Displaying all 6 results
- 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
- 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
- Conference Session
- ELOS Technical Session 3: Advancing Engineering Competencies: From Labs to Writing
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Dave Kim, Washington State University-Vancouver; Charles Riley P.E., Oregon Institute of Technology; Sean St. Clair P.E., Oregon Institute of Technology; Olusola Adesope, Washington State University
- Tagged Topics
- Diversity
- Tagged Divisions
- Experimentation and Laboratory-Oriented Studies Division (DELOS)
community of practice focusing on engineering lab writing education. Thispaper presents the content, delivery, and results of the professional development workshop onengineering lab writing.2. Workshop Content and DeliveryThe workshop was designed for the participants to conduct the following in a small groupsetting: 1) develop engineering lab report assignments; 2) improve engineering lab reportassessment; 3) guide students in navigating writing with generative AI (ChatGPT-4); and 4) trainlab teaching assistants or lab report graders. Participants accessed the guides (available atengineeringlabwriting.org) to design and develop sample labs, discuss issues related to labwriting and how to deliver lab writing expectations, and provide feedback to
- Conference Session
- ELOS Technical Session 3: Advancing Engineering Competencies: From Labs to Writing
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Gautom Kumar Das, University of Maryland Baltimore County
- Tagged Divisions
- Experimentation and Laboratory-Oriented Studies Division (DELOS)
allowed to use generative AI tools (e.g., ChatGPT) during anystage of the writing process or they could choose not to use them. If AI assistance was used,students were asked to include the following information in the Appendix of their reports: theprompt(s) used, and other details on how the AI-assisted content was incorporated or revised.This information was collected to ensure the accuracy of the report content and the authenticityof references.2.2 Instructor’s AssessmentA total of 48 draft reports (i.e., first submission) were evaluated for this study. Reports in whichstudents self-reported the Checklist were analyzed further for this study.3. Results and DiscussionAs mentioned earlier, the primary goal of this study was to evaluate the
- Conference Session
- ELOS Technical Session 1: Integrating AI, VR, and MR in Engineering Lab Experiences
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Jessica Ohanian Perez, California State Polytechnic University, Pomona; Yitong Zhao, California State Polytechnic University, Pomona; Juliana Lynn Fuqua, Cal Poly Pomona
- Tagged Divisions
- Experimentation and Laboratory-Oriented Studies Division (DELOS)
question and wanted a simple answer. When that happened, they wanted to be able to turn off the AI temporarily or permanently. They coped by totally muting it. A better solution would be for the AI to have a feature like Alexa or Siri in which users can easily say “hey, stop”. That’s essential, according to students. • How the AI was responding to the surrounding speech. AI occasionally responded to not direct questions so if the student was talking through the lab as they completed the assignment, the AI would respond to a question that was not asked. • Students felt that the AI was trained on ChatGPT. Students were asking history questions to it, and it was answering with somewhat relevant
- Conference Session
- Experimentation and Laboratory-Oriented Studies Division (ELOS) Technical Session 3: Best of ELOS
- Collection
- 2023 ASEE Annual Conference & Exposition
- Authors
- Rachel C. Childers, The Ohio State University; Sunny Kwok, The Ohio State University
- Tagged Topics
- Diversity
- Tagged Divisions
- Experimentation and Laboratory-Oriented Studies Division (DELOS)
to the question “Was there anything that you would take away fromthis experience and apply to future projects or group work? If so what?” in the survey wasanalyzed and validated in two ways. Salient themes were identified by the authors and thefrequency of those themes were tabulated to count the number of occurrences specific featureswere identified from students. Responses were coded into the following 8 themes:Communication, assigned roles, motivation, lab skills/course content, collaboration/teamwork,leadership, enjoyment, and delegation/group organization. In addition, the responses were inputinto an artificial intelligence natural language processing tool (ChatGPT, OpenAI) to identifythemes from responses in an unbiased manner. This