primarily be in fields like AI development,machine learning engineering, and data science, along with roles focused on overseeing AIsystems and ensuring they align with ethical and regulatory standards [10]. Business leaders are already recognizing the importance of AI for the future of theiroperations. 98% of executives agree that AI is a crucial component of their businesses,underscoring the strategic importance of embracing AI in order to remain competitive [7]. As AIcontinues to evolve and become an integral part of everyday operations, it is expected that its’influence will be as transformative to the global economy as the internet and electricity were intheir times. In fact, many experts argue that AI’s widespread application could reshape
the program, 2) the associated learningoutcomes (LOs) are very high-level (versus the specific LOs associated with discipline-specificcourses, such as Circuit Analysis, Statics, and Dynamics), and are thus more easily satisfied usinggeneral project-based assessments. To initiate the CURES development process, course learning outcomes were assessed toidentify the subset of outcomes which did not easily integrate within a research-based project.Course LOs are provided below: 1. Describe the engineering majors, engineering profession, roles, organization, engineering ethics, and careers; investigate professional societies and licensing as a professional engineer; create an initial career development plan and understand the
of science/engineering. 12. I have learned about ethical conduct in my field. 24. I have confidence in my potential to be a research scientist or engineer.The following research questions guide our analysis: • RQ1. Are students making gains on outcomes associated with UREs? • RQ2. How do outcomes compare to other types of UREs?We collected survey data over four offerings of the course: spring 2018, fall 2018, spring 2019,and fall 2019. The pre survey was administered at the end of week 2 and the post survey wasadministered at the end of the course (week 11). In total, there were 72 responses that had apre/post response match.Results and DiscussionFigures 2-4 compare
, detailed inAppendix A, considered qualitative research design, including previous work on student sense ofbelonging [18], [19]. In this study, the qualitative questions complement the aforementionedquantitative questions, providing necessary context. All responses were analyzed for commonthemes to provide a deeper understanding of students’ perspectives.Survey AdministrationThe survey was created using Google Forms and was distributed to all EMPOWER programstudents through email. Participation in the survey was voluntary, and informed consent wasobtained at the beginning of the survey. The study was reviewed and approved by UC SanDiego’s Institutional Review Board (IRB), ensuring compliance with ethical guidelines forresearch involving human
sharing agreement to share student-level databetween our institutions. In order to effectively comply with FERPA requirements around datasharing, as well as ethical obligations to students, we developed a data sharing agreement thatenumerates each institution’s roles and responsibilities for providing and protecting confidentialinformation. Below are some lessons and recommendations from our experience developing andimplementing this agreement.Determining data needs: Creating the provisions of a data sharing agreement requires knowingwhat pieces of information will be used in analysis (at least in general terms), so that those itemscan be enumerated. We wanted to limit sharing to what was needed out of respect for studentprivacy. However
AI courses (Discover AI, AI Ethics, and AI for Business)[10], [11]. This qualitative technique aims to explore the meanings and interpretations that the students make of their experiences, and to observe common themes across the participants allows us to capture the “essence” of the phenomenon [12]. Data collection was through pre-interview questionnaires and semi-structured interviews lasting approximately 60 minutes with each of the 19 participants. Interview transcripts were read several times and key portions of each were coded for meaningful units. Memos were created to summarize key points of each interview in order to ease comparison of common essences and to organize demographic
), Developing a Culturally Adaptive Pathway to Success: Implementation Progress and Project Findings Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual Online . 10.18260/1-2--34412[12] Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A development perspective. Educational Psychologist, 6(1), 49–78.[13] Wigfield, A., & Eccles, J. S. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015[14] Moreno MA, Goniu N, Moreno PS, Diekema D. Ethics of social media research: common concerns and practical considerations. Cyberpsychol Behav Soc Netw. 2013 Sep;16(9):708-13. doi
education research and engineering education research. Her work involves designing and researching contexts for learning (for students, educators, and faculty) within higher education. Her research draws from perspectives in anthropology, cultural psychology, and the learning sciences to focus on the role of culture and ideology in science learning and educational change. Her research interests include how to: (a) disrupt problematic cultural narratives in STEM (e.g. brilliance narratives, meritocracy, and individualistic competition); (b) cultivate equity-minded approaches in ed- ucational spheres, where educators take responsibility for racialized inequities in student success; and (c) cultivate more ethical future
. Finelli studies the academic success of students with attention-deficit/hyperactivity disorder (ADHD), social justice attitudes in engineering, and faculty adoption of evidence-based teaching practices. She also led a project to develop a taxonomy for the field of engineering education research, and she was part of a team that studied ethical decision-making in engineering students.Dr. Maura Borrego, University of Texas at Austin Maura Borrego is Director of the Center for Engineering Education and Professor of Mechanical Engi- neering and STEM Education at the University of Texas at Austin. Dr. Borrego is Senior Associaate Editor for Journal of Women and Minorities in Science and EDr. Jenefer Husman, University of Oregon
needed to thrive in thedigital age. This exposure not only broadens students' career opportunities but also cultivatescritical thinking, problem-solving, and innovation abilities that are essential for success in the21st-century economy. Moreover, familiarizing students with AI technologies early on fosters adeeper understanding of ethical considerations, biases, and societal impacts, enabling them tobecome responsible and informed users and contributors to the development of AI solutions.Ultimately, integrating AI education into Community college curricula equips students with thecompetencies necessary to adapt and excel in a world where AI is increasingly shaping our dailylives and professional landscapes.To bridge this gap and build capacity
education. It would be ethical and based more on a moralresponsibility for our educational system and government to believe in and care about growingthe intellectual capital of all its citizens [2]. However, research on this topic from the CommunityCollege Research Center and engineering education researchers located the source of interest fordoing this important work as the need to meet the needs of industry. More specifically, statesneed higher education to play a large role in workforce development to meet industry demand forengineers and computer scientists [3], [4]. This reliance on higher education to train the futureworkforce was named social efficiency [5]. Beyond social efficiency, Labaree explained socialmobility was a common goal for
ethnic groups, Hispanic/Latino students suffer low educationalattainment at all levels, e.g. 18% of all Associates, 12% of all Bachelors, 9% of Masters and 7%of Doctorate degrees [1]. The authors describe multiple contributing factors such asfirst-generation students, the Hispanic cultural value for work ethic and contributing income tothe family, distrust of the American education system, aversion to debt, and a focus on survivalrather than success that is socialized in many working-class Latino children due to poverty issuesand low-income levels. Twenty-five years of lower STEM degree completions despite growingenrollment, along with higher education costs, lack of preparedness [2], and not feeling welcomeon campus [3] contribute to the equity