changes toward AI tools (Q5). Responses werecontexts. By doing so, it aims to contribute to the growing body anonymized to encourage candid reflections. The these trends: DB students emphasized technical automation, question used are: “Debugging code faster with AI,” whereas EEM students noted challenges in domain-specific applications: “AI can’t solve Q1. What do you hope to learn from the prompt problems itself.” engineering unit in this course? Q2. What challenges do you anticipate in learning prompt engineering? Q3. How do you think prompt
speaking and verbal communica-success across various industries, including engineering. The tion skills. At Maastricht University, students utilized Virtu-eight NACE competencies are: alSpeech, an AI-powered virtual reality platform, to practice public speaking in immersive, real-world scenarios[20]. The • Career and Self-Development: Engaging in continuous platform provided students with simulated audience interac- learning, self-reflection, and professional growth. tions, real-time feedback on
application and explaining your reasoning. 5. Reflect on the complexities of material selection, particularly when balancing engineering requirements with environmental considerations and cost.Figure 1 shows examples of student generated Ashby Charts from two different teams. The ploton the left shows cost versus density for six material types as generated by Team A, and the ploton the right shows GWP versus density for specific materials as generated by Team B. Theproject was open ended allowing for variations in how data was presented.Figure 1. Examples of Ashby Charts generated by Team A and Team B. On the left is depictedcost versus density and on the right is depicted GWP versus density.Survey InstrumentA survey was designed as a
subset of statements to gather the desired information. The rationale behind thealignment of the original factors with our themes and the selection of the most relevant statementsfor each theme is detailed in the following subsections. 1. Mapping Satisfaction to Learning Environment Satisfaction was defined in the original study as the “outcome of an experience” 12 . Since the experiences in both studies refer to education, we identified a subset of satisfaction-related statements that could be mapped to our research focus on learning environment. These statements were deemed most reflective of student perceptions regarding how AI-supported learning impacts their learning experience. The selected statements were
, participatorySimultaneously, data breaches, algorithm-driven content citizens in a democracy [5], [6]. Their ideas continue tomanipulation, and persistent security risks expose people to influence modern approaches to reflective, analytical thinking.continuous vulnerabilities. Yet, most individuals lack theknowledge and skills to recognize these dangers, let alone C. The Cognitive Science Revolution: Understandingmitigate them effectively. Thinking Errors Critical thinking, digital literacy, and cybersecurity While early philosophers focused on principles of soundawareness are vital defenses against manipulation, reasoning, modern cognitive
. ©American Society for Engineering Education, 2025 Development of a measure of intersectional socioeconomic inequality that extends beyond incomeAbstractIn this research paper, we describe our initial development of a more holistic socioeconomicinequality measure, the Model of Intersectional Socioeconomic Inequality. Our development ofthis model is in response to the urgent need for a more comprehensive understanding of inequalitythat goes beyond income disparities. Traditional socioeconomic measures do not reflect therealities of inequality. Particularly, they do not recognize the complex sociological processes thatimpact low-income students and their access to resources necessary to be successful in STEM.Thus, a
recognitioncommensurate with such achievements and contributions [1], [2]. However, this belief is oftenoverly idealized and may not always reflect the complexities of reality, as it fails to fully accountfor the barriers, biases, and inequalities that impact who succeeds and how recognition isdistributed. [3], [4], [5]. For many, in particular women and underrepresented and minoritized(URM) students, the STEM space—the early stages of pursuing an engineering degree or later intheir professional careers—frequently experience overt sexism, gender bias, racism,discrimination, stereotyping, and isolation [4], [6], [7].National concern and acknowledgment of barriers faced by women in STEM is longstanding andwell-documented [1], [3], [8], [9]. According to the
students who are already spending way too much time outside of the classroom and the lecture hall studying to give up even more of that little bit of time off. SteveAll three quotes reflected the academic environment that students experience in engineering atthis particular institution and suggest that institutions rarely recognize or address issues likecommunication gaps and the misalignment of students’ expectations around learning methods andgoals.A second aspect has to do with the use and implementation of technology in courses. Twostudents noted how for some departments and students adding technological innovation can bedifficult. On the one hand, a graduate student in Computer Science said, I could say with other departments
alsoaware of the need to critically reflect on their own teaching practices, motivated by the desire tobe change agents with respect to structural and societal issues within engineering, which areconcerns for the participants. However, addressing structural inequities in the engineeringcurriculum requires further development of their understanding of how to integrate criticalconsciousness into their teaching. Three categories of themes resulted from the analysis of thefaculty’s motivations and alignment with CRP: (1) Promoting Students’ Academic andProfessional Success through Equitable Teaching, (2) Fostering Cultural Awareness throughInclusive Pedagogy, and (3) Developing Critical Consciousness for Addressing Societal Impactin Engineering
mentoring. She serves as an instructor for core first-year engineering courses such as E101: Introduction to Engineering & Problem Solving and E102: Engineering for the 21st Century. Her commitment extends to undergraduate and graduate-level research courses, where she fosters an environment of innovation and discovery. She established the study abroad program for E101 for Quito, Ecuador for Spring 2024 and is now the program director for the study abroad program for E101 for Prague, Czech for Spring 2025. Dr. Qaqish’s academic journey reflects her dedication to learning and excellence. She earned her Bachelor of Science in Biomedical Engineering from Boston University, followed by a Master of Science in
strongly agree (5). To mitigate response bias, theoriginal instruments contained some items that were worded such that the responses had to bereverse coded; we retained that wording.The scales were developed for K-12 education; therefore, we edited some of the terms to makethem applicable to higher education (e.g., faculty instead of teachers, institution instead ofschool). Because we were interested in STEM education, we also modified some of the languageso it was specific to STEM instead of using general references. For example, an originalprofessional beliefs item was: “Historically, education has been monocultural, reflecting onlyone reality and has been biased toward the dominant (European) Group” (Pohan & Aguilar2001). We reworded it as
Session XXXX Examining Student Usage/Access Statistics from two Canvas LMS courses: Undergraduate and Graduate Tariq Khraishi Mechanical Engineering Department University of New Mexico AbstractThe author has been utilizing Canvas LMS (Learning Management System) for either asynchronousundergraduate course teaching or as an online presence to communicate many aspects of an in-person graduate course. In this paper, the author reflects back on student usage or access statistics inthese two courses to derive from them some interesting data or numbers. The pulled-out numbers
period of transition, understanding the past, present, and future of itsenergy sector becomes crucial. This paper explores the history and current state of WestVirginia’s power systems industry, addressing challenges and opportunities in resources,generation, transmission, and distribution. The historical and ongoing evolution of WestVirginia's power systems further reflects broader global trends in energy development andtransition, as seen in studies from Nigeria [1] and South Africa [2] on their power supplyevolutions. Within this paper, the power systems industry is considered as everything that rangesfrom energy to electrification; thus, natural resources, energy sources, electricity generation, andfuture plans and trends are of interest
,equipping students with the skills necessary to meet industry demands while addressingworkforce shortages. These efforts reflect recent national funding initiatives, such as the TexasChips Initiative, and the push to expand manufacturing and semiconductor businesses, whichhave created opportunities for more active collaboration between universities and industries. University-Industry Collaboration ModelEvery university has its unique strengths and weaknesses, along with varying conditions.LeTourneau University possesses a distinctive legacy and practice with its excellent hands-onengineering program. However, as a four-year college, it faces challenges due to the lack ofextensive infrastructure often required for
personalized project work that AI cannot easily replicate – toalternate explanation for a complex circuit analysis problem, ensure that grades truly reflect student learning.offer real-time feedback, and even generate custom practicequestions mimicking one-on-one tutoring [5]. Advanced Additionally, tools for AI detection are emerging, but theirmultimodal Gen AI models process and generate images and accuracy is uncertain, and they raise ethical questions (e.g.,animations to support diverse learning styles. This multimodal false accusations or invasion of privacy if student submissionsare sent to third-party detectors). Therefore, the consensus in necessary. If a tool is to be used responsibly
reflective of the diverseinternational student enrollment with economic and policy pool of applicants, consisting of 31.2% from Connecticut,influences at a Midwestern U.S. university using the Seasonal 35% from other United States states, and 33.7% international.Autoregressive Integrated Moving Average (SARIMA) model. In order to maintain student privacy, all institutional recordsThe study discovered that tuition increases had a relatively were anonymized, or all personally identifiable informationlow impact on international student enrollment, suggesting was deleted. The dataset was also audited for regional bias,that factors such as academic reputation and career prospects and no statistically
and focus group interviews to capturethe students' experiences in more depth. These interviews explored students' perspectives onhow their mindset and grit evolved throughout the program, including their challenges,motivations, and reflections on perseverance. This allowed for a deeper understanding ofhow students perceived their own growth in grit and mindset over time. Students sharedstories of overcoming personal and academic obstacles. Qualitative methods also revealedfactors influencing passion and perseverance, including faculty support, project involvement,and personal academic goals.In conclusion, these findings underscore the role that grit and mindset play in shapingstudents' attitudes toward their degree programs and suggest areas
, studentsD. Post-Course Survey are required to document and acknowledge their AI At the end of the semester, students completed a follow-up tool usage in all relevant coursework, fostering criticalsurvey to assess the impact of AI integration on their learning reflection on AI’s role in academic work.experience. This survey provided insights into how students’ 2) Prompt Engineeringfamiliarity with and attitudes toward AI tools evolved over This dimension introduces students to prompt engineer-the course. Students were asked to reflect on their ability to ing—the practice of crafting precise inputs to optimizeuse AI tools effectively in academic
, adaptive AI difficulty levels, and competition createdistinguishing between player and AI moves. This method an engaging experience that improves curiosity and deeperprovided a detailed dataset for evaluating player strategies and cognitive processing [6]. One example of this approach is Tic-decision-making patterns. While we observed the gameplay Tac-Toe, a simple yet strategic game that reflects fundamentalperformance of all participants across different grade levels, our AI decision-making processes [7]. The game provides adetailed strategy analysis focused specifically on first-grade structured environment where students can observe how AIstudents due to the availability of screen recording data
accessibility of mentor interactions, specificcontributions of mentorship to their academic success and faced challenges. Intervieweeswere encouraged to provide specific examples and describe the most valuable qualities intheir mentorship. The last theme focused on how the GEES program contributed to students’career readiness. Interviewees were asked to reflect on specific courses or programs that thegreatest impact on their career preparedness and to discuss other career guidance theyreceived during their studies. Results and FindingsSurvey ResultsDemographicsThis study included 27 GEES program students who completed the pre-survey and 23 whocompleted the post-survey. Regarding gender distribution, the pre-survey
values of the quiz. The natural distribution also reflects this. Thestatistical Z-test was performed where the p-value obtained is very small, less than the commonsignificance level of 0.05, indicating a statistically significant difference between the Baseline andStudy groups. This suggests that the intervention in the Study group had a significant effect Proceedings of the 2025 ASEE Gulf-Southwest Annual Conference The University of Texas at Arlington, Arlington, TX Copyright 2025, American Society for Engineering Education 5compared to the Baseline group. Figure 4. Histogram Comparisons of
Teaching SustainabilityEmbedding sustainability within construction education requires a diverse range of pedagogicalapproaches that actively engage students and emphasize practical, real-world applications. Efforts toembed sustainability within construction courses often utilize active and experiential learningstrategies, reflecting the idea that real-world applications better enable students to internalize andretain environmental concepts (Abraham, 2020). Project-based learning (PBL), a student-centeredpedagogy that emphasizes active, real-world problem-solving, engages students in interdisciplinarychallenges like designing net-zero energy buildings or retrofitting structures, fostering collaboration Proceedings of the 2025
PBL enhancesproblem-solving skills and promotes critical thinking by requiring students to design andimplement solutions to complex tasks [10].Peer review is an invaluable method for fostering critical evaluation and collaborative learning. Itencourages students to assess the quality of others’ work while reflecting on their own [11] [12][13]. This process helps students refine their analytical skills and improves their understanding ofhow to evaluate statistical results and experiment designs. Research demonstrates that peer reviewis a useful learning tool [14] and also strengthens students’ writing and oral presentation skills[15]-[19], which are essential for engineers to communicate their findings effectively.Incorporating peer review in
, and task complexity, reflecting strategies proposed by [21], [22], [23] and [24] inmulti-layer AI control frameworks.For AI models that must share parameter updates—such as robot learning strategies, anomalydetection patterns, or domain-specific heuristics—we adopt a bidirectional exchange between local(ϕℓ ) and cloud (ϕc ) networks: ϕℓ (t + ∆t) = α ϕℓ (t) + (1 − α) ϕc (t), N h X i ηc (n) ϕc (t + ∆t) = ϕc (t) + N ϕℓ (t) − ϕc (t
- and post-course surveys will be explored to better understand innovative practices that help strengthenundergraduate students’ acclimation, advancement, and commitment in engineering pathwaysrelated to engineering and aerospace related fields. Findings show how peer mentoring andcollaborative team learning have potential to increase the success and engineering careeraffiliation for non-traditional groups, specifically Veterans, active military and adult studentlearners in engineering. Peer leaders were identified from previous courses and shared similarcharacteristics as the adult learner, Veteran and active military student population that wascurrently enrolled in the course. Through a qualitative approach, the aggregated reflections
74 20683.80 279.51 6 58 4082.33 70.38Participants and Officers Perspectives on the Learning OutcomesDuring the development of the model for the engineering design competitions, the officersdeemed it necessary to outline what they envisioned the participants learning from thecompetitions. Furthermore, the officers decided to refrain from explicitly stating these learninggoals to the participants, but they did allude to goals using the rulesets and objectives given.After the competition, the officers always held an open discussion among the teams to reflect andshare their experiences, challenges, growths, and key takeaways. ESG officers
in Higher Education, vol. 32, No. 2, April 2007, pp. 159–181[6] M. J. Ford and H. Dillon, “A secure, Scalable Approach to Student-Graded Homework for Self-Reflection”, 2024 ASEE Annual Conference & Exposition, Portland, OR, USA, June 23-26, 2024[7] N. M. Edwards, “Student Self-Grading in Social Statistics,” College Teaching, vol, 55, no. 2, August 2010 [Online]. Available: https://doi.org/10.3200/CTCH.55.2.72-76[8] V. Cherepinsky, “Self-Reflective Grading: Getting Students to Learn from their Mis- takes,” PRIMUS, vol. 21, no 3, April 2011. [Online]. Available: https://doi.org/10.1080/10511970903147861[9] P. M. Sadler and E. Good, “The Impact of Self- and Peer-Grading on Student Learning
supervisors in the VM setup exist on thesame machine and the communication delay between them is little compared to having twodifferent Raspberry Pi’s to communicate. This communication and processing delay add up tothe latencies and we can see these reflections in Figure 3a and Figure 3b. a) Virtual Machine (VM) b) Raspberry Pi Testbed Figure 3 Workload Distribution ComparisonConclusionFrom the overall experience of iEDGE, we conclude that such an initiative for engaging next-generation engineers and scientists in hands-on implementation plays a pivotal role in bridgingthe disconnect between theoretical knowledge and practical application. Our REU student
, and antifouling. Each experiment wasconducted independently, with no direct integration between them, allowing students to focus onspecific skills and techniques relevant to each topic, without an overarching project orconnection between the various lab activities. This method reflected a more traditional laboratoryteaching style, where each experiment serves to reinforce a specific set of theoretical concepts.In contrast, the Spring 2025 semester will implement a project-centered approach, wherestudents will be assigned a semester-long biomedical engineering project. This project willrequire students to conduct a series of interconnected tests on a biomedical device to determineits efficacy, simulating real-world engineering challenges. The
professional practice.• We emphasize the importance of understanding and maintaining ethical standards in every aspect of their work, helping students to navigate complex situations with integrity and accountability.• By embedding these ethical considerations into our teaching, we prepare students to make informed, responsible choices that reflect their commitment to professionalism and societal impact. Why Inclusive Belonging for Excellence Matters in an Education Setting• Goes beyond merely providing access to education.• Involves actively reducing barriers to understanding the material presented.• To achieve this, it's essential to consider the unique and varied lived experiences of students. • For example