Impact of Artificial Intelligence on Students in Higher Education,” Int J Educ Method, 2024, doi: 10.12973/ijem.10.2.997.[12] V. V. Singh, “Assessment of Artificial Intelligence-Based Digital Learning Systems in Higher Education Amid the Pandemic Using Analytic Hierarchy,” 2024, doi: 10.21203/rs.3.rs- 3828524/v1.[13] B. Anuyahong, “Analyzing the Impact of Artificial Intelligence in Personalized Learning and Adaptive Assessment in Higher Education,” International Journal of Research and Scientific Innovation, 2023, doi: 10.51244/ijrsi.2023.10412.[14] A. Rai, “Explainable AI: from black box to glass box,” J Acad Mark Sci, vol. 48, no. 1, pp. 137– 141, Jan. 2020, doi: 10.1007/s11747-019-00710-5.[15
Paper ID #45928Work-In-Progress: Optimizing Student Mental Health Support through Biomarker-DriveMachine Learning and Large Language ModelsDr. Yuexin Liu, Texas A&M University AI/DRL in low Reynolds number hydrodynamics, Stress Management and Well-beingDr. Behbood ”Ben” Ben Zoghi P.E., Southern Methodist University Ben Zoghi is the Associate Dean, Advanced Studies and Industrial Partnerships, Executive Director, Hart Center for Engineering Leadership Bobby B. Lyle Endowed Professor of Engineering Innovation Professor of Electrical and Computer Engineering and a faculty Fellow, with Los Alamos National Laboratory. Before
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, M., Swindell, G. S., Kokkinos, N. C., & Willhite, G. P. The Impact of the Energy Transition on Petroleum Engineering Departments: The Faculty Perspective. Presented at the SPE 2023 Annual Technical Conference and Exhibition. https://doi.org/10.2118/215086-MS[6] Feder, J. As Industry Changes, So Does Petroleum Engineering Education, Journal of Petroleum Technology, 71(12), 44–48, 2019. https://doi.org/10.2118/1219-0044-JPT[7] Mosobalaje, O., Aku, M., Egbe, T., Ibeh, C., Aderibigbe, B., Ogbonna, J., and Olayemi,M. A Framework for Introducing Data Analytics and Machine Learning to Petroleum EngineeringUndergraduates. Presented at the 2024 SPE Nigerian Annual International Conference andExhibition. https://doi.org/10.2118
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Health Help‐Seeking Instrument (UE‐MH‐HSI): Development and validity evidence.”Journal of Engineering Education, vol. 113, no. 4, pp. 1198-1225, 2024.[12] L. Ricci, J-B. Lanfranchi, F. Lemetayer, C. Rotonda, F. Guillemin, J. Coste, and E. Spitz,“Qualitative Methods Used to Generate Questionnaire Items: A Systematic Review,” QualitativeHealth Research, vol. 29, no. 1, pp.149-156, 2019. doi:10.1177/1049732318783186[13] J.H. Hammer, C.J. Wright, M.E. Miller, and Wilson, S. A., “The Undergraduate EngineeringMental Health Help-Seeking Instrument (UE-MH-HSI): Development and validity evidence,”Journal of Engineering Education, vol. 113, no. 4, pp. 1-28, 2024.https://dx.doi.org/10.1002/jee.20615[14] W.C. Lee, J.L. Hall, A. Godwin, D.B. Knight, and D
quantum computing and explore collaborations with industry partners to further enhancelearning outcomes.Reference[1] Y. You, A. Buluc, J. Demmel. “Scaling Deep Learning on GPU and Knights Landingclusters”. 9 Aug 2017. [Online]. Distributed, Parallel, and Cluster Computing.https://arxiv.org/abs/1708.02983[2] E. A. Huerta, A. Khan, E. Davis, etc. “Convergence of Artificial Intelligence and HighPerformance Computing on NSF-supported Cyberinfrastructure”. Journal of Big Data. Vol 7,Article number: 88 (2020). https://arxiv.org/abs/2003.08394[3] Y. Zhang. "Scalable Parallel Machine Learning on High Performance Computing Systems:Clustering and Reinforcement Learning". Purdue University, 2022.[4] B. Oliveri. “Why Accelerated Data Processing Is Crucial
) Beginning in a large group, students began Whole-Class Video Appendix B Explore students categorized discussing the attributes of images of wind Recording - Imagine & information and formed new turbines displayed on the screen, discussing Explore understandings based on the geometry the recognized. Next, students provided information participated in the "Mini Windmill" lesson from the elementary KidWind Curriculum. Finally, students participated in a
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, realization, relief, remorse, sadness, and surprise.Results & DiscussionThis section presents the findings for all the research questions. Section A refers to the firstresearch question about the emotions expressed by engineering students and professors to theemotion questions in UPHEME. Section B discusses the second research question on how theemotions of participants vary in terms of demographics (primary discipline, self-identified agerange, self-identified gender identity, self-identified race/ethnicity) and Section C shows thefindings for the third research question and gives a comparison of four pre-trained NLP modelsin identifying participants' emotions expressed in UPHEME.A. Emotions expressed by Engineering Students and Professors in
Paper ID #47648Navigating the AI Ethics Frontier: A Cross-national Comparison of AI PolicyDocuments for Developing Responsible AI WorkforceEmad Ali, Virginia Polytechnic Institute and State University Emad Ali is a Ph.D. student in the Department of Engineering Education at Virginia Tech. His research focuses on the integration of artificial intelligence in engineering education with particular emphasis on its ethical implications. He holds a master’s degree in Electrical Engineering with specialization in Embedded Systems, from the Information Technology University (ITU Lahore), as well as a Bachelor’s degree in Avionics
transformation (r =.75). Again, these patterns suggest those elements of the frameworkviewed as important were also viewed as transformative for CEE students.Figure 3. Absolute pattern match across three rating questions. Refer to Appendix B for each ofthe statement clusters.Go-ZonesThe Go-Zone is a scatterplot picture that uses the same rating used in pattern matches to evaluatethe importance, feasibility, and transformative potential of each statement. The scatter plot isdivided into quadrants based on the average for each contrasted rating. The quadrants are thencolor-coded to easily identify the distribution of points based on two rating values. The greenarea contains ideas that are above average for both the contrasted ratings. The yellow and
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Paper ID #46367How to Cultivate Digital Engineering Management Talents: A Case on the”Digital Intelligence Innovation and Management” Engineering Doctoral ProgramYuxin Xue, Zhejiang University Yuxin Xue is a Ph.D student in the School of public affairs, Zhejiang University. Her research interests include Science and Technology Management.Prof. Tuoyu Li, Zhejiang University Tuoyu Li is a associate professor of the Institute of China’s Science, Technology and Education Policy, Zhejiang University. His research interests include Engineering Education, Science Technology and Education Policy.Min Ye, Zhejiang University Min
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” “AI, drones, AR, VR, 3D Artificial Intelligence modeling, 3D printing”In addition to the course surveys, the final project poster was analyzed for evidence of studentlearning. The posters were evaluated using a rubric developed to assess students' ability todescribe natural hazards, develop a reasonable solution for each hazard, use evidence effectivelyto support their selected solution, and employ reasonable sustainability practices. This rubric isincluded in Appendix B. This rubric (and the assessment herein) was separate from rubrics andgrades used to evaluate students taking the course. The researchers determined that achieving 8points demonstrates proficiency in
Paper ID #46541WIP - An Exploratory Approach to Introducing Generative AI into a Large-scaleEngineering-focused General Education CourseDr. Brian P Kirkmeyer, Miami University Brian Kirkmeyer is the Karen Buchwald Wright Senior Assistant Dean for Student Success and Instructor in the College of Engineering and Computing at Miami University in Oxford, Ohio. His background includes BS, MS and PhD degrees in Materials Science and Engineering, and has focused his teaching on global inquiry and engineering design within heavy metal music and culture. He is active in broadening access in engineering and computing, and in particular
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Paper ID #48171Work in Progress: Enhancing Engineering Education through Course Alignment:A Structured Approach to Integrating Learning Outcomes, Content, andAssessmentDr. Cyrus Habibi, University of Wisconsin - Platteville Dr. Cyrus Habibi is an accomplished electrical engineer and associate professor at the University of Wisconsin-Platteville with over 13 years of teaching experience and a decade of industry expertise. Holding a Ph.D. in Electrical Engineering, he is a registered professional engineer in Minnesota and has taught a variety of courses, including Power Electronics, Signals and Systems, and Control Systems
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architectures, it would not be a problem. • Some of the Verilog HDL code generated by the tool was either not synthesizable (as this was not part of the prompt) or used advanced Verilog syntax that we do not teach to students. In the case of non-synthesizable Verilog (which means the design, when processed by the tool flow, can be programmed to an FPGA) we modified the prompt with “synthesizable to an FPGA”. We did not penalize the chatbot for using more complex Verilog. Course LLM Base Letter Grade Assumed Letter Grade ECE 287 B- A- Table 4: LLM grade performance in EE and CpE curriculum at Miami
attend. In addition tothe lecture time, the Math Launch Center was staffed with undergraduate learning assistants.Weekly knowledge checks and testing took place in the CECS Evaluation and Proficiency Center(EPC).Starting in Spring 2023, the course was assigned three credit hours with a designated meetingpattern available on a student’s schedule. Study hours were required in the Center however, asthe majority of students were returning from fall, they did not subscribe to the change and manyfailed to utilize the assistance. By Summer term B 2023, the current structure was established. Inaddition to prior changes, attendance at lecture and in open lab – both now housed in a computerlab called Mathematics Assistance Learning Lab (MALL) – were
biomedical engineering education,ultimately enhancing the learning experience and better preparing students for professionalpractice. 1.2. Definition of “Teaching Kit” This review defines “teaching kit” as a collection of materials, tools or resources that servesthe purpose of assisting educators in delivering an instructional experience or that assists studentsin understanding and participating in an instructional experience. 1.3. Definition of “Bioengineering” This review defines “bioengineering” a) as any engineering related activity with the goalof improving human health, or b) an activity that would give students in an accredited biomedicalengineering program the skills that would be necessary to engage in a). 1.4. Methodology