Displaying all 6 results
- 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
- 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
- ERM Technical Session: Developing Engineering Competencies I
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Olivia Ryan, Virginia Polytechnic Institute and State University; Katherine Drinkwater, Virginia Polytechnic Institute and State University; Susan Sajadi, Virginia Polytechnic Institute and State University; Mark Vincent Huerta, Virginia Polytechnic Institute and State University
- Tagged Divisions
- Educational Research and Methods Division (ERM)
it is not taken personally. I think everyone made kind and helpful comments that reassures me and I appreciate that I am able to know what they think of me so I know how to improve. he feedback I received was helpful and not as intimidating to receive so I think next T time I won't be as scared to share how I feel about my teammates in terms of the work they put in because I like that ChatGPT is another layer and it is ultimately helpful to get this feedback. tudents also stated that they felt like they could provide negative feedback instead of focusingSon just positive feedback, “I enjoyed the AI-generated feedback reports because they allowed team members to be
- Conference Session
- ERM WIP V: Assessing & Developing Competencies in Engineering Education
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Jason J Saleem, University of Louisville; Edward James Isoghie, University of Louisville; Jeffrey Lloyd Hieb, University of Louisville; Thomas Tretter, University of Louisville
- Tagged Divisions
- Educational Research and Methods Division (ERM)
summaries inaddition to standard quantitative anthropometric data tables to support their work on a designproblem focused on workstation design. We used generative AI (i.e., ChatGPT) to produce 10fictitious interview transcripts as a starting point, adjusting the prompts as needed to constructrealistic looking interviews. After editing the transcripts to introduce more variability anddistinction across the 10 interview transcripts, intentional “design seeds” were planted within theinterview texts for students to potentially discover during their qualitative analysis. Our goal wasto have recurrent design seeds (e.g. comments about the absence of adequate lumbar support forthe desk chair), appearing across multiple interview transcripts in a variety
- Conference Session
- ERM Technical Session: Faculty Influences on Student Support
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Gadhaun Aslam, University of Florida; Yuxuan Wang, University of Florida; Idalis Villanueva Alarcón, University of Florida; Edwin Marte, University of Florida
- Tagged Divisions
- Educational Research and Methods Division (ERM)
/neutral.For this categorization purpose, the researchers manually classified the emotions into biggercategories. ChatGPT-4 was used as a secondary resource to categorize different emotions under abigger umbrella of emotion. For example, in model 4, the emotions like anger, remorse,annoyance, disapproval, and disgust were all categorized as ‘Anger’ to be able to compare it withresults from other models. This categorization is shown in Appendix A. For this study, students(474, 81.4%) include all undergraduate and graduate students while professors (84, 14.4%)include full professors, associate professors, assistant professors, adjunct professors, academicadvisors, and lecturers. Out of the remaining 24 participants, 2 had already graduated and theothers
- Conference Session
- ERM Technical Session: Developing Engineering Competencies III
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Katherine Drinkwater, Virginia Polytechnic Institute and State University; Olivia Ryan, Virginia Polytechnic Institute and State University; Susan Sajadi, Virginia Polytechnic Institute and State University; Mark Vincent Huerta, Virginia Polytechnic Institute and State University
- Tagged Divisions
- Educational Research and Methods Division (ERM)
pose. Subcode Representative Quote1. Perceptions of how AI-generated Getting the feedback from ChatGPT will likely help me give better feedback in the future since I canfeedback helps with providing and/or use it as a guide as how to phrase my feedback to others. (151)receiving feedback2. Managing emotions in engaging in I know I could be stubborn and feel that I am right, but that is simply not being an engineer. I need tofeedback processes know that collaboration is key to success in this class and all facets of engineering…(64