perceptions on the Use of ChatGPTAbstractThe language model known as Chat Generative Pre-Trained Transformer (ChatGPT) wasdeveloped by Open Artificial Intelligence engineers. It's a kind of AI system that can produce textresponses to a variety of questions and prompts that seem human. ChatGPT provides a number ofbenefits, such as round-the-clock assistance, prompt question answering, research-relatedinformation discovery, coding program writing, etc. Notwithstanding these benefits, ChatGPT'slimited contextual knowledge of a given subject may result in inaccurate or irrelevant responses.Additionally, the feedback may be unfair or erroneous due to bias

Conference Session

Educational Research and Methods Division (ERM) Technical Session 22

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mohammad Faraz Sajawal, University of Oklahoma; Javeed Kittur, University of Oklahoma

Tagged Divisions

Educational Research and Methods Division (ERM)

. ©American Society for Engineering Education, 2024 Design and Development of Survey Instrument to Measure Engineering Students’ Perspectives on the Use of ChatGPTAbstractChat Generative Pre-Trained Transformer (ChatGPT) is a language model created by engineersworking in Open Artificial Intelligence. It is a type of artificial intelligence system that generateshuman-like text responses to a wide range of prompts and questions. ChatGPT offers severaladvantages including 24/7 support, quick response to questions, finding research-relatedinformation, writing a coding program, etc. Despite these advantages, ChatGPT has limitedcontextual understanding of a certain topic, which can lead to incorrect/irrelevant responses. It canalso be

Conference Session

Educational Research and Methods Division (ERM) Technical Session 19

Collection

2024 ASEE Annual Conference & Exposition

Authors

Trini Balart, Pontificia Universidad Católica de Chile; Jorge Baier, Pontificia Universidad Católica de Chile; Martín Eduardo Castillo, Pontificia Universidad Católica de Chile

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Educational Research and Methods Division (ERM)

Paper ID #43635WIP: Traditional Engineering Assessments Challenged by ChatGPT: An Evaluationof its Performance on a Fundamental Competencies ExamTrini Balart, Pontificia Universidad Cat´olica de Chile Trinidad Balart is a PhD student at Texas A&M University. She completed her Bachelors of Science in Computer Science engineering from Pontifical Catholic University of Chile. She is currently pursuing her PhD in Multidisciplinary Engineering with a focus in engineering education and the impact of AI on education. Her main research interests include Improving engineering students’ learning, innovative ways of

Conference Session

Educational Research and Methods Division (ERM) Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Campbell R. Bego, University of Louisville; Tessa Withorn, University of Louisville; Judith Danovitch, University of Louisville; Angela Thompson P.E., University of Louisville; Elisabeth Thomas, University of Louisville; Gabriel Ethan Gatsos, University of Louisville; Alvin Tran, University of Louisville

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Educational Research and Methods Division (ERM)

of Engineering Fundamentals at the University of Louisville. Dr. Thompson received her PhD in Mechanical Engineering from the University of Louisville. Her research interests are in biomechanics and engineering education, particularly related to first-year students.Elisabeth Thomas, University of LouisvilleGabriel Ethan Gatsos, University of LouisvilleAlvin Tran, University of Louisville ©American Society for Engineering Education, 2024 Working towards GenAI literacy: Assessing first-year engineering students’ attitudes towards, trust in, and ethical opinions of ChatGPT AbstractGenerative artificial intelligence (GenAI) can be used by engineering

Conference Session

Educational Research and Methods Division (ERM) Technical Session 22

Collection

2024 ASEE Annual Conference & Exposition

Authors

Xiaorong Zhang, San Francisco State University; Stephanie Claussen, San Francisco State University; Fatemeh Khalkhal, San Francisco State University; Yiyi Wang, San Francisco State University

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Educational Research and Methods Division (ERM)

Engineers (SWE) at SFSU. ©American Society for Engineering Education, 2024 Evaluating ChatGPT's Efficacy in Qualitative Analysis of Engineering Education ResearchAbstractThis study explores the potential of ChatGPT, a leading-edge language model-based chatbot, incrafting analytic research memos (ARMs) from student interview transcripts for use inqualitative data analysis. With a rising interest in harnessing artificial intelligence (AI) forqualitative research, our study aims to explore ChatGPT's capability to streamline and enhancethis process.The research is part of a mixed-methods project examining the relationships between engineeringstudents' team experiences, team disagreements, and

Conference Session

Educational Research and Methods Division (ERM) Technical Session 22

Collection

2024 ASEE Annual Conference & Exposition

Authors

Dhruv Gambhir, Nanyang Technological University; Yifan Xie, University College London; Ibrahim H. Yeter, Nanyang Technological University; Junaid Qadir, Qatar University; Andy Khong, Nanyang Technological University

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Educational Research and Methods Division (ERM)

learning analytics, acoustic signal processing, and recommendation systems. ©American Society for Engineering Education, 2024 Perceptions of Engineering College Instructors and Their Students Towards Generative Artificial Intelligence (GenAI) Tools: A Preliminary Qualitative AnalysisAbstractGenAI tools, such as ChatGPT, have gained significant traction in engineering colleges and arerevolutionizing how students approach each assignment and project. However, integrating theminto the education system introduces challenges to the core assessment criteria and the traditionalgrading system that has been used in these institutions for decades. To achieve a betterunderstanding of the

Conference Session

Educational Research and Methods Division (ERM) Technical Session 19

Collection

2024 ASEE Annual Conference & Exposition

Authors

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

Tagged Divisions

Educational Research and Methods Division (ERM)

Consortium and teaches application of emerging technologies. Over the past 35 years ©American Society for Engineering Education, 2024ASEE 2024 Educational Research and Methods (ERM) Division Using Generative AI for A Graduate Level Capstone Course Design -A Case Study Abstract This WIP paper aims at exploring the pros and cons of using the newly released,advanced generative artificial intelligence (AI) tool, ChatGPT, to design the curriculum for aCapstone course, which is completed towards the end of the Master of Engineering TechnicalManagement (METM), a 21-month online graduate program for working professionals in theengineering

Conference Session

Educational Research and Methods Division (ERM) Technical Session 19

Collection

2024 ASEE Annual Conference & Exposition

Authors

Hudson James Harris, University of Oklahoma; Javeed Kittur, University of Oklahoma

Tagged Divisions

Educational Research and Methods Division (ERM)

: Articles pertaining exclusively to the teaching of deep learning algorithms(III) The articles that made it to the final phase were reviewed in detail. (IV) This information wasconsolidated, synthesized, and examined to find the emergent themes.Keywords: ChatGPT, engineering education, GenAI, large language models, undergraduateengineeringIntroductionThe dawn of the Fourth Industrial Revolution heralds an unprecedented era of technologicalconvergence, where the integration of digital, physical, and biological systems becomes a definingcharacteristic of societal and economic transformations. Artificial Intelligence (AI), especiallygenerative AI, stands at the vanguard of this revolution, driving innovations that blur the traditionalboundaries across

Conference Session

Educational Research and Methods Division (ERM) Technical Session 19

Collection

2024 ASEE Annual Conference & Exposition

Authors

Trini Balart, Texas A&M University; Jorge Baier, Pontificia Universidad Católica de Chile; Catalina Cortazar, Pontificia Universidad Católica de Chile; Kristi J. Shryock, Texas A&M University

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Educational Research and Methods Division (ERM)

it and discussing whether working on this problem could induce them to Flow. Thedesign of this activity was based on the relationships between identification of skills (e.g.,[10]) and flow state (e.g, [11]) in the development of a sense of purpose in life. Finally there was an interaction with ChatGPT, where students had to use it tobrainstorm how they could apply their skills to each of the three global challenges listed.They included screenshots of these interactions in their presentation, and critically analyzedthe answers given by ChatGPT, expressing their agreement or disagreement and suggestingimprovements based on their criteria, preferences and common sense.Purpose in Life - Short Form (PIL - SF) Questionnaire This

Conference Session

Educational Research and Methods Division (ERM) Technical Session 24

Collection

2024 ASEE Annual Conference & Exposition

Authors

Bradley J. Sottile, The Pennsylvania State University

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Educational Research and Methods Division (ERM)

), the third ethics scenariopresented respondents with a scenario featuring the issue of utilizing artificial intelligence. Thequantitative portion of the third scenario prompt was: Please consider the following scenario when answering questions on this screen: A major writing assignment is coming up for an engineering student’s capstone design course during a very busy part of the semester. There are a few major sections of the paper that require mostly formulaic responses. A student in the course decides to use ChatGPT, an artificial intelligence chatbot, to write those sections of the paper for them. [Question 06. Likert scale, responses choices: very unethical, somewhat unethical, neither ethical or unethical, somewhat ethical, very ethical

Conference Session

Educational Research and Methods Division (ERM) Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Xiuhao Ding, University of Illinois at Urbana - Champaign; Meghana Gopannagari, University of Illinois at Urbana - Champaign; Kang Sun, University of Illinois at Urbana - Champaign; Alan Tao, University of Illinois at Urbana - Champaign; Delu Louis Zhao; Sujit Varadhan, University of Illinois at Urbana - Champaign; Bobbi Lee Battleson Hardy, University of Illinois at Urbana - Champaign; David Dalpiaz, University of Illinois at Urbana - Champaign; Chrysafis Vogiatzis, University of Illinois at Urbana - Champaign; Lawrence Angrave, University of Illinois at Urbana - Champaign; Hongye Liu, University of Illinois at Urbana - Champaign

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods Division (ERM)

gold standard to evaluateautomated text analytic approaches. Raw text from open-ended questions was converted intonumerical vectors using text vectorization and word embeddings and an unsupervised analysisusing document clustering and topic modeling was performed using LDA and BERT methods. Inaddition to conventional machine learning models, multiple pre-trained open-sourced local LLMswere evaluated (BART and LLaMA) for summarization. The remote online ChatGPTclosed-model services by OpenAI (ChatGPT-3.5 and ChatGPT-4) were excluded due to subjectdata privacy concerns. By comparing the accuracy, recall, and depth of thematic insights derived,we evaluated how effectively the method based on each model categorized and summarizedstudents

Conference Session

Educational Research and Methods Division (ERM) Technical Session 17

Collection

2024 ASEE Annual Conference & Exposition

Authors

Xavier Rene Plourde, University of California, Berkeley; Garrett Ethan Katz, Syracuse University

Tagged Divisions

Educational Research and Methods Division (ERM)

cross-site copy-paste from 3rd-party sources (e.g., ChatGPT), andparticipant authentication using a Google account to keep keylogs secure. Experimenters canconfigure and deploy their own instance of the application following the documentation in thecode repository. Configuration uses a simple, human-readable, text-based format to specifyproblem descriptions, initial code stubs, test case data, survey questions, and other parameters.We validated the tool with a pilot study approved by our institutional review board whichinvolved 17 problems of increasing difficulty and duration, for a total of 105 minutes. Forexample, the final problem was to determine the winner in a tic-tac-toe board (complete problemdetails are available in our code

Conference Session

Educational Research and Methods Division (ERM) Technical Session 26

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sushant Makarand Padhye, University of Cincinnati; David Reeping, University of Cincinnati; Nahal Rashedi , University of Cincinnati

Tagged Divisions

Educational Research and Methods Division (ERM)

reasonably accuratecategorizations. These minor errors were subsequently corrected by hand. Since there were overone thousand unique names and a smaller window width was necessary to control unintendederrors, there needed to be a workflow in the form of a loop, as shown in Figure 5.Figure 5. Workflow for ChatGPT API UsageBecause of the large scale of the dataset and the number of course names, we found it better toperform the categorization in two steps. First, we used the API to sort all the course names intogeneral categories like the sciences (math, physics, chemistry, and biology) and engineering(general engineering, mechanical, civil, electrical, and chemical engineering). We then usedparticular categories to sort them into specific types of

Conference Session

Educational Research and Methods Division (ERM) Technical Session 18

Collection

2024 ASEE Annual Conference & Exposition

Authors

Navarun Gupta, University of Bridgeport; Junling Hu, University of Bridgeport; Ioana A. Badara, Post University; Buket D. Barkana, The University of Akron; Deana A. DiLuggo, University of Bridgeport

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods Division (ERM)

they can right away see being applied through concepts ofsimple Calculus and Python programming.Deep Convolution-based networks with the Triplet loss were quite successful (e.g., FaceNet) inface recognition, resulting in greater than 99% accuracy on benchmarks such as LFW. With therecent success of transformer-based Natural Language Processing architectures (e.g., ChatGPT),transformers have been attempted in Computer Vision applications. They have shown considerablesuccess with better computational efficiency than CNN-based architectures. In this project, wecompared the FaceNet and transformer-based architecture for face recognition. We also providedan insightful understanding of the face recognition process, its limitations, and future

Conference Session

Educational Research and Methods Division (ERM) Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Arianna Gabriella Tobias, University of Oklahoma; Javeed Kittur, University of Oklahoma; Khondhaker Al Momin, University of Oklahoma; Tahrima Rouf, University of Oklahoma; Arif Mohaimin Sadri, University of Oklahoma

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Educational Research and Methods Division (ERM)

importance of socialmedia in engineering education, highlighting its potential as a versatile tool for enhancing teachingand learning processes. The insights obtained lay the groundwork for further exploration anddevelopment in this rapidly evolving field.ReferencesThe articles included in the final review stage are marked with an asterisk (*). [1] M. Kaplan and M. Haenlein, “Users of the world, unite! The challenges and opportunities of Social Media,” Business Horizons, vol. 53, no. 1, pp. 59–68, Jan. 2010, doi: 10.1016/j.bushor.2009.09.003. [2] J. Qadir, “Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education,” in 2023 IEEE Global Engineering Education Conference

Conference Session

Educational Research and Methods Division (ERM) Technical Session 18

Collection

2024 ASEE Annual Conference & Exposition

Authors

Marino Nader, University of Central Florida; Michelle Taub, University of Central Florida; Sierra Outerbridge, University of Central Florida; Harrison N. Oonge, University of Central Florida; Hyoung Jin Cho, University of Central Florida

Tagged Divisions

Educational Research and Methods Division (ERM)