increasinglyinterested in addressing global challenges. In biomedical engineering particularly, students oftenenter the discipline because they are interested in problem-solving at the intersection ofengineering and public health. Global health problems present some of the most pressing andcomplex issues of our time, requiring innovative and sustainable solutions that account for diversecultural, social, and environmental contexts. Despite this enthusiasm, many engineering studentslack a structured introduction to the ethical and practical challenges inherent in global health designprojects. To create meaningful and sustainable change, engineers must approach these challengesusing frameworks that emphasize ethical responsibility, cultural humility, and long
Inclusive Course Titles Intro to Environmental Engineering Data Science for Env Engineers Prompt Engineering and Human-AI Collaboration AI Ethics & Environmental Policy Capstone Design Project Data Science for Env Engineers Data Literacy and Computational Thinking Intro to Programming
system?Within the healthcare sector, nursing is the single largest profession with nearly five million(5M) Registered Nurses (RNs) in the US. And yet, nursing is NOT recognized as a STEMprofession by many federal agencies. Why not [3] [4]? Nursing is clearly based in science (e.g.chemistry is necessary for understanding pharmacology). Nursing clearly uses math (e.g.appropriate dosage of medicines requires calculations). And like engineering, nursing is aprofession that cares for the public. In fact, the code of ethics of nursing specifically calls for thecare of every patient – whether an individual, family, group, population, or community [5].Nursing is unique among the healthcare professions in that its code of ethics explicitly mentionsBOTH
critical thinking and effort. Finally, some responses, about 10%, also reflectedcuriosity and apprehension about AI's future impact and while some saw it as a "fast-growingand multifaceted tool" with "limitless potential," others expressed concerns about its rapiddevelopment, potential misuse, and ethical implications. Notably, a few students mentioned fearsabout AI replacing human jobs or concentrating power and wealth.Question 2: Which AI tools or websites do you use most often?ChatGPT and Grammarly are the most commonly used AI tools among participants, with 75%and 65% of respondents, respectively, reporting regular usage. These tools were often mentionedtogether, with 57% of participants highlighting their combined use for academic and
robots and the future of work.The trainees can select from a menu of courses across a range of disciplines that broaden theirtechnical knowledge. The interdisciplinary coursework broadens the theoretical and 6methodological perspective of the trainees. Additionally, the program requires each trainee toinclude a chapter on the broader impacts of research into their dissertations and theses. Theprogram’s required applied ethics course offers a collective intellectual space in which thetrainees explore non-technical consequences of their technical research. This paper presents a discussion of convergence research through the lens of the
, such as information covered by the HealthInsurance Portability and Accountability Act (HIPAA) and the Family Educational Rights andPrivacy Act (FERPA), as well as facilitating the investment in innovation by protecting theconfidentiality of intellectual property under development. As such, they can be an importantpart of both engineering education and engineering practice. According to the National Societyof Professional Engineers (NSPE) code of ethics III.4 “Engineers shall not disclose, withoutconsent, confidential information concerning the business affairs or technical processes of anypresent or former client or employer, or public body on which they serve” [2]. Keeping tradesecrets confidential enables trust and facilitates communication
onlineeducation during the COVID-19 epidemic, emphasizing the difficulties in preserving the integrity ofassessments, the quick changes in educational methods, and the growing dependence on technology.Their results support the necessity of creative approaches to academic integrity in online settings.Online learning concerns: Toprak et al. [3] highlight that enforcing academic integrity in onlinelearning environments is more challenging due to ethical concerns investigate differences in howstudents and teachers view privacy and the application of rules. According to their research, 78% ofstudents prefer moderate punishments for misbehavior, but 52% of teachers support harsherpunishments. Despite these disagreements, both sides agreed that it is critical
(Ex. study 3.33 3.14 3.07 3.32 3.29 3.27 3.33 3.47 3.29 3.38 3.30 abroad or student organizations) Campus resource connections 3.32 3.24 3.20 3.41 3.09 3.42 3.31 3.69 3.24 3.59 3.19 Graduate school 3.27 3.13 3.27 3.43 2.85 3.34 3.25 3.26 3.27 3.28 3.24 Ethics and social responsibility 3.12 2.92 3.33 3.10 3.22 3.12 3.08 3.57 3.03 3.15 3.15 Personal wellness
. Overall, the students’ increasedidentification as scientists raised the stakes of instruction in experimental methods, laboratoryand publishing ethics, and technical writing. This increase in identification as a professionalscientist or engineer helps the students to gain authentic practice in these skills in a controlledenvironment and build their confidence for when these skills are needed in their future careers.The publicly available end product of the course, now published online as Physics in Progressissue 1, served as a motivating factor and now serves as a time capsule containing writingartifacts that students take pride in and can share in portfolios or as otherwise appropriate.IntroductionAt what point does one cease to be an
science, the production of scientific knowledge, and critical approaches to scientific inquiry. ©American Society for Engineering Education, 2025Paper ID: 48415Title: Toward a Critical Framework for AI Tool Selection and Adoption in AcademicResearch Contexts: Reflections from the Brown University Library’s Critical AILearning CommunityAbstract: Given the rapid proliferation of artificial intelligence (AI) tools in academicenvironments, critical questions about AI and its role in economizing the research processcontinue to emerge. While AI tools have the potential to enhance productivity and fostergreater collaborative inquiry dramatically, there remain overarching concerns about AI'simpact on information ethics
technical proficiency; the objective is the implementation of sustainabledesigned solutions [6], [7]. However, community participation is important. The success of theproject is determined by the community’s engagement with the project and availability. There isthe need for a deep understanding of the community, its social and cultural contexts to developtrust and proper communication to achieve the desired collaboration and partnership.Complementary to the community understanding, students and faculty advisors develop aframework with high ethical standards and professionalism, which may not be applied in everycommunity [6], [8], [9]. To provide context, and a sense of purpose and clarity, students andfaculty advisors have to understand economic
Paper ID #47277Harnessing the Power of GenAI: A New Era for Data Science Education forCivil and Environmental EngineeringMatthew Yukio Takara, Carnegie Mellon University Matthew Yukio Takara is a Ph.D. student in the civil and environmental engineering department at Carnegie Mellon University. He holds a B.S. in civil engineering with a minor in data science from the University of California, Berkeley and a M.S. in civil engineering from Carnegie Mellon University. In addition to his interest in engineering education research, his thesis research focuses on the sustainable and ethical use of AI and sensing technologies in
transformation and artificial intelligence 3. Enhancing Undergraduate Education and 5. Enabling regional initiatives in entrepreneurship Curriculum Improvement and innovation 4. Ethics and Society in Engineering Education 6. Entrepreneurship and innovation to overcome the 5. Government, Industry, and University economic and financial crisis 6. Management of Engineering Education 7. Equal rights, opportunities and spaces for women in 7. Online and Remote Laboratories Latin America and the Caribbean in the 8. Recruitment and Retention in Engineering professional field 9. Technology for
experience, perceived reliability of AI-generated content, and the extentto which AI aligns with their learning goals [9-12]. Moreover, concerns about the accuracy of AIoutputs and ethical considerations, such as potential biases in AI algorithms, have been raised byboth students and educators [13-16].Studies involving generative AI tools in STEM education suggest a mixed response: studentsappreciate the efficiency and accessibility of AI tools but remain cautious about over-reliance andthe lack of critical evaluation skills when using AI-generated solutions. This highlights the needfor educational interventions that not only incorporate AI tools but also teach students how tocritically evaluate and effectively integrate these technologies into
and social awareness, preparing them to design solutions with broader societal and ethical implications. Pedagogical Primarily lecture-based with limited Combines immersive learning, problem-based Approach experiential learning or learning (PBL), and interdisciplinary projects. interdisciplinary engagement. Students engage in iterative co-design and reflective exercises, bridging the gap between
-onecoaches or as project mentors. The learning coaches serve as a bridge between faculty andstudents, offering practical advice, facilitating teamwork, and encouraging intrinsic motivation. Aone-on-one coach is a peer graduate student who provides academic, professional, and personalmentorship to undergraduate students. Project mentors offer similar guidance in the context ofvertically integrated research teams, guiding students through practical aspects of conductingengineering projects. Much of the research on the topic of graduate student mentors focuses onthe role of generic mentoring, coaching techniques, or ethical considerations. There is a need toevaluate the specific impacts on collaborative, academic, and professional culture that
Paper ID #47128Expanding the Engineering Workforce: An Exploratory Study of a Mid-CareerTransition from a Non-Engineering BackgroundBailey Kathryn McOwen, Virginia Polytechnic Institute and State University Bailey McOwen is a Ph.D. student in Engineering Education at Virginia Tech with an academic foundation in physics and industrial engineering. Her research focuses on workforce development, professional training for engineering practitioners, and engineering ethics, with an emphasis on how emerging technologies can enhance continued education. Through her research, service, and academic work, she aims to bridge
practice during the lecture time as well. After this set oflectures, students can complete Task 6 (Section 2.1.6).The last big topic is 3D design. In these lectures, students learn how to design custom parts in acomputer-aided design (CAD) suite. As with web design, the goal is not to make the studentsexperts in CAD, but rather to give them the skills to create functional prototypes for novelsituations. After these lectures, students can tackle Task 7 (Section 2.1.7).For the rest of the lectures, there are various topics. One lecture is used to demonstrate how toefficiently debug embedded systems with surface mount components. Another lecture is used todiscuss ethics in embedded systems [14, 15, 16, 17]. Finally, the last lecture brings an
engineering identity, diversity, equity, and inclusion, Asian American Studies, Critical Mixed Race Studies, engineering ethics, and pop culture.Dr. Qin Zhu, Virginia Polytechnic Institute and State University Dr. Zhu is an Associate Professor in the Department of Engineering Education at Virginia Tech, with additional affiliations in the Department of Science, Technology & Society, the Department of Philosophy, the Center for Human-Computer Interaction, and the Center for Future Workplaces and Practices. He serves as Associate Editor for Science and Engineering Ethics, Studies in Engineering Education, and Editor for International Perspectives at the Online Ethics Center for Engineering and Science. Additionally, Dr
what I bring to engineering 6 I better understood key concepts in this course 7 I felt prepared to do well in this course Course 8 I was able to refine my understanding of course concepts Understanding 9 I made connections across course concepts 10 I can now articulate main ideas of this course 11 I understood more about my own weaknesses as a student 12 I was able to improve my work Areas for Growth 13 I gained insights about my study habits 14 I thought about ethical concerns in engineering 15 I learned about
to real-world contexts[18]. In addition, Darr provides practical strategies for librarians to teach students how to useinformation ethically and avoid plagiarism. Drawing from real-life examples, digital resourcechallenges, and tested instructional materials, she emphasizes understanding authorship,publication, and research integrity through engaging lessons and exercises [19]. Interestingly, domestic students at both the undergraduate and graduate level reportedlower confidence levels and performed on average 6.3% higher than their agreement percentage.International students at both the undergraduate and graduate level reported higher confidencelevels and performed on average 11.6% lower than their agreement percentage, suggesting a
usefulness of thetool, specifically noting that they did not have the opportunity to simply copy and paste what anAI tool suggests. Instead, they had a chance to rethink and revise their writing through the KVIStool. In addition, the visualized graph appears to help students capture the overall focus of theirwriting, rather than losing sight of their main idea by concentrating too narrowly on a specificaspect.As AI technologies grow more advanced, concerns about over-reliance, ethical use, and misusehave become increasingly significant. Addressing issues such as authenticity, feedback quality,bias, and digital literacy is critical to harnessing the potential of generative AI in engineeringeducation and ensuring equitable learning opportunities. The
projects, by providing alternate viewpoints and that will increaseteam’s performance.5- As a new freshman Student, by asking many primitive questions from the instructor. As theresult instructor will be more prepared for the harder questions from other students.6- As a Simulator in which students can practice their project presentations.7- As a Flashcard for practicing and preparing for exam.8- For collecting Feedback regarding lectures or course.9- As a Student Advisor, by providing teaching plan, submitting course incomplete applicationform, registration, course progress, pre-requisite requirement, etc.Creating these nine options requires several best practices to ensure that they are effective,ethical, and user friendly. You can also use
,” Science Advances, Vol. 9, 2023. [Online] Available: https://doi.org/10.1126/sciadv.adh2458. 6. J. Simon, The Ultimate Resource, 1981. 7. Union of Concerned Scientists, “What is Climate Engineering,” Explainer [Blog] 6 November 2017. [Online] Available: https://www.ucs.org/resources/what-climate-engineering. 8. D.B. Oerther, “Is It Time to Decenter Humans in Our Discussion of Sustainable Development?” Environmental Engineering Science, Vol. 39, No. 11, 2022. [Online] Available: https://doi.org/10.1089/ees.2022.0239. 9. D.B. Oerther, “Environmental Health Professionals: Local Interprofessional Collaborations Require Global Thinking to Meet Shared Ethical Obligations,” J. Environ. Health, vol. 84, pp. 26-28
innovation, as well as ethical considerations, emphasizingthe need for targeted education that incorporates these advancements into the curriculum.MethodologyA survey was developed to gather insights from biotechnology professionals regarding the AIand generative AI (GenAI) tools and techniques they currently use or train their employees toutilize. The objective is to identify the most critical AI skills and tools required in the industry andassess whether there are gaps in the current biomedical engineering curriculum that need to beaddressed. The survey design avoids directly soliciting advice from participants; instead, itfocuses on understanding industry practices to infer actionable insights for curriculumimprovement.The survey consists of the
Paper ID #46480Emotions in Education for Sustainability in EngineeringDr. Angela R Bielefeldt, University of Colorado Boulder Angela Bielefeldt is a professor at the University of Colorado Boulder in the Department of Civil, Environmental, and Architectural Engineering (CEAE) and Director for the Engineering Education PhD Program. Her research interests include social responsibility, ethics, sustainability, and community engagement. She is a Fellow of the American Society for Engineering Education (ASEE) and a licensed P.E. in Colorado.Dr. Joan Tisdale, University of Colorado Boulder Dr. Joany Tisdale is a Teaching
and professional developmentsupport. As a woman of color with a STEM background and a doctorate in higher education, theprogram director set out to address expected resistance to the program’s success at the institutionduring scholars’ recruitment. When she became a Fellow in a national leadership developmentprogram, she interviewed senior leaders across the university. This included leaders who oversawacademic, fiscal, and other business decisions at college and university levels. From theseinterviews, she discovered more about the inner workings of human resources, institutionalequity, general counsel, ethics and compliance, and diversity, equity, and inclusion units. When she poked into the daily actions of the organization
ethical uses of LLMs, which included helping to understand concepts,correcting grammar, and creating citations, among others. When pressed, students revealedstress, running out of time, and failing to find the answer for themselves pushed them to usingLLMs in ways that may seem unethical [4].In a computer science course, LLMs can be used to both generate code and help a studentunderstand it [5]. Depending on how the LLM is being leveraged, it could be perceived as abenefit or risk to the student [6]. During their first year, many computer science students learnthe fundamentals of programming, which serves as a critical foundation for their future computerscience courses. However, as they encounter difficult programming challenges on a
diversity andinclusion in an Engineering Department,” Journal of Professional Issues in EngineeringEducation and Practice, vol. 145, no. 2, pp. 1-12, April 2019.[5] M. N. Miriti. “Nature in the eye of the beholder: A case study for cultural humility as astrategy to broaden participation in STEM”, Education Sciences, vol. 9, no. 4, pp. 1-10, Dec.2019.[6] E. E. Anderson, S. Solomon, E. Heitman, J. M. DuBois, C. B. Fisher, R. G. Kost, M. E.Lawless, C. Ramsey, B. Jones, A. Ammerman, and L. F. Ross. “Research ethics education forcommunity-engaged research: A review and research agenda,” Research Ethics Education, vol.7, no. 2, pp. 3-19, March 8, 2012 [Online]. Available:https://journals.sagepub.com/doi/abs/10.1525/jer.2012.7.2.3. [Accessed Nov. 25, 2024
institutionsto ensure compliance with ethical practices. For our recruitment, we wanted to ensure that ourrespondents would have at least some experience with makerspaces, thus we recruited fromclasses that include a makerspace component. We recruited students in the Fall semester of 2024and are preparing for a second round of data collection in the Spring semester of 2025. Given thelength and complexity of our instrument, we are looking for at least 200 good-quality responsesfrom students in order to perform the EFA proposed for this phase.Conclusions We want to acknowledge the progress we made in the almost two years of the project aswe look into the future and anticipate the impacts of our research. First, we successfullydelineated and