technology adoption or avoidance, andthe targeted framework interact and present.AcknowledgementsThis material is based upon work supported by the National Science Foundation under AwardNumber 2346881. Any opinions, findings and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect those of the NSF.References[1] D. Baidoo-Anu and L. O. Ansah, "Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning," SSRN, 2023.[2] M. Flavin, "Disruptive technologies in higher education," Research in Learning Technology, vol. 20, 2012.[3] J. Qadir, "Engineering Education in the Era of ChatGPT: Promise and
), pp. 45, 2022.[2] S. Malik, K. Muhammad and Y. Waheed, "Artificial intelligence and industrial applications-A revolution in modern industries," Ain Shams Engineering Journal, pp. 102886, 2024. .[3] A. B. Rashid and A. K. Kausik, "AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications," Hybrid Advances, pp. 100277, 2024.[4] S. Azhar, "Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry," Leadership and Management in Engineering, vol. 11, (3), pp. 241–252, 2011.[5] T. O. Olawumi and D. W. Chan, "Development of a benchmarking model for BIM implementation in developing countries," Benchmarking: An
, 2023.[3] ANSYS Workbench, https://www.ansys.com/products/ansys-workbench[4] N. Smith, J.L. Davis, “Connecting theory and software: Experience with an undergraduatefinite element course,” The ASEE Annual Conference and Exhibition, Seattle, June 2015.[5] K.A. Watson, A.O. Brown, R.K. Hackett, A. Pham, “ Finite Element Analysis LearningModules for an Undergraduate Heat Transfer Course: Implementation and Assessment,” TheASEE Annual Conference & Exposition, June 2012.[6] S. Higbee, S. Miller, “Finite Element Analysis as an Iterative Design Tool for Students in anIntroductory Biomechanics Course,” Journal of Biomechanical Engineering, 143(12), 2021.[7] A. Hickey, S. Xiao, “Finite element modeling and simulation of car crash
received the Ph.D. degree in electrical engineering from the University of Western Ontario, London, ON, Canada in 2006. She received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, in 1993 and 1996 re ©American Society for Engineering Education, 2025Community Partner and Institutional Stakeholder Perspectives on the Impact of the NSF-STEM Scholars of Excellence in Engineering and Computing Studies ProgramAbstractThe Scholars of Excellence in Engineering and Computing Studies (SEECS) program, funded byan NSF S-STEM grant, delivers engineering solutions that tackle community challenges whileproviding students with opportunities for
insightsgained from this analysis will be used to inform the development of future data collectionprotocols aimed at conducting interviews with university students, faculty, and administrators inthe subsequent phases of the project.AcknowledgementsThis material is based upon work supported by the National Science Foundation through AwardNo. 2340778. Any opinions, findings, and conclusions, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References[1] AIR, “Broadening participation in STEM (science, technology, engineering, and mathematics),” American Institutes for Research. Accessed: Jul. 21, 2022. [Online]. Available: https://www.air.org/project
professor of chemical engineering at Bucknell University and co-director of the National Effective Teaching Institute. His research examines a range of engineering education topics, including how to assess and repair student miscoProf. Dominic J Dal Bello, Allan Hancock College Dom Dal Bello is Professor of Engineering at Allan Hancock College (AHC), a California community college between UC Santa Barbara and Cal Poly San Luis Obispo. At AHC, he is Department Chair of Mathematical Sciences, Faculty Advisor of MESA (the Mathematics, Engineering, Science Achievement Program), has served as Principal/Co-Principal Investigator of several National Science Foundation projects (S-STEM, LSAMP, IUSE). In ASEE, he is chair of the
Assemblies for hosting student visits. We appreciate thevaluable feedback from our Advisory Board: Dr. Sean Barrett, Dr. Sissi Li, Dr. Davida Smyth,and Dr. Ruth Stark. We also would like to acknowledge the many undergraduate studentresearchers who have contributed to this project, an updated list can be found on our website [9].References[1] National Quantum Initiative, https://www.quantum.gov/. [Accessed Jan. 11, 2025].[2] NSF Focus Area: Quantum Information Science, https://new.nsf.gov/focus-areas/quantum. [Accessed Jan. 11, 2025].[3] I. Siddiqi, D. Gil, and J. S. Broz, “Quantum for All,” Physics, vol. 13, pp. 143, Sept. 2020.[4] 2023 ACS Guidelines for Undergraduate Chemistry Programs, https://www.acs.org/content/dam/acsorg/about
itsmanufacturer, is not guaranteed or endorsed by the author(s).Acknowledgement and Material AvailabilityWe would like to thank Julia Chamberlain, Kathleen Cruz, Sara Dye, Rob Furrow, Irene Joe,Bwalya Lungu, Hannah Minter Anderson, Ali Moghimi, and Patricia Turner from the PCIDiversity, Equity, and Inclusion Faculty Learning Community at UC Davis. We will make ourcurrent learning module available at https://cube3.engineering.ucdavis.edu.References[1] U.S. Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices [Online] Available: https://www.fda.gov/medical- devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml- enabled-medical-devices[2] V. Binson
the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching, 44(8), 1187–1218.Forest, C. R., Moore, R. A., Jariwala, A. S., Fasse, B. B., Linsey, J. S., Newstetter, W. C., & Quintero, C. (2014). The Invention Studio: A university maker space and culture. Advances in Engineering Education, 4(2), 1–32.Godwin, A. (2016). The development of a measure of engineering identity. In 2016 ASEE Annual Conference & Exposition Proceedings. New Orleans, LA: ASEE.Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M.-C. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career
Prospects. National Academies Press, 2009.[2] P. S. Lottero-Perdue, “Elementary teacher as teacher of engineering: Identities in concert andconflict,” in ASEE Annual Conference & Exposition 2013, Atlanta, GA, USA, June 23-26, 2013.https://peer.asee.org/19487[3] S. M. Nesmith and S. Cooper, “Connecting engineering design and inquiry cycles: Impact onelementary preservice teachers’ engineering efficacy and perspectives toward teachingengineering,” School Science and Mathematics Association, vol. 121, pp. 251-262, 2021.[4] D. J. Sherfnoff, S. Sinha, D. M. Bressler, and L. Ginsburg, “Assessing teacher education andprofessional development needs for the implementation of integrated approaches to STEMeducation,” International Journal of STEM
agreeReferencesBorrego, M., Knight, D. B., Gibbs Jr, K., & Crede, E. (2018). Pursuing graduate study: Factorsunderlying undergraduate engineering students' decisions. Journal of Engineering Education,107(1), 140-163.Gilmartin, S. K., Thompson, M. E., Morton, E., Jin, Q., Chen, H. L., Colby, A., & Sheppard, S.D. (2019). Entrepreneurial intent of engineering and business undergraduate students. Journal ofEngineering Education, 108(3), 316-336.Lattuca, L., Terenzini, P., Knight, D., & Ro, H. K. (2014). 2020 Vision: Progress in preparingthe engineer of the future.Lee, W. C., Hall, J. L., Godwin, A., Knight, D. B., & Verdín, D. (2022). Operationalizing andmonitoring student support in undergraduate engineering education. Journal of
collaborative inquiry and dialogue. As we individually and collectivelyinterrogate our assumptions and beliefs and expand our knowledge about other ways of knowingand being, we have begun to see a quality of care emerge in our discussions – care about how wemight support each other and ourselves.AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.2234256 Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.References[1] J. Butler, Giving an Account of Oneself. New York: Fordham University, 2005.[2] A. Comeaux, Change (the) Management: Why We as Leaders Must Change
degree program at a large, public, research-intensive(R1) university in the southern U.S.Data Collection Co-creators were recruited through emails sent by each university’s disability resourceoffice and engineering department(s) to undergraduate students. These emails outlined eligibilitycriteria, which required co-creators to be currently enrolled undergraduate engineering studentsat that university who identify as disabled or as a person with disabilities. The emails invitedeligible individuals to participate in the study by reflecting on their disability-related experiencesat their university. Additionally, the emails detailed the participation process and offered a $40gift card as compensation upon completing the interview. To ensure
sharing among educators, it aimsto make PBL a more accessible and appealing pedagogical approach for engineering education.AcknowledgmentThis material is based upon work supported by the National Science Foundation under Grant No.2117224. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.References[1] D. Jonassen, “Supporting Problem Solving in PBL,” Interdisciplinary Journal of Problem- Based Learning, vol. 5, no. 2, Sep. 2011, doi: 10.7771/1541-5015.1256.[2] W. Hung, D. H. Jonassen, and R. Liu, “Problem-Based Learning,” 2008, pp. 485–506.[3] D. Boud and G. Feletti, The Challenge of Problem-Based Learning
frontier, this study offers a glimpse of how Copilot can support course updates usingsimple prompts. Alternative AI tools with different capabilities may be more effective in creatingspecific technical content.References [1] Batista J, Mesquita A, Carnaz G. Generative AI and Higher Education: Trends, Challenges, and Future Directions from a Systematic Literature Review. Information. 2024; 15(11):676. https://doi.org/10.3390/info15110676 [2] Noroozi, O., Soleimani, S., Farrokhnia, M., & Banihashem, S.K. (2024). Generative AI in education: Pedagogical, theoretical, and methodological perspectives. International Journal of Technology in Education (IJTE), 7(3), 373-385. https://doi.org/10.46328/ijte.845 [3] Choi, G.W
scientific and business authority in theengineering profession [1] in a metaphorical free body diagram, using Faulkner’s notion of “nutsand bolts” engineering identity [86] and Cruz et al.’s critical analysis of engineering ethicseducation [87]. Please see figure one for an illustration of this framework.Figure 1: Free body diagram of engineering professionalismBefore diving into the theoretical roots of this figure, I offer a brief explanation of my underlyingassumptions. Foundational to this image is the sociological tension between human agency andsocial structure. I view engineers as neither free agents nor objects living their lives according toa structurally determined script. Rather, I view them as individuals with some level of decision
explanation[s] of complex topics,” helping them understand information by summa-rizing key topics, explaining information in different ways, demonstrating “step by step walk-throughs for challenging problems,” or otherwise providing additional context and definitions ofterms. One respondent stated that they sometimes use AI to “understand a difficult phrase by past-ing it into the chatbot and asking it to explain it more clearly.” Respondents also noted the benefitof AI chatbots as a “24/7 teacher” and “free tutor” for students, allowing them “quick tutoringopportunities” to ask questions if there was not enough time in class or office hours, or if “a humanteacher is not available.” AI also has the capacity for “virtually unlimited follow up
framework for assessing and improving the project. Future work includes refiningdata collection methods, addressing limitations such as varying problems across cohorts, andfurther assessing research quality. Future efforts will focus on identifying student learningoutcomes, refining the project, and exploring new ways to introduce PROCESS andmetacognitive problem-solving to a growing cohort of engineering students.References[1] S. D. Sheppard, K. Macatangay, A. Colby, and W. M. Sullivan, Educating Engineers: Designing for the Future of the Field, vol. 9. San Francisco, CA: Jossey-Bass, 2009. [Online]. Available: https://files.eric.ed.gov/fulltext/ED504076.pdf[2] J. G. Donald, Learning To Think: Disciplinary Perspectives. The Jossey-Bass
that is brief, well-structured, and that takestheir psychosocial needs into account.Acknowledgements: This work was supported through funding by the National ScienceFoundation IUSE Grant No. 2111114/2111513. Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the National Science Foundation.Note: The first and last author share first authorship equally.References[1] Nat. Center for Science and Engineering Statistics (NCSES), “Diversity and STEM: Women, minorities, and persons with disabilities 2023,” Nat. Science Foundation, Special Report NSF 23-315, 2023. [Online]. Available: https://ncses.nsf.gov/wmpd[2] Nat.Center for Education
Technology Education, ASEE, 2024, https://mindset.ASEE.org. [Accessed: Jan. 14,2025].[6] S. Das, D. Kleinke, and D. Pistrui, “Reimagining engineering education: does Industry 4.0need Education 4.0?,” ASEE Annual Conference, Jun. 2020, Montreal, CA.[7] D. Pistrui, D. Kleinke, and S. Das, “The industry 4.0 talent pipeline: a generational overviewof the professional competencies, motivational factors & behavioral styles of the workforce,”ASEE Annual Conference, Jun. 2020, Montreal, CA.[8] D. Pistrui, J. Layer, and S. Dietrich, “Mapping the behaviors, motives and professionalcompetencies of entrepreneurially minded engineers in theory and practice: an empiricalinvestigation,” The Journal of Engineering Entrepreneurship, ASEE Special Issue, vol. 4
over adopting AI tool use in their curriculum. Per the framework,components contributing to a sense of agency include past experiences, expectations of thefuture, and present cultural, structural, and material conditions that can be opportunities, barriers,and resources [1].At the onset of the project, our team theorized several factors which might impact teacher’s AIuse based on Biesta et al.’s framework, including social supports or hindrances from otherteachers or administrators, school and community resources and access to use AI tools,perceptions of added value of AI tools on teaching outcomes, opinions and ethical concernsabout AI tools, and familiarity with AI tools from prior personal or professional use.Ecological Systems TheoryBiesta
that the intersection of their race or ethnicity and gender serves as anadvantage in facilitating opportunities. For example, Brianna, who is Latina, stated “one of thereason[s] why I’m here is because I got a really big scholarship due to my ethnicity and myaccomplishments. So I think, like, that definitely helps. Being a minority student, it gives yousome advantages.” In contrast, Jordan, a white woman, shared “since I've been [at institution] no,I don't think [my identity as a woman has] had any effect [on my experience].” Additionally, women of Color highlight the importance of communities of peers withshared identities. For example, Nicole, a Black woman, stated: The whole idea [of the minorities in engineering program
, 2000, doi: 10.1037/0003-066X.55.1.68.[7] M. Gopalan and S. T. Brady, “College Students’ Sense of Belonging: A National Perspective,” Educ. Res., vol. 49, no. 2, pp. 134–137, 2020, doi: 10.3102/0013189X19897622.[8] A. Master and A. N. Meltzoff, “Cultural Stereotypes and Sense of Belonging Contribute to Gender Gaps in STEM,” Int. J. Gender, Sci. Technol., vol. 12, no. 1, pp. 152–198, 2020.[9] K. Rainey, M. Dancy, R. Mickelson, E. Stearns, and S. Moller, “Race and gender differences in how sense of belonging influences decisions to major in STEM,” Int. J. STEM Educ., vol. 5, no. 1, pp. 1–14, 2018, doi: 10.1186/s40594-018-0115-6.[10] C. U. Lawrence and E. Lee, “WIP: Sense of Belonging Research in
Vin G B S Vin B S B S B G S Fig. 4: The common source amplifier (left) and the resulting bugs from disconnecting thebody (center) and connecting a PMOS gate, drain, and source with the NMOS body (right) Page 1 Page 2
(Figure 27) represents a male passenger who survived despite gender being a negativefactor in the model’s prediction. His high Pclass and fare ($35.5) played a crucial role inincreasing his survival probability. The cumulative SHAP value graphs (Figs. 28, 29) furtherhighlight these trends by selecting the few vital causes from the trivial many, showing thatinstance 3’s survival was driven by gender, wealth, and class, aligning with historical data where75% of women survived compared to only 19% of men. On the other hand, instance 55 survivedsolely due to his high social class, with gender contributing the least to his survival.3 Evaluation and FindingsIn this section, we evaluate the effectiveness of the DARE-AI labs through surveys conducted
seven features: (1) First-year or sophomoreengineering vs. non-first-year or sophomore; (2) hands-on vs. non-hands-on; (3) whether thestudy was on first-year curriculum design (4) types of microcontroller use (5) If the study involvesmicroelectronics, (6) Identify the goal(s) of the study, and (7) Present the findings andconclusions. The three identified themes were (1) curriculum design, (2) student learningoutcomes, and (3) challenges and limitations. The thirty-two papers in the study providedinformation on each theme except “challenges and limitations,” which only included 31 papers,as one did not mention any challenges.Table 1: Differentiation of articles based on curriculum designs, Device Type, and Level
] witheach differing in focus and scope. Some tools emphasize broad abilities, while others targetspecific subskills. Some prioritize language over culture or focus on international differenceswhile overlooking intracultural variation. Others remain ambiguous, with unclear objectives[58], [59]. A summary of some of these tools is provided in [58], including those designed forindividuals, teams, leaders, and organizations [59].In the process of identifying a suitable theoretical framing for this study, we reviewed severalinstruments, each offering unique perspectives on cultural awareness and interaction. TheMiville-Guzman Universality-Diversity Scale - Short Form (MGUDS-S), for instance, measuresindividuals' awareness and acceptance of similarities
studies evaluated consumer awareness [40] and the impact of consumer information,including environmental benefits, on buyer intentions [36]. After considering vehicle rangeissues, charging stations, consumer information, “the affordability of EVs remain[s] the greatestbarrier [31].” In summary, factors related to EV were found to be battery and chargingtechnology, access and convenience, environmental value, and primarily affordability.Charging Infrastructure Other scholarship has focused more specifically on best practices in building chargingstation infrastructure. These included land use clustering [41], community charging hub modelsfor multi-dwelling EV drivers [42], and supply/demand optimization algorithms [5]. Carlton andSultana’s
Conference on Computer Vision (ICCV), 2961-2969. 2. Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017). Multi-view 3D object detection network for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6526-6534. 3. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (NeurIPS), 91-99. 4. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580-587
toward plant sciences,” CBE Life Sci. Educ., vol. 13, no. 3, pp. 387–396, 2014, doi: 10.1187/cbe.13-12-0231.[2] A.-B. Hunter, S. L. Laursen, and E. Seymour, “Becoming a scientist: The role of undergraduate research in students’ cognitive, personal, and professional development,” Sci. Educ., vol. 91, no. 1, pp. 36–74, 2007, doi: 10.1002/sce.20173.[3] S. E. Brownell et al., “A High-Enrollment Course-Based Undergraduate Research Experience Improves Student Conceptions of Scientific Thinking and Ability to Interpret Data,” CBE—Life Sci. Educ., vol. 14, no. 2, p. ar21, Jun. 2015, doi: 10.1187/cbe.14-05-0092.[4] M. T. Jones, A. E. L. Barlow, and M. Villarejo, “Importance of Undergraduate Research for Minority Persistence and