interests.Yargo Teixeira Gomes de Melo, York College of Pennsylvania ©American Society for Engineering Education, 2025 Reflections on Artificial Intelligence use in Engineering CoursesChatGPT was launched on November 30, 2022, by the San Francisco-based artificial intelligence(AI) provider, OpenAI. Within a year, this tool has been widely adopted for tasks such as writingpapers, solving engineering problems, programming, and much more. This paper explores thegrowing use of AI by college students and faculty. By embracing OpenAI and similar tools, weaim to demonstrate how these technologies can be used effectively and ethically. We specificallyexamine how AI has been
Paper ID #48292WIP: Integrating Human Rights Frameworks and Reflective Learning intoEngineering Senior DesignDr. Jorge Paricio Garcia, University of Connecticut Dr. Jorge Paricio is an Associate Professor-in-Residence in Industrial Design, at the Mechanical Engineering Department at the University of Connecticut. He received his Bachelor’s degree from the Complutense University of Madrid. He also holds a Master’s Degree in Industrial Design from Pratt Institute and a second Master’s in Human Resources Management from Johnson &Wales University. He holds a PhD from the Complutense University in Madrid, Spain, with a
Framework: The INCLUDE ApproachThe INCLUDE Framework (Innovation, Needs-driven design, Collaboration, Learning throughEmpathy, User-centered solutions, and Diversity-driven Education) offers a transformativeapproach to integrating intellectual disabilities into engineering education. It emphasizes threeinterconnected pillars: Multidisciplinary Collaboration, where diverse teams of engineers,healthcare professionals, sociologists, and disability advocates co-create holisticsolutions; Empathy-Driven Learning, which fosters understanding through immersiveexperiences, user engagement, and reflective practices; and Innovative Assessment Tools, whichevaluate technical feasibility, collaboration, empathy, and social impact using metrics likeempathy
identifying ‘humans’ that may be impacted by research. This is especially a challenge for research projects involving technology. Indeed, many undergraduate research projects at WPI, a predominantly- STEM institution, involve the design, development, test, implementation, analysis, and use of technologies. Techno-centrism (at the expense of a human-centric approach to technology) is not unique to undergraduate research projects. Many of the ethical problems with current commercialized technologies such as facial recognition systems are indeed a reflection of the widespread techno-centrism in the tech industry (Morozov, 2013; Sims, 2017). In such a techno-centric framing, either humans become means to
1 University of MichiganAbstractThe University of Michigan Robotics program focuses on robotics as an embodied intelligence,where robots must sense, reason, act, and work with people to improve quality of life andproductivity equitably across society. ROB 204 is an introductory course for robotics majors thatprovides a foundation for designing robotic systems to address a user need with a sociotechnicalcontext. The course combines lectures, labs, and discussions to teach and reinforce learningobjectives in an equitable and experiential manner. In this paper, we present the lab procedures,required materials, and reflections that operationalize concepts from lecture. Labs collectivelyinclude hardware
opportunities fora diverse group of undergraduate research assistants; and 2) To develop a novel adaptive real-timeoptical sensing algorithms in near-Ultraviolet (UV) spectrum by combining reflected-UV and UVfluorescence techniques to transform our ability to detect biological surface contaminants, such assaliva, that could potentially contain infectious pathogens. The reflected-UV and UV fluorescenceimaging methods are used in various scientific, industrial, and medical optical sensing systems,such as in germicidal irradiation (disinfecting), digital forensics, food/agricultural industries,remote sensing, space science (NASA Perseverance), etc. The recent use of UV light surfacedisinfection mobile robot platforms and devices has shown promising
department’s recentlyapproved mission statement obligates themselves to “educate human-centered engineers anddiscover new knowledge in service of the common good” [2]. In addition, the program’seducational objectives include supporting their graduates’ ability to be “discerning about the roleof engineering in society and critically reflect on their contributions to society professionally andpersonally” [2]. There are several aspects of Boston College's Human Centered Engineering program that arefoundationally new or at least notably uncommon: • centering an entire undergraduate program on the concept of human centered engineering; • embracing practices of reflection; • purposefully integrating much of the curriculum across
academic and professional skills. End-of-semester student reflections written by 80 students were qualitatively coded inATLAS.ti to understand the student experience in the course. Categorization of emergent themeswas guided by Self-Determination Theory, which consists of basic needs around autonomy,relatedness, competence, and motivation. Self-Determination Theory helped to guide findingsemerging from the data which included new soft and technical skills acquired by students,challenges faced, and growth throughout the course. Students described learning skills bothindependently and from peers, by persevering through challenges, and by working on real-worldprojects.Keywords: Interdisciplinary, project-based learning, vertically integrated
: Expanding STEM opportunities for females is crucial not only to address longstanding gender disparities in science, technology, engineering and math (STEM) fields, but also to unlock a broader spectrum of innovation, foster inclusive economic growth, and ensure that future advancements reflect the diverse needs of society. Systemic barriers contributing to the gender gap in STEM disciplines are deeply embedded in societal norms, institutional structures, and cultural expectations, particularly for people of color. Race tends to amplify the gender gap in STEM for women of color by layering unique stereotypes, cultural expec- tations, and systemic exclusions. Addressing the gender
equitable model to STEM education.1. The InSciTE model1A. The InSciTE mission, values and program objectives:InSciTE was created in Spring 2022 from a coalition of 14 faculty from all 9 academicdepartments at CSE to form a truly multidisciplinary council. The council is formed by facultywith diverse ethnic and intersectional identities, a reflection of the authentic partnership andleadership of faculty impacted by enduring inequities in STEM and of the ethos of InSciTE. Thecouncil guided the creation of the program with all decisions reached through consensusbuilding. The council defined the mission of the program as “to create an equitable student-driven environment for undergraduate students to develop skills on interdisciplinarycommunication
, University of Minnesota, with atotal of 19 students providing responses, reflecting approximately 15% of the current active cohort.The respondents included individuals at various stages of the program, such as first-year students,second-year students, and those in their final semester who are completing a thesis or capstoneproject. This convenience sample aims to illustrate the current experiences of students usingGenerative AI tools within this specific graduate program.Data Analysis: Quantitative responses were aggregated and summarized as percentages or meanratings. We collected statistics on the frequency of tool usage, such as how often students useGenerative AI (GenAI) for academic tasks, and noted specific concerns, like the percentage
document are those of the authors and do not reflect thepolicy or position of the U.S. Naval Academy, Department of the Navy, the Department ofDefense, or the U.S. Government.IntroductionAs the oldest of the 6 undergraduate Robotics Engineering degree programs in the United States,we reflect on national trends and program-level lessons learned since we modernized ourcurriculum a decade ago. After a brief overview of our program, we discuss changes in therobotics education landscape over the last ten years, including the proliferation of degreeprograms, issues in accreditation, challenges in hiring, the expectations of students andadministrative challenges. Some of the content is based on our own program observations andassessments, other data
abilities(reflected as most and least effective) that will affect the progress of the facilities that they workin. Additionally, they have favorite foods; feeding them something other than their favorite willdecrease their hunger but won't completely satiate them. Reflecting complexities of humanbehavior, these diverse preferences, being randomly assigned, can sometimes appearcontradictory, such as when a villager's favorite job is the one they are least effective at. Ofcourse, all of these preferences can change periodically. Players need to carefully monitor thesevillagers and their preferences because unhappy villagers are likely to leave the village, makingit more difficult to achieve the goals of the game before the end of the timeframe.Figure
student cohorts now included undergraduates from Indonesia, Europe, andSouth America and from other Asian countries such as Thailand, China, and Taiwan,reflecting a more diverse and globally representative student body. The breakdown ofparticipants’ nationalities in each module was as follows: For non-COIL STEM modules, thestudent body at the Indonesian university consisted solely of Indonesian undergraduates,whereas participants in the STEM COILs were a mixture of ‘international’ students in Japan(attending in person) and Indonesian undergraduates attending online. Participants in thehistory modules (both COIL and non-COIL) were mainly from Europe and South America,partially from Asian countries such as Thailand, China and Taiwan attending from
program was used,with mentors following a specific form for each meeting.The mentoring sessions involved:Meeting 1: Obstacles to Success and Opportunities for SuccessThis session involved a discussion on what went wrong during the first semester and whatopportunities are available for success. It provided an opportunity for the mentor and mentee tomeet each other and reflect on the previous semester, highlighting both achievements and missedopportunities.Table 1: Obstacles to success. Students were asked to complete this section by choosing the top5 obstacles (number them in order from most important to least important). Study Habits Finding a good place to Going to class study
as it coincided with the removal of the SI sessions, which had beena key component of their academic support during their first year. However, it remained unclearwhether these challenges were primarily due to the absence of SI or if they reflected the typicalstruggles students face when transitioning into more advanced coursework and the increasedacademic demands of their second year. To determine if the decrease in performance was uniqueto the SSP students, their quarterly GPA was compared to other engineering students withsimilar academic progress over the first year.First and Second Year Engineering at Louisiana Tech UniversityThe students in this study completed their first and second years of engineering at LouisianaTech University
studentsformulated cohesive solutions that integrated multiple ROS2 packages. By the time they reachedthis final assignment, most learners had developed a solid framework of fundamentalcompetencies that could be extended to their final, open-ended projects.Rationale for Key ChangesFrom the outset, the lab sequence was devised with progressive complexity in mind, graduallylayering new tools and concepts to reduce cognitive overload. This scaffolded approach helpedstudents steadily build confidence, ensuring each new skill—such as command-line proficiency orROS2 control—was reinforced before introducing more demanding tasks. Additionally, hands-onintegration with simulated environments and (for some students) real hardware reflected howrobotics is typically
, theyoften reflect the focus of those departments. For example, WPI’s program is heavily orientedtoward computer science, whereas the program at Lawrence Technological Universityemphasizes mechanics. Our RE program was set to be managed at the faculty level to bridge theME and EECE departments, facilitating the allocation of resources for implementing the newprogram.Table 1. Undergraduate RE programs around the world !"#$%CD#(#%D )*+"(C, -%./C(0%"( !"1%C2C/1+/(%P4C*2C/0 !"#A%CD%#()"*+D%AIJKA(LJCDKDMD%(1!)LO( PJKD%4(RDSD%C TUTU TUU9 PJKW%#CKD+("X( PJKD%4(RDSD%C TU?? @SA#%JA%(B%AIJ"*"CKAS*(PJKW%#CKD+ PJKD%4(RDSD%C TU?? PJKW
provide support and resources to develop these skills. The goal of thisintegration is to ensure graduates go into industry or graduate programs equipped tocommunicate effectively with the ability to work on teams to support projects and solveproblems. The need for these skills is reflected in ABET Outcomes and by what employersindicate graduates need to succeed [1-3]. Despite these efforts, employers indicate that recentgraduates may not possess the needed skills to communicate and collaborate effectively [4-5].Furthermore, graduates may struggle to transition from academic to workplace settings. Thesechallenges demonstrate the need to examine how engineering educators support professionalskill development, understand the factors that influence
, students answered several reflection questions about theirexperience in the course, as part of a required course assignment. Three of these questions wereselected for analysis: (1) What is one piece of advice you would give to student teams taking thisclass in the future? Why?; (2) What was the most difficult, challenging, or demanding thingabout ENGL XXX?; and (3) What was the best thing about ENGL XXX? Responses from 450+students were qualitatively analyzed. The themes that emerged from this data analysis arepresented in this paper, and are used as part of the evaluation of this course.This paper will (1) describe the course and course assignments, (2) summarize student responsesto the reflection questions listed above, (3) identify, based on
(with more than 1,000 employees) have already implemented AI in their operations,while an additional 40% are actively exploring or testing AI technologies. These developmentsare driving new economic opportunities and innovation globally. For instance, the latest reportfrom the International Data Corporation (IDC), China Model as a Service (MaaS) and AI LargeModel Solution Market Tracking [2], indicates that, in the first half of 2024, China’s MaaSmarket reached 250 million RMB, while the AI large model solution market totaled 1.38 billionRMB. This growing market for large model services reflects an increasing investment byenterprises, underscoring the transformative and disruptive impact of AI across industries.The social transformation driven
same institution. That earlier project, which involved thedesign and implementation of a cross-college, transdisciplinary model of instruction, providedvaluable experiential and analytical grounding for this study. Drawing from that shared foundation,this analysis gained a deeper understanding of the complexities of convergence education, enablingus to identify recurring themes related to course design, team formation, and the broaderimplications for transdisciplinary pedagogy. As before, this collaborative team was able to haverich discussion about transdisciplinary teaching and learning and educational transformationtogether accordingly. This thinking is reflected in the current paper. The researcher also conducted semi-structured
responses to each question to gain a detailedunderstanding of the participants' perspectives.Question 1: In your own words, how would you describe Artificial Intelligence (AI)?When asked to describe Artificial Intelligence (AI) in their own words, the responses fromparticipants revealed a wide range of perceptions, reflecting both positive and negative views ofthe technology. The majority of participants (36%) described AI as a tool designed to enhanceefficiency and assist with tasks. Many respondents highlighted its ability to save time, streamlineresearch, and improve productivity in academic and professional settings. Phrases such as "ahelpful tool," "a way to quickly search content," and "a tool that enhances daily life" werefrequently
progress for individual assignments, and radar plots that show students'actual achievements and reflections on their learning. Our findings indicate that visualizingstudents' learning in a dashboard provides opportunities to tailor feedback to address theirspecific learning needs and supports student scaffolding. Continuous quality improvementrequires both a mechanism to collect feedback on students' learning and a dashboard thatvisualizes how the feedback is used to improve the quality of instruction and learning integrity.Keywords: Dashboard, Continuous quality improvement, Grading, Feedback, RubricIntroductionEducational institutions face increasing pressure to demonstrate the effectiveness of theirinstructional methods, particularly due to
. This digital evolution highlights the critical needfor a dynamic response to these safety and security challenges. The Bureau of Labor Statisticsprojects a growth of 33% in Information Security Analysis and related fields between 2023 and2033 [1], reflecting the growing importance of data protection at both the individual andcorporate levels. In response to this, cybersecurity professionals must not only possess technicalknowledge and education but be equipped to anticipate and adapt to both current threats andemerging risks.Recognizing the limitations of traditional classroom instruction alone, many universities havedeveloped hands-on lab courses and summer programs to broaden students’ experiences.Programs like the National Science
Progress” paper will outline the steps wehave taken to utilize faculty input and established curriculum to develop an interdisciplinary programrequiring a small number of new courses yet still meeting both ABET requirements for mechatronicsand robotics and partner interest. Student reflections on the program and its first course offering are tobe gathered, along with reactions from faculty, to drive ongoing continuous improvement.2. INTRODUCTIONDigitally connected factories and robot-driven production processes have been highlighted as the futureof the manufacturing industry [1]. A growing national interest in accelerating industrial capacity andmodernizing education through capitalizing on advanced robotics systems supported by
such detailed measurement is required.(ii) Measuring the Rotational Speed of Wheels or Motors: Although reflective sensors or Hall sensors can be used as alternatives, their implementation is complex, accuracy is often low, and programming is challenging. In mobile robots, accurately measuring the current speed is critical. Traditional teaching systems often assume that the command output to the DC motor directly reflects its state. However, due to load variations, this assumption is frequently inaccurate, making it difficult to determine the actual state of the motor. Additionally, in systems addressing environmental issues, such as those that adjust operations based on the rotational speed of wind or water
percentage of A grades earned, but the 10:40 am and 11:45 am class times always had 50% or more of the students achieving an A. • The sections with the lowest grades, C or failing grades, were both 8:30 am sections and SP1-9:35 am sections. The other 9:35 am section had a decrease of 10% in the number of students that earned C grades.Based on these results for the final course grade, the design course had the highest percent of Agrades for the 9:35 am section, and the mechanics course had the highest percent of A grades forthe 10:40 am and 11:45 am sections. These times reflect students’ preferred times for students orthe times when their performance is highest during the day, as reflected in their course grades.While not
members who develop and teach courses selected as TI courses receive avariety of incentives, such as small stipends for new courses or modifications to existing courses,financial support for external speakers, and, if the course is team-taught, full teaching credit foreach faculty member for the first iteration that the course is offered.The process for selection as a TI course is competitive. A call for proposals goes out in Octobereach year for potential multidisciplinary courses for the following academic year. Proposals arethen due by the middle of January. The proposed course must align with the TI mission andaddress contemporary and emerging societal challenges. The proposals require a tentativesyllabus that clearly reflects the mission of
follows the constructivist learning theory, when the gummy is pressed.which posits that knowledge construction occurs bestthrough hands-on experiences and reflections on those experiences [7]. By engaging in this simpleexperiment, students not only learn about basic science and engineering concepts but alsopotentially develop critical thinking and problem-solving skills essential for their future academicand professional endeavors.MethodsWe demonstrated a simple pressure sensor using fiveproducts with varying levels of softness, all of whichare readily accessible to students (Figure 2(a)). Theproducts tested (each costing less than $15) includedthree types of gummy candies: Life Savers, which arehigh softness (Figure 2(b)); Haribo Goldbears