in this sector including supply chain disruptions, shortage of talentedand skilled workforce, and intense competition from foreign chip manufacturers. According to theSemiconductor Industry Association (SIA), the U.S. share of global semiconductor manufacturinghas dropped significantly, from about 37% in 1990 to approximately 12% in 2023 [1]. In responseto this concerning decline, the U.S. government has initiated programs to increase domesticmanufacturing, such as the CHIPS and Science Act, which aims to boost advanced chipsproduction in the U.S, prompting an urgent need to bolster workforce readiness [2, 3].In this paper, we discuss our efforts and experiences in an industry-sponsored project that aims toaddress this need by preparing ‘fab
engineeringdisciplines.IntroductionThis paper advocates for a paradigm shift in engineering ethics through the lens ofintersectionality, a concept rooted in social science that examines how overlapping socialidentities, such as race, gender, and disability, intersect with systems of power and oppression[1]. The Intersectionality-Informed Ethics Principles (IIEP) framework offers a structuredapproach to integrating these considerations into engineering decision-making, helpingprofessionals address both technical and societal dimensions of their work. For instance, IIEPenables engineers to design sustainable energy systems that prioritize equitable access formarginalized communities while maintaining technical rigor. Through theoretical insights andpractical applications, this
and across time. Although ChatGPT cansuccessfully complete different types of tasks, current models still show errors in logic, factualinformation, arithmetic, grammar, reasoning, coding, and even the model’s own self-awareness[1]. Assessing the performance of these tools is an ongoing task, and one that engineeringstudents, faculty, and industry professionals must engage with when deciding how to use theresponses they get from a GAI tool.This exploratory study aims to showcase student, faculty, and industry perceptions about thecapabilities of GAI to perform various tasks, as well as how they approach testing thisperformance. The methods, results, and discussion sections offer various insights to theengineering education community; the
is usually rare, coming at the midterm and end of a semester in the form ofofficial student course evaluations. This infrequent feedback system does not allow for just-in-time adjustment of teaching style or addressing common points of confusion when it is neededmost. For this reason, some instructors choose to implement “muddiest point” reflections, ametacognitive exercise in which students briefly summarize the most confusing conceptencountered in class each day [1].Students respond positively to such reflections [2], and they may improve student performancewhen used effectively. In one study, muddiest point reflections alone did not improve examperformance, but results did suggest benefits for students whose instructor reviewed
academic experience.Keywords: Mentoring Relationships, Emotional Intelligence, Hidden Curriculum, GraduateStudent Development, Engineering Education1. Introduction: The landscape of engineering education is undergoing a significant transformation, movingbeyond the traditional focus on technical expertise to recognize the critical importance ofinterpersonal dynamics in mentoring relationships [1]. At the heart of this evolution lies thegrowing recognition that successful mentoring in engineering education requires emotionalintelligence, particularly when supporting underserved doctoral students [1]. Emotionalintelligence is a psychological competency that plays a crucial role in helping mentors andmentees navigate the complex hidden curriculum
, recurrent “design seeds” across multiple interviewtranscripts for students to potentially discover. This project may inform industrial engineeringand other faculty who wish to supplement their course design work for students with supportingmaterials using generative AI.IntroductionThe integration of generative artificial intelligence (AI) into industrial engineering educationmarks a transformative shift in pedagogical strategies and the preparation of future engineers.Generative AI, recognized for its capability to generate content such as text, images, and designs,holds substantial promise for enhancing educational experiences [1], [2]. It fosters creativity,enables personalized learning, and supports the resolution of complex problems
a Southeast university in the United States. Clustering revealed four distinctmotivational profiles: Adaptive High Achievers, Competitive Strivers, Mastery-OrientedImprovers, and Low-Performance Avoiders. Findings highlight the critical role of self-efficacyin predicting resilience, with students demonstrating high self-efficacy being nearly three timesmore likely to exhibit resilience. This empirical research paper provides practical insights intofostering academic success and resilience through tailored interventions.IntroductionStudying psychological variables in higher education provides valuable insights into students'cognitive and behavioral development, shaping their adult personality and academic success [1].While many students
identify activities in whicheach cohort positively improved domains in student interests. This approach provides meaningfulinsights for developing more inclusive and impactful STEM education interventions, ultimatelyenhancing the structure and effectiveness of STEM summer camps.Keywords: Electrical and Computer Engineering, Middle School Summer Camp, STEMeducationIntroductionBased on data from the U.S. Bureau of Labor Statistics, the overall demand for engineers isexpected to grow at a faster rate than the average for all occupations from 2023 to 2033 [1]. Asurvey of 90 engineering students at the University of New Haven found that 65% of them haddecided to study engineering by the age of 16 or earlier [2]. Another survey with 500 U.S.college
responsibilities.Keywords: service-based learning, social skills, social responsibility, engineering, communityengagement, professional developmentIntroductionService-Based Learning (SBL) has become a valuable educational approach, especiallywithin higher education in engineering. By combining academic learning with communityservice, SBL offers students the chance to apply their theoretical knowledge in real-worldsituations. This not only helps them acquire technical skills but also fosters the developmentof the social and personal skills needed to tackle the complex challenges of today’s society[1-3]. Recent research highlights that the implementation of SBL projects has proveneffective in enhancing academic performance and student retention, especially in fields
perpetuate caste inequities despite an apparent caste-blind environment. They also explore gender diversity in computing education, particularly addressing the leaky pipeline issue affecting women’s participation in STEM fields. ©American Society for Engineering Education, 2025 “I can’t see race here”: Pragmatic, theoretical, epistemological, and communicativechallenges researchers and instructors have with observing race in engineering classrooms1. Introduction:Engineering has historical origins in white supremacy, patriarchy, and classism [1], [2], [3].Despite efforts to diversify the profession, these systems of power and inequity have largely beenperpetuated. While many research efforts document the
outcome, innovative and non-traditionallabs were developed with a focus on solid mechanics where hands-on experiments help bridgethe gap between theory, numerical analysis, simulations and real-world applications. Thetraditional lab exercises at majority of undergraduate engineering colleges (including ours)include compression, tension (flat and threaded), double shear, and torsion (circular and non-circular specimens). In this paper we have identified 6 different labs 1) Stress ConcentrationAnalysis Around a Circular Hole, 2) Testing of Riveted Connections, 3) Beam Deflection, 4)Tensile Testing at Extreme Temperatures, 5) Buckling of Slender Columns and 6) Thermal Stressin Bimetallic Strips to assess SO6. The assessment data from Testing of
water, access to electricity,limited connectivity, and low quality of education. Over 700 million people lack access to electricity [1],over 2.2 billion people lack access to clean water, and over 2.6 billion do not have access to the Internet.To address these concerns, the United Nations established a set of goals, the UN SustainableDevelopment Goals [2] which were agreed upon by most of the world’s nations in 2015 following thelimited success in achieving the prior Millenium Development Goals. The field of humanitarianengineering (HE) has emerged as a means of educating students to participate in projects to addressthese global challenges. However, most students participating in HE projects tend to be civil ormechanical engineering students
. Standardized tensile testing is performed to evaluate the mechanicalproperties of the printed components. The results highlight the effect of processing conditions onthe mechanical properties of the TPMS composites as well as its potential advantages andsuitability for applications in various industries.1.0 Introduction Nature's design solutions, honed over billions of years of evolution, have given rise to amyriad of remarkable features such as hierarchical structures, lightweight composites, self-healing mechanisms, and optimal geometries [1-3]. These features not only ensure exceptionalmechanical properties to living organisms but also ensure energy efficiency and resilience in theface of environmental challenges [4-8]. The integration of
students to be solved in class. This was followed by the instructor going overthe third problem in class with the students. Student groups were required to submit thesolution to the third problem by the end of the day of class. Early formative feedbackavailable based on student performance on the third problem was used to modify futurelecture content. The intervention resulted in better grades for students and better teachingevaluations for the instructor as compared to a similar offering of the course in theprevious semester. Therefore, it is highly recommended. 1. IntroductionThe course redesign approach of this project focuses on active learning using in-class groupworkand formative feedback. In a challenging course, it is important that
upon a previous offering from 2018within Texas A&M University’sCollege of Engineering (COE) targeting juniors and seniors. The curriculum was purposefullydesigned to include experiential learning criteria [1, 2, 3, 4] as well as metacognitive educationalstrategies [5, 6, 7, 8, 9] that have been long proven to offer enhanced academic experiences forundergraduate engineering students. This course targeted second-year students, offering a semi-rigorous, two (2) credit-hour course to prepare students for upper-division coursework and industryinternships. Often, in engineering education, second-year students aren’t deliberately targeted andare vulnerable to falling through retention gaps [10, 11 12, 13, 14, 15]. Therefore, targeting second-year
©American Society for Engineering Education, 2025 International Coral Reef Research Experiences for Community College StudentsIntroductionCommunity colleges are evolving from their traditional roles of providing a two-year experienceor a technical education into institutions capable of offering not just associate degrees, but careerprograms, professional and continuing education, language, and equivalency programs andbeyond [1], [2], responding to the changing needs of communities and their economies.However, research practices are not inherent to the community college model and are rarelyincluded as a component in student training or capstone experiences. Additionally, coral reefscience is considered an
CS education. We recommend educatorsguide students in leveraging custom, context-specific assistants to improve learning and developcritical AI application skills.IntroductionLarge Language Models (LLMs) enable educational platforms to support students throughadvanced tools with real-time personalized feedback, guidance, and engagement mechanisms.By employing methods like retrieval-augmented generation (RAG), LLMs are increasingly ableto overcome challenges related to scalability and handling unexpected or unforeseen inputs, asare often experienced with intent-based chatbots [1]. RAG-powered assistants demonstratesignificantly improved performance in terms of response accuracy, adaptability, and studentsatisfaction [2].This study examines
. ©American Society for Engineering Education, 2025 Lessons Learned: 35 Years of Impact of the Leonhard Center for the IntroductionThis Lessons Learned paper presents the 35-year history of The Leonhard Center for the Enhancement ofEngineering Education [1]. The Leonhard Center is a teaching and learning center dedicated to theenhancement of engineering education within the College of Engineering at Penn State University.Established in 1990 by an alumnus of the university, William and Wyllis Leonhard, the Leonhard Center hasthe mission of catalyzing and supporting the enhancement of teaching, learning, and assessment at Penn StateUniversity to deliver world-class engineering education [1]. The Leonhard Center was the first of its kind tobe housed
support the United States inremaining a strong economic and global competitor [1-3]. However, through analysis of nationaldata sets, approximately only half of the students who enter a STEM major will graduate with aSTEM degree [4].Recent research examining the reasons why students leave STEM disciplines show that theytypically leave for non-technical reasons including poor teaching, curriculum overload, limitedadvising and support, or a rejection of the competitive culture in many STEM disciplines [7-10].In more recent years, studies have continued to document the same factors influencing attritionin STEM degrees as well as student’s lack of self-efficacy, failure of the material to capturestudent interest, overly competitive grade structures
is more active. Phrases such as instructors “delivering a lecture,” courses “contain”or “cover” content, and students “grasping a concept” all point to thinking about learning asgaining an object. As Sfard [1] and others observe, we can also view learning as participating ina practice. This metaphor aligns with engineering mindsets, wherein we often care less aboutwhat students know, and more about what they can do with their knowledge. This shift inmetaphor suggests that multiple approaches to learning may be needed for different subjects.How do other common metaphors, such as learning as lighting a fire and planting a garden,influence how we teach? Drawing on Ingold’s anthropology of lines [2], we outline howmetaphors such as learning as
built into the policy. Thispaper provides a review of how five states are evaluating their teacher capacity to offer computerscience, including their calculations and the opportunities and limitations associated with theapproaches. The ultimate goal of this work is to provide robust and flexible guidance to otherstates to ensure that any policy is well planned and supported to promote equitableimplementation.IntroductionAs states increasingly recognize computer science (CS) as essential for preparing students for thedigital future, the push to make CS a graduation requirement has unveiled a significantchallenge: the shortage of qualified CS teachers, especially in high schools. Teacher shortagesare a universal problem [1], [2], [3] and are
Figure 1: Loginchanged, the solutions are well known online. The students simply follow the solution manualformulas and swap in their random values. These platforms are also inflexible for the instructors,they are unable to modify the problems, only select which are to be used. Finally, and perhapsmost importantly, they are very expensive. A subscription to one of these services usually costsmore than $100 per semester per student which can be prohibitive, especially if several classes areusing them in the same semester. The current trend in education is toward Open EducationalResources (OER) to make education affordable for everyone. This homework platform and anaccompanying OER Microelectronics textbook allows our students to study
constraints are often immov-able. The paper concludes by suggesting future directions for constraint-driven embedded systemsprojects, emphasizing the potential of this method to continually create novel, challenging learningexperiences in the face of rapidly evolving technology.1 IntroductionEmbedded systems education often struggles to balance theoretical knowledge with practical, en-gaging projects. While microcontroller-based projects are common, they frequently lack the scaleand complexity that mirror real-world engineering challenges. Additionally, with the success ofMaker Spaces and the popularity of many of these projects, finding interesting projects that havenot already been covered deeply on the web is difficult. This paper proposes an
, attached to a cantilevered frame secured to a 135’ LMS irrigation pipe. In this paper, we present asummary of the students’ approach to managing expectations via detailed calculations, modeling, andscaled prototypes for a community partner whose vision included reliance on future infrastructure to beused in a novel and unexpected way.IntroductionCommunity engaged learning (or service-learning) enhances student education by linking theory topractice and classrooms to communities [1][2]. Partnering with community organizations contextualizesengineering, broadens perspectives on who engineers can be and serve, and supports diverse studentretention, particularly for those motivated to create impact [3].Well-structured service-learning fosters deep
have someversion of a generative AI chatbot to interact with their clients that are available 24 hours a day,7 days a week, and 365 days a year.Artificial intelligence can be defined from the Merriam-Webster Dictionary as “the capability ofcomputer systems or algorithms to imitate intelligent human behavior” and generative AI can bedefined as a type of AI technology that generates content such as text, images, audio, and video.A chatbot is a computer program that uses a large language model (LLM) to simulate aconversation with human users, typically through text [1]. Therefore, a generative AI chatbot isa type of artificial intelligence system designed to engage in human-like conversations bygenerating text-based responses dynamically rather
and experiences in a specific direction, butthey also help people quickly identify what skills and courses students are expected to have for aspecific engineering major. While most modern engineering work is done through trans-disciplinary teams whose skills may overlap [1], students are still expected to choose a specificmajor that often connects them to a specific engineering department, coursework, andrequirements. Because of how much this decision can shape a student’s experiences in college,students often seek help and advice in finalizing this decision. There exists a collection ofresearch that examine how students select engineering and an engineering major (e.g., [2], [3]),which has helped develop exploration activities for students
approach and learning style of a textbook, and regenerateproblems algorithmically to give students unlimited opportunity for practice and mastery [1].Similarly, ALEKS by McGraw Hill is another digital platform that allows instructors to buildassessments and track student performance. However, the key difference between the two is thatALEKS uses an adaptive learning approach, requiring students to demonstrate mastery of a topicbefore progressing to the next. ALEKS uses machine learning rooted in Knowledge SpaceTheory to create and continually update a detailed map of each student's knowledge. It identifies,in real time, whether a student has mastered a specific topic and if they are ready to learn it. Thisapproach is to keep students engaged
sustainability into engineeringeducation. A new course is recommended to prepare engineering students for the globalized field,covering cultural, ethical, and practical aspects of global engineering.IntroductionGlobal education serves as a formidable catalyst in shaping the trajectory of a sustainable futurefor our planet. This report meticulously examines the multifaceted ways in which global educationinitiatives play an instrumental role in cultivating environmental consciousness, instilling socialresponsibility, and fostering cultural awareness. The narrative underscores the harmoniousintersections between these initiatives and two foundational frameworks: The United NationsSustainable Development Goals (SDGs – Appendix 1) and the Grand Challenges
. ©American Society for Engineering Education, 2025 Democratizing the Analysis of Unprompted Student Questions Using Open-Source Large Language ModelsAnalyzing student questions can help instructors make informed pedagogical improvements byproviding a better understanding of student thinking. In past literature, the analysis of studentquestions (SQs) has primarily been conducted using taxonomic categorization [1]. Thesetaxonomies focus on various aspects of learning. For instance, utilizing taxonomies based onBloom’s taxonomy [2] can reveal what cognitive levels students are utilizing or struggling with.On the other hand, the taxonomy proposed by Scardamalia and Bereiter in 1992 [3] can be usedto determine how familiar
complementary direct-write nanolithography process that utilizes thermalscanning probe lithography (t-SPL) to generate nanopatterns [3-6]. Table 1 compares thevarious nanolithography techniques and highlights the advantages (+) and disadvantages (-)for each technique. From Table 1, it shows that there is not a “perfect” nanolithographytechnique for educational purposes, but that t-SPL is the leader in being able to seenanopatterning in real time and in a cost-conscience manner but at the expense of not beingindustrially relevant. Focused-Ion Thermal Electron Beam Maskless Layer Parameter Beam