special attention to the impact thatsituating modeling in engineering education within public policy brings to the discourse on thetopic. Our findings will advocate for a future that is safer for the public. 1. IntroductionThe primary reason for test dummies in crash testing is to measure human injury under differentconditions. Test dummies play a crucial role as part of testing programs that provide valuabledata to both automative manufacturers and customers. Automotive manufacturers gain insightinto the simulated behavior of the human body in their designs during crash under regulatedconditions. They are, therefore, required to meet certain standards as regulated by the NationalHighway Traffic Safety Administration (NHTSA), a federal agency
ongoing and future work.problem descriptionGenerative AI development currently involves three major groups of people involved in anadversarial relationship over fundamental intellectual property rights: 1) artists, 2) AI developers,and 3) producers and distributors of these commercialized artistic works. The members of thefirst group, artists and writers, publish their work as a central element of their professional lives.Without publication and sales, artists and writers cannot make a living. However, whenever anartist or writer publishes something, the published work contains samples of the creativecognitive algorithms the artist or writer used to make the work. For more on cognitivealgorithms, see [1].Cognitive algorithms are an adaptive set
admissionsto broaden access. These graduate programs highlight innovative approaches to online engineeringeducation but also raise questions about learner preparedness, credential recognition, and programscalability. Finally, we explore the integration of artificial intelligence (AI) tools in asynchronousonline platforms, including both their promise for enhancing personalization and the risks theypose to critical thinking and equity. This paper concludes with actionable recommendations forcourse design, technology use, and institutional policy to support inclusive and effectiveasynchronous learning. 1. IntroductionOnline education, particularly asynchronous programs, has become a popular choice in recentyears. Asynchronous learning is different
mitigate any negative impacts associated with high immersion levels. Cognitive load refers to the amount of information that our working memory can effectively process, highlighting the cognitive limits of information processing. When cognitive demands exceed these limits, it can lead to cognitive overload, which negatively impacts both learning outcomes and overall satisfaction. Therefore, when designing virtual training programs that incorporate various multimedia elements and interactive features, it is essential to manage cognitive load carefully. Maintaining an optimal cognitive load will facilitate effective learning, ensuring that participants can engage fully without becoming overwhelmed by excessive information [1-3]. This approach is
decade. The production rate, stable at just under 2boats per year for the past 15 years, is projected to exceed 5 boats annually by 2030 due togeopolitical uncertainty. This growth will necessitate a substantial increase in the submarineindustrial base (SIB) workforce, with 15,000 annual new hires through 2032 [1]-[5]. Theseexpansion efforts have driven considerable investment in developing a STEM-literate navalworkforce pipeline in regions of high SIB density. This need is demonstrated in effortspioneered in southern New England, developing new pedagogies for K-12 outreach and teachersupport programs [6], [7].This transitory period “could lead to a period of heightened operational strain for the SSN force,and perhaps a period of weakened
is an effectivestrategy to increase student engagement with team members and with the material itself [2][12],and help students cultivate professional communication and feedback skills [13].However, team-based learning is known to face persistent challenges with unequal contributions,misreporting of peer contributions [1] [14] [20] and negative perceptions [10], despite havingvarious mechanisms for encouraging student participation. Grading mechanisms cansignificantly influence team-based learning and outcomes. Many existing approaches have beenimplemented to address free-riding and increase students’ accountability [3][4]. Despite thecollective goal of team-based learning, students are often driven by individual incentives [6].This
between the professions as well as facilitating thecommunication of the professions with the public. Collectively, the weight of evidence of theliterature identified in systematic reviews supports the inclusion of nursing in STEM.IntroductionSTEM, or science, technology, engineering, and math, is a collection of fields identified asessential to maintaining a competitive advantage for the United States (US) in the globalmarketplace [1]. The US marketplace, herein measured as the annual Gross Domestic Product(GDP), is approximately twenty five trillion dollars ($25T) [2]. Approximately seventeenpercent, or four and one-half trillions dollars ($4.5T) is spent on healthcare annually. And nearlythirty percent of the Federal government total
Management Education Among Engineering Students Changwon Son1, Mihwa Park2, and Wesley Wehde3 1 Department of Industrial, Manufacturing, & Systems Engineering, Texas Tech University, Lubbock, TX 2 Department of Curriculum and Instruction, Texas Tech University, Lubbock, TX 3 Department of Political Science, Texas Tech University, Lubbock, TXAbstractThe risks of emergencies, disasters, and crises are continuously increasing and makingdetrimental impacts on communities, especially engineering companies. Thus, engineers areexpected to possess necessary skills and knowledge for emergency, disaster, and crisismanagement (EDCM). However, educational efforts for engineering students, many of whombecome engineers, have
. The MBKMcurriculum emphasizes four key strategies: (1) in-depth learning approaches, (2) formativeand holistic assessments, (3) teacher leadership development, and (4) integrating Science,Technology, Engineering, and Mathematics (STEM) disciplines into existing subjects toenhance critical thinking and problem-solving skills. Despite its ambitious goals, theimplementation of MBKM has faced significant challenges. Geographical disparities acrossIndonesia’s 17,000 islands, the COVID-19 pandemic, unequal access to technology, andcoordination gaps among policymakers, educators, and administrators have hinderedprogress. These obstacles have led to inconsistent curriculum application, jeopardizing its fullimplementation by the 2024 target. A
respectful of differences, fair to individuals, and creates opportunities forbelonging as a way to increase the effectiveness of engineering design.IntroductionThe first tenant in the code of ethics of the Professional Engineer (PE) is to hold paramount thehealth, safety, and welfare of the public [1]. But who are the “public”, and how do the conceptsof diversity, equity, inclusion, and justice (DEIJ) fit within the definition of “public”? Does theethical code of engineering – and its emphasis on the public – provide an opportunity to promoteindividuals? In contrast to engineering, the code of ethics of the Registered Nurse (RN) includestwo important unique attributes [2]. First, the nurse is called to “advocate”. And second, thenursing patient is
transparency, contextual relevance, and effectiveness. This specific auto-ethnographic endeavor seeks to highlight the need for a Permanent Symposium on AI and document the considerations, challenges, and hopes in designing one.1 IntroductionThe rapid development and deployment of artificial intelligence (AI) technologies have broughtabout unprecedented opportunities for innovation and growth. However, these advancementshave also raised fundamental questions about the governance and accountability of AI sys-tems. Decision-makers in AI, including policymakers, industry leaders, and technologists,are grappling with a series of complex and interconnected challenges. These include ensuringtransparency and explainability in AI decision
theAdvanced Clean Cars II (ACC II) rule, setting forth an ambitious goal for all passenger cars,trucks, and SUVs sold in the state to be zero-emission vehicles by 2035. Continuing its decades-long role as a leader in environmental regulation, California paved the way for the rest of thenation to embrace such standards, with an additional twelve states adopting ACC II to date.Legislative and regulatory enthusiasm for electric vehicles reaches far beyond CARB’s rule asthe Bipartisan Infrastructure Law (BIL) and Inflation Reduction Act (IRA) allocated over $7.5billion to EV infrastructure and another $43 billion to projects ranging from batterymanufacturing to workforce transition for auto workers[1]. However, the goals set out by ACC IIwill require
coherent and complete content structure forthis study. Additionally, this paper adopts a case study approach, presenting thewell-established practices of certain universities in a concise yet comprehensive caseformat to help readers better understand specific aspects of practical implementation. Through the educational practices of these universities, this study aims tosummarize the practices and reforms related to the digital transformation ofengineering education in Chinese universities, identify common challenges, andpropose several policy recommendations. Figure 1 The framework of the paper2 Background of digital transformation of engineering education in China2.1 Digital economy Since the 1940s, the