systems thinking competencies incontexts extending beyond self-reported attitudes and behaviors. The problem scenario is ahypothetical vignette that requires students to evaluate multiple aspects within an ill-structuredproblem context. This scenario includes information that potentially encompasses engineeringand technical skills, economic feasibility, ethical considerations, and cultural sensitivity, all ofwhich should be taken into account when analyzing potential solutions [9]. "The Village of Abeesee has about 50,000 people. Its harsh winters and remote location make heating a living space very expensive. The rising price of fossil fuels has been reflected in the heating expenses of Yakutia residents. In fact, many
areessential.References1. Multisim. http://ni.com2. SPICE: http://ni.com3. Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://arxiv.org/abs/1810.04805. 2019.4. Rishi Bommasani, et al. On the Opportunities and Risks of Foundation Models. https://arxiv.org/abs/2108.07258. 2021.5. Wayne Xin Zhao, et al. A Survey of Large Language Models. https://arxiv.org/abs/2303.18223. 20236. https://chatgpt.com7. Alvarez, J.M., Colmenarejo, A.B., Elobaid, A. et al. Policy advice and best practices on bias and fairness in AI. Ethics Inf Technol 26, 31 (2024). https://doi.org/10.1007/s10676-024- 09746-w8. Valerio Capraro, et. al., The impact of generative
, data-processing paths, control logic and stored- program machines.To limit the scope of this study, only the analog section of the course will be considered,primarily due to the effort required in categorizing and reassessing all student exam papers. Also,the course sits between tightly scaffolded prerequisite and subsequent analog electronics courses,so the analysis could provide insight into the students’ success in the future based on these examresults. This research was approved under Human Ethics Protocol 29248.MethodologyThe analog section of the exam comprised of five questions, totaling 86 marks. Some questionswere also split into smaller sub-questions related to the overall theme/topic of the question. Eachquestion was assessed
withthermal responses gave them a clearer intuition for how energy systems behave in response tovarious heat sources and sinks.RQ4: To what extent was the course design supportive of students with marginalized identities?In alignment with ethical research norms, quantitative data involving fewer than five respondentsis not reported to protect participant anonymity. However, when treated qualitatively, theresponses offer meaningful insights. All three students from marginalized backgrounds reportedthat laboratory activities were highly beneficial to their learning, while traditional lectures andclass discussions were less helpful. This indicates a possible compensatory effect, where the labsprovided an essential learning support for these students
weekly professional development workshop seriesfocused on providing students with the skills necessary to succeed in their summer researchproject and beyond. Active-learning workshop activities include: scientific communication(delivering an elevator pitch for different audiences, presenting at lab meeting, writing aconference abstract, making and presenting a scientific poster), research methods (hypothesisgeneration, laboratory documentation, data management, searching the scientific literature), andidentifying the broader impacts of their research (scientific outreach, research ethics andalgorithm bias). In addition to developing students’ research skills, a major goal of this workshopseries is to develop a sociotechnical mindset in the
remote learning context (during COVID-related remote learning)?• How does climate change impact on my local community, my state, and the world?• What are the ethical dilemmas and possible benefits of AI and robotics in our everyday life?• How can we strengthen community partners businesses and endeavors through internships and design work?These inquiries were advanced in a design cycle fashion that engaged students in investigatingchallenges and celebrations of community and how to advance and act within these contexts forgood. From 9th through 12th grade, students made use of engineering literacies and tools, such asroot cause analysis, 5 whys, stakeholder mapping, pitches, and design challenges. We found thatthe co-development of
Higher Education,vol. 45, no. 3, pp. 342–360, 2021.[2] A. Yoshimura and C. W. Borst, "A study of class meetings in VR: Student experiences ofattending lectures and of giving a project presentation," Frontiers in Virtual Reality, vol. 2, p.648619, 2021.[3] E. Southgate et al., "Embedding immersive virtual reality in classrooms: Ethical,organizational and educational lessons in bridging research and practice," International Journalof Child-Computer Interaction, vol. 19, pp. 19–29, 2019.[4] T. E. Goldsmith and P. J. Johnson, "A structural assessment of classroom learning," inPathfinder Associative Networks: Studies in Knowledge Organization, R. W. Schvaneveldt, Ed.Norwood, NJ: Ablex, 1990.[5] R. D. Reason, P. T. Terenzini, and R. J. Domingo
. Statistical analyses included paired t-tests forwithin-group comparisons, independent t-tests for between-group comparisons, and the Mann-Whitney U test for non-parametric data. Effect sizes were calculated using Cohen’s d to quantifythe magnitude of observed differences, ensuring a comprehensive evaluation of the VRintervention’s impact on CT skills. Ethical guidelines, including informed consent andinstitutional review board approval, were strictly followed throughout the study.Results Descriptive statistics revealed differences in CT skills between the control and experimentalgroups across pre-test and post-test scores. The experimental group demonstrated improvementsin Creative Thinking (mean = 4.21) and Problem-Solving (mean = 3.01), while
-oriented.” Then, they come to see that engineeringknowledge is everywhere, as one parent mentioned: “he didn’t realize all of the pieces that go intoengineering, like project management and…working with the community.” Parents also possesssome understanding of the practical application of engineering knowledge in the real world, as theyalso view engineering as a form of service: “...it's almost like you’re doing engineering, but you’reworking with the community... it’s a service…”When discussing their children’s choice to pursue an engineering course at the high school level,parents commonly highlighted their children’s strengths in mathematics and science, as well asqualities such as intelligence and a strong work ethic, before referring to other
at the University of Wisconsin, Milwaukee. Papadopoulos has diverse interests in structural mechanics, sustainable construction materials (with emphasis in bamboo), engineering ethics, and engineering education. He is co-author of Lying by Approximation: The Truth about Finite Element Analysis. As part of the UPRM Sustainability Engineering initiative to develop a new bachelor’s degree and curricular sequence, Papadopoulos is PI of A New Paradigm for Sustainability Engineering: A Transdisciplinary, Learner-Centered, and Diversity-Focused Approach, funded by the NSF HSI program. He is also an active member of the Engineering for One Planet Network.Dr. Michael J. Prince, Bucknell University Dr. Michael Prince is a
). Participant in cybersecurity competitions (5+ years), employed as Systems Exploitation Engineer (3 years), general focus on using gamified cybersecurity platforms as learning tools. Founded and currently runs the university's cybersecurity club, working to make cybersecurity more welcoming to students in traditional academic pathways by teaching the skills needed for participation as well as the ethical perspective. By teaching in a formal setting, he aims to share the excitement of hands- on learning in cybersecurity and inspire more students to explore the field.We designed Hacking Competitions to foster intermediate-level proficiency in technical andprofessional skills that align with the direction of the cybersecurity industry, while
x x x ABET (2) - Engineering Design x x x x ABET (3) - Communication x x x x ABET (4) - Ethics & Professional Responsibilities x x x x ABET (5) - Effective Teams x x x x ABET (6) - Experimentation x x ABET (7) - Lifelong Learning x x xThe electrical and mechanical engineering capstone courses feature open-ended projects, whilechemical and civil engineering capstone courses are more structured
qualities, experience, and beliefs. These include essays on leadership, academicresearch, community service, and personal and professional ethics. Therefore, the data consistsof numerical features such as standardized examination scores and Grade Point Averages (GPA),along with textual data from the essays and letters of recommendation. Applications also collectpersonal information including but not limited to the applicant's name, address, gender, andethnicity. Figure 1 details the potential stages in the admissions pipeline where bias couldemerge and where AI is currently used as per the Intelligent survey [4].In the context of university admissions, features like gender and ethnicity are usually examinedfor bias, as done by Kahlor et al. [13
. ©American Society for Engineering Education, 2025 Change | Makers: What can come next in engineering design?IntroductionThere have been growing calls for engineers and engineering educators to take more completeresponsibility for their role in society as technological developers and technically literatemembers of society, the exclusivity of their practice, and the impact their work has on the worldboth socially and environmentally. These calls appear in various forms including SustainableDevelopment Goals (SDGs) [1], calls to action [2], and academic literature [3-5]. However,change in engineering often comes slowly. While some change has been seen, for example, insome engineering codes of ethics and graduate attributes, others have been
enjoys thinking Q3.21 There are no ethical problems within my team that teammates are unable to resolve Q3.22 My team shares information and individual team members do not keep information to themselves Q3.23 My team is committed to the team goal Q3.24 Everybody in my team strives to express his or her opinion Q3.25 My team understands their roles and responsibilities for doing various team tasks Q3.26 My team understands where they can get information for doing various team tasks Q3.27 My team understands their interaction patterns Q3.28 My team informs each other about different work issues Q3.29 My team is likely to make a decision together Q3.30 My team can
low engagement,only instructors were given access to the peer assessment results. This allowed students toprovide candid feedback without fear of judgment from their teammates.Regarding the ethical considerations of this study involving human subjects, due to the use ofpersonal data from participants in an international collaborative program, the study wasconducted in compliance with established research ethics guidelines, including ethicalstandards, codes of conduct, and responsibilities. Specifically, (1) prior consultation was heldwith program coordinators to obtain permission for conducting the survey and using studentdata, and (2) students were informed of the research purpose and content, and their consentwas obtained before
engineering course was part of the broader Discover program designed toprovide high school students access to undergraduate-level education while addressing thegrowing demand for STEM education to inspire future engineers. The course "Introduction toStructural Engineering" ran for 10 weeks, providing high school students from all grades (9th-12th) with a comprehensive foundation in structural engineering principles while fosteringcritical thinking, problem-solving skills, and ethical awareness. Institutional data were collectedon students participating in this program. Student racial and ethnic backgrounds are shown inFig. 1. The engineering course was one of four courses offered in Summer 2024, accounting forapproximately 21% of the total summer
computer science and engineering from the University of Minnesota. Next, she was a Postdoctoral Fritz Family Fellow with the Massive Data Institute of McCourt School of Public Policy at Georgetown University, Washington, DC. She is involved in projects in the intersection of education, data mining, machine learning, ethics, and fairness. Her research interests include data mining, recommender systems, predictive models within educational contexts, and the fairness concerns that arise from their use. Her goal is to help students succeed using data and machine learning models.Dr. Christine Lisetti, Florida International University Christine Lisetti is an Associate Professor at Florida International University (FIU) in
sustainability. As geographer LauraPulido [36] writes, environmental injustice, particularly environmental racism, fundamentallysustains contemporary racial capitalism through land, resources, and human appropriation,commodification, and segregation. Examples of engineering projects maintaining environmentalracism include invasive infrastructures such as oil and gas pipelines [37], corporate entitlementsto pollution such as the petrochemical industry in Chemical Valley, Ontario [38], andurbanization projects of city-building to engineer racist settler colonial landscapes [39].However, these were never discussed during my formal engineering education, not even in myengineering ethics or engineering social impact courses.Additionally, my engineering
into engineeringand STEM fields. Thus, it is important to explore in which ways critical consciousness can beadded to engineering programs.In engineering education, there have been some recent efforts to increase critical thinking andsocial awareness with mixed results. When critical consciousness projects have been added tosome engineering curricula, research has shown that students still struggle to fully consider thebroader ethical implications of their work [17, 18]. Despite the various approaches thatresearchers have utilized, such as journal writing [19], integrating critical literacy approaches[18], user-centered design projects [10], and multi week course projects [4] there are stillquestions about how to better prepare engineering
. Thisdemand often exceeds the capacity of available TAs, leading to delays in addressing studentconcerns and creating a bottleneck in the learning process. Additionally, the repetitive nature ofmany inquiries can detract from TAs' ability to provide meaningful, in-depth guidance [6]. AI chatbots offer a scalable solution to mitigate these challenges by complementing therole of TAs. These tools can handle repetitive, procedural queries, allowing TAs to focus on morecomplex instructional tasks. Moreover, AI systems equipped with problem-solving frameworkscan promote response consistency, reducing variability in student support [7]. Despite theseadvantages, integrating AI tools must address ethical considerations and potential limitations,such as
(Fig. 5). These positive changes wereassociated with three primary response trends. First, 27 students (27.6%) described how failure in thecourse motivated changes in their behavior or work ethic: “It has taught me to ask more questions when I am struggling to figure something out instead of sitting around doing the same thing over and over.” “It has showed me that using failure as motivation can help future attempts rather than give up and hurt future attempts.”Second, 19 students (19.4%) adopted a growth mindset, seeing failure as an opportunity to learn: “It has helped be to think of failure better since every time I would not get the score I wanted I always was able to look at what I did wrong
into smaller chunks reduces cognitiveload, making learning more effective. These foundational principles align with microlearning'sstructuring of educational content for better learner outcomes. Bartram [41] further validated theeffectiveness of such strategies by demonstrating how bite-sized simulations in medical trainingenhanced engagement and reduced cognitive overload.Despite its potential, the integration of AI in microlearning faces challenges. Issues such as biasin AI-generated content, ethical concerns, and over-reliance on automation remain critical areasof discussion. Ivanov and Soliman [35] cautioned against the lack of depth and critical analysisin AI-generated materials, which could lead to surface-level understanding if not
field. The more perspectives takenwhen considering a solution broadens its validity and applicability. As engineers are called to considerparamount the safety and welfare of the public by the National Society of Professional Engineers Codeof Ethics, it is necessary to consider a range of perspectives to produce solutions representative of allindividuals [12]. Additionally, females have been found to generally display more empathy, the capacityto understand the perspectives of others, than males [5]. Thus, increasing female participation inengineering could lead to an increase in empathy demonstrated in engineering solutions. Empathyallows engineers to better understand the needs of society, so more empathy could allow engineers tobetter meet
-minded efforts that focused on discussions centered aroundunderrepresented and minority groups. For example, teaching modules to discuss ethics andimplicit bias, doing literature reviews from authors of diverse backgrounds, and facilitatinggroup reflection on stories of people from disadvantaged groups and how they encountered andovercame different engineering challenges. “I have ethics modules in each of my courses so I try to review those modules and make sure I have up to date information about how engineering design impacts as many identity groups”While the team could identify specific efforts of equity-minded teaching in their classroom,many struggled to recall concrete examples of success stories that directly resulted
used a mixed methods research or design-based research approach,and two papers were literature reviews. Furthermore, research frameworks utilized by researchpapers focused on coops or cooperative education included: 1. Engineering identity 2. Motivation theory 3. Self-efficacy 4. Mental HealthFinding 2: Learning and Skill Development The second major finding from our review is that there were many descriptive andresearch papers focused on learning, most of which focused on students’ learning and skilldevelopment of professional skills. The most common professional skills discussed included:communication, leadership, engineering ethics, time management, and general workplaceknowledge. In contrast, there was only one
ethical imperative, and empowering individuals who would otherwise not be ableto fully engage in STEM increases our national potential to advance science and solve real-worldproblems. In this paper, we share a conceptual framework that seeks to define the “interruptions”experienced by Black women in STEM as they navigate undergraduate STEM programs. Ourframework, grounded in Black feminist epistemologies, is informed by two years of datacollected from surveys, interviews, focus groups, reflective journals, and audio diaries of fortyBlack women undergraduates at three institutions of higher education. This frameworkilluminates the relationship between societal power structures, Black women’s STEM self-concept, and selected coping strategies
) for the Fig. 5. Survey responses (n=130) to the statement “I am confident that I can write well statement “I prefer to write without using without using generative AI.” The vast generative AI.” The majority of respondents majority of respondents (90%) agree with the (66%) agree with the statement. statement.There is a significant, moderately strong correlation between students’ confidence in writing wellwithout the use of generative AI and their preference for writing without generative AI(chi-square: 71.274; Cramer’s V: 0.427; df: 12; p < 0.0001). Several reasons might explain whycurrently enrolled students would prefer to write without using GenAI (such as prior experience,ethical concerns about plagiarism and
and communicate thoseresults to others” [14].Data LiteracyGiese et al. proposed a data literacy framework for the purposes of engineering education thatfocused on statistical and programming competence as central components, as well as a thirdpillar to address ethical issues in terms of “transparency and awareness”[15]. However, the scopeof this framework does not align with this course redesign because (1) this course does not havea programming component, and (2) the “transparence and awareness” component of thisframework doesn’t address the competencies needed to productively work with, contextualize, orcommunicate with data to multiple audiences. As a result, the authors looked beyond theengineering education literature to other