earned B.S. degrees in Civil Engineering and in Mathematics from Carnegie Mellon University (1993) and a Ph.D. in Theoretical and Applied Mechanics at Cornell University (1999). Prior to UPRM, Papadopoulos served on the faculty in the Department of Civil Engineering and Mechanics at the University of Wisconsin, Milwaukee. Papadopoulos has diverse interests in structural mechanics, sustainable construction materials (with emphasis in bamboo), engineering ethics, and engineering education. He is co-author of Lying by Approximation: The Truth about Finite Element Analysis, and after many years, he has finally (maybe) learned how to teach Statics, using an experiential and peer-based learning ”studio” model. As part of
student learning outcomes and proficiencies, rather than specific coursecontent.To begin the curriculum redesign process, a retreat was held in December of 2019 to gather inputfrom faculty and staff of the department, with a focus on the question, “What do we want ourstudents to be able to do, know, and care about after successfully completing the ME program?”The output of this retreat was six guiding “areas” that would guide a department committee in(eventually) redesigning the curriculum: Problem Solving; Communication; Professional Identityand Ethics; Teamwork, Leadership, and Inclusivity; Information Literacy, Judgement, andCritical Thinking; Character Traits and Self-Directed Learning.As all readers will know, the Covid-19 pandemic caused
online students interested in undergraduate research opportunities?and 2) has student interest in undergraduate research changed since the onset of the COVID-19pandemic? The purpose of this study was to compare pre- and post- student perspectives ratherthan to directly inquire about perceptions of how the pandemic impacted fully online students toavoid acquiescence (response) and recall bias. This paper provides a summary of theresults.MethodsThis study was conducted at a medium-sized private university with two residential campuses andone distance campus. Online student participants were recruited from an upper-divisionundergraduate ethics course, that is required in nearly all online degree programs, ensuring abroad representation. The survey
beneficial? What types of technical skills are most beneficial? What knowledge and skills would you like to see from new graduates that you believe are missing? What do new graduates need to know to be an effective team member? What should students be able to do upon entering the workforce?Data Analysis: This exploratory study is a work in progress. The findings will help identify gaps incurrent student preparedness. This will ensure graduates are better prepared for the demands ofthe field. The data was in vivo coded to nine key areas. Communication; Teamwork; Professionalattitude (Attitude, Work Ethic; Growth & Development; Confidence; Willingness / Drive,Asking Questions); Internships
, 2014). 1. Engineering Knowledge 2. Problem Analysis 3. Design and Development of Solutions 4. Investigation 5. Tool Usage 6. The Engineer and the World 7. Ethics 8. Individual and Collaborative Teamwork 9. Communication 10. Project Management and Finance 11. Lifelong Learning 5This PBL program is also referring to the global competence as a 21st century imperatives(National Education Association (NEA), 2010) for nurturing mindset of global citizen.It is easily expected that the major learning outcomes of this 3DDA workshop are related toEngineering Knowledge, Problem Analysis, Investigation, and Tool Usage. The question is howand how much extent other attributes could be acquired
values, power dynamics, and systems of oppression. The infrastructure, technologies, and products created by engineers shape how peoplelive, work, and interact, often reinforcing existing inequities or creating new ones. From thedevelopment of weapons used in war to technologies that perpetuate surveillance and control,engineering has a direct impact on societal structures and human rights [4]. Even choices thatseem purely technical, such as material selection or energy sources, carry ethical implications, asthey affect environmental sustainability and global resource distribution. By failing to questionthe broader implications of their work
, emphasize the importance ofintegrating AI ethics into educational curricula. This study builds on these methodologies by implement-ing domain-specific sentiment analysis and introducing a real-time feedback system to support personalizedlearning experiences.Aligned with these advancements, a web-based NLP platform[5] was developed for undergraduates, en-abling them to apply linguistic theories through case-based activities. This platform provides visualizationtools for tasks such as coreference resolution and word embeddings, allowing students to better understandabstract NLP processes through hands-on data manipulation. These types of interactive platforms bridgethe gap between theoretical knowledge and practical application, creating a more
given numerous guest lectures and organized numerous workshops on the ethics and use of GenAI in engineering education.Jesan Ahammed Ovi, Colorado School of Mines Jesan Ahammed Ovi is a Ph.D. student in the Computer Science Department at the Colorado School of Mines, where he works as a Research Assistant under the supervision of Dr. Estelle Smith. His primary research area is Human-Computer Interaction (HCI), complemented by prior experience in Natural Language Processing (NLP) and data mining. Jesan was previously a faculty member at East West University. He also contributed to the ”GenAI Adoption at Mines” research project, where he led data analysis efforts using advanced statistical and machine learning
learningobjectives designed to cultivate students' ability to navigate ambiguity and complexity [4]. Byemphasizing empathy, creativity, and contextual awareness in project-based learning, theinitiative seeks to prepare graduates who are not only technically proficient but also equipped toaddress societal, environmental, and ethical considerations.A cornerstone of the initiative is the creation of a Faculty Learning Circle, aimed at enhancingboth Pedagogical Content Knowledge (PCK) [5] and Technical Content Knowledge (TCK) [6].Through collaborative discussions, case studies, and reflective teaching provocations, facultyhave aligned their instructional strategies with a unified set of HCD principles. This approachensures consistency across courses while
their experiences using generative AI. They are asked to communicate newinsights, further questions, and their emerging understanding of client-based design. While theactivity itself is scaffolded throughout, these student reflections provide instructors anopportunity to guide discussions on creativity, decision-making, and AI’s role in the engineeringworkflow, as well as touching on some problematic and ethical considerations of generative AI.ExamplesThis project was implemented at a mid-sized private university in the northeastern United Stateswithin a 30-student first-year introduction to engineering design course. Microsoft Copilot isfreely available to all students (via university-wide license) which is capable of both text andimage
in classroom education, transportation, computer large number of research articles.programming, construction, space science, engineering, medicalindustry, and many other scientific and technological arenas. Concrete is one of the commonly used construction materialsNevertheless, AI is considered prohibited in many circumstances utilized worldwide because of the availability of its ingredientsdue to ethical concerns, trepidations of job displacement, and its and its relatively easier application. However, application ofportrayal in media. This combination of ethical, economic, and concrete in complex geometric structures, e.g., tunnels,cultural factors drives suspicion and agitation against AI
always the case, technology can be used positively or corresponding answer is shown in Fig. 2.negatively, ethically or unethically. The goal of thisprogramming course, as with every other course, is to educatethe students to fulfill the course’s outcomes successfully. Theysay, “If you can't beat them, join them.” This seems to be theideal case to apply this saying here. II. USE OF AI IN THE COURSE Fig. 1. Interaction of use AI.A. The Proposal We shall avoid proposing to exclude AI from the course ormuch more from the curriculum. This would be something outof anybody's control, much more of the instructor. On thecontrary, we propose an approach to incorporate AI in thecourse in a beneficial way
Phenomena, Mathematical Methods, Ethics, and Safety• These updated LOs were inputted into GroupWisdom .• Our subject matter experts (SMEs) read through the LOs and individually added LOs in the brainstorming phase.Body of Knowledge Process• Collected and refined learning objectives (LOs) for five graduate chemical engineering courses covering six topics: • Thermodynamics, Kinetics and Reactor Design, Transport Phenomena, Mathematical Methods, Ethics, and Safety• These updated LOs were inputted into GroupWisdom .• Our subject matter experts (SMEs) read through the LOs and individually added LOs in the brainstorming phase.Most added LOs were non-curricular skills or specialized topics.Body of Knowledge Process• The SMEs individually grouped
between the communitycollege, university and industry partners, a non-profit organization, and social scientistsattempts to more fully understand how to implement, assess, and expand computing pathwaysfor a diverse group of students, especially in the CC context.One of the primary objectives for the project was to develop and implement an interdisciplinaryAI certificate, which was completed at the HSCC. As the program matured, two college creditcertificates were developed. The first certificate is the AI Awareness Certificate, where studentshad to take both the AI Thinking and AI Ethics course, and choose either an AI Business classor AI Robotics course. The Artificial Intelligence Practitioner certificate shares the AI thinkingand ethics courses
of AI among university students on learning outcomes and processes 2. Evaluate how AI-driven teaching tools can be purposed for personalized and inclusive educationFigure 2: Example fill-in-the-blank quiz created by ChatGPT to help students practice their under-standing of thermodynamic concepts. Created in ChatGPT-4 using the following prompt: Generate20 fill in the blank questions to help me study for a thermodynamics quiz covering basic thermo-dynamics vocabulary and units. 3. Explore the ethical dimensions and practical challenges of AI use 4. Understand how the integration of AI into classroom settings alters student-teacher and student-student dynamics 5. Explore and compare the perceptions of instructors and
, Computer Science Education, Machine Learning, PersonalizedLearning, Ethical AI, Research, Graduate Programs, Undergraduate Programs.INTRODUCTIONThe technological innovations of the 21st century have fundamentally transformed how the worldoperates [1], creating entirely new areas of expertise and workforce demands [2,3,4,5]. Theinterdisciplinary interest from scholars in linguistics, psychology, education, and neuroscience aswell as other disciplines, who examine AI through the lens of their respective fields, such as itsnomenclature, perceptions, and knowledge poses challenges in defining AI [6]. This hasnecessitated the development of AI categories within specific disciplinary contexts.There is a pressing need for widespread education across all
Deliberating Public Welfare in Engineering – The Capability ApproachAbstractThis paper addresses the theme of “the Moral and Ethical Responsibility of Engineers andEngineering”, particularly responding to the question of how to define or deliberate the meaningof ‘public welfare’ and ‘common good’ in engineering degree programs. Drawing from decadesof international work on human development, particularly in the global south, this paper reportson adapting the capability approach to an engineering degree program. Developed by AmartyaSen, the capability approach sought to replace GDP-based models of welfare economics byframing the goal of development as enabling individuals to live a life they value. The things aperson values, what they are and can do
while fostering a deeperunderstanding of its benefits and limitations. As AI technology continues to evolve, it isincreasingly apparent that we need to develop and share more promising practices that enablefaculty and students to navigate various applications effectively. In each activity presented,students integrate technical skills with social and ethical thinking as they explore and evaluatehow GenAI can enhance or hinder their engineering design process. These topics are woven intotwo team-based design challenges to make this meaningful and applicable to students.ImplementationDuring the fall semester, Engineering Foundations I is offered across 20 sections, each averaging37 students, and is taught by seven faculty members. For both
methods. Theframework used here may serve as a framework for other institutions examining incorporating orimplementing leadership in programs.Overall, USAFA works to instill outcomes for graduates to work towards including: (1) Critical Thinking (2) Application of Engineering Problem-Solving Methods (3) Scientific Reasoning and Principles of Science (4) The Human Condition, Cultures, and Societies (5) Leadership, Teamwork, and Organizational Management (6) Clear Communication (7) Ethics and Respect for Human Dignity (8) National Security of the American Republic (9) Warrior Ethos as Airmen and GuardiansThe fifth institutional outcome describes the leadership outcome, for students to exhibitleadership, teamwork
practices. The results suggest a need for University [7]. A study on the impacts of AI tools on,better awareness and guidance on effectively utilizing AI tools in specifically, mechanical engineering curriculum underscorestechnical education. This study may guide educators in the importance of acknowledging the growing impact ofpromoting the adoption of AI tools in engineering education advanced GenAI tools in education and professional settings.while encouraging critical thinking, ethical use, and a balance The study concluded that rather than dismissing them outright,between AI reliance and traditional learning approaches. institutions, educators, and organizations should adopt a
approachesused in smaller programs. Finally, only syllabi from common first year engineering courses wereused; we did not include any major-specific introductory engineering courses.FindingsLearning Outcomes as Habits and MindsetsMost introductory engineering course syllabi emphasized teamwork, communication, problemsolving, design, engineering tech/tools, and ethics (Table 2). Teamwork and communicationstood out as nearly universal to introductory engineering curricula, followed by engineeringdesign & process as an approach to problem solving indicating that these skills are highly valuedacross introductory engineering courses (Table 2). The consistent inclusion of engineering-specific tools and technology indicated an early commitment to building
indicates a significant difference across most questions between the expecteddistribution of responses and the observed responses. Only ABET SO 1 and one part of ABETSO 6 (related to conducting lab experiments) showed no significant difference.These results contrast with previous studies [2] [4] [6] that typically found a positive correlationbetween student performance and surveys used to gauge self-efficacy. This discrepancy may bedue to the inclusion of a broader range of ABET outcomes in our study, including aspects oftenoverlooked, such as ethics, professionalism, and teamwork skills.Table 3: Chi-Square Test Results ABET Student Outcome (and summary of the outcome) h-value p-value 1 – Ability to solve complex
covers linear regression, neural networks, sparsity, and dictionary learning. The goal of this part is for students to see a vari- ety of ML methods that they can understand most of, but they do not code these applications from scratch. Instead, students use Python libraries. 3. Part 3 (3 weeks) What other cool ML things are out there? This part briefly introduces advanced ML algorithms and the ethics of ML.The following subsections describe the learning activities in each course part and Fig. 1 summa-rizes the schedule for the Fall 2024 semester. Key activities are highlighted in blue text in both thefollowing text and in Fig. 1.3.1 Part 1Given the desire to present ML algorithms from first principles and the lack of
Kanika Sood, California State University, Fullerton Daisy Tang, California State Polytechnic University, PomonaThis work-in-progress study describes our grant-funded efforts in developing a computer sciencefaculty learning community (FLC) across six California state institutions. With an emphasis onsocially responsible computing (SRC), the faculty development effort that prepares faculty forSRC lesson implementation has integrated social scientists with computer science faculty in therotating leadership team. It works collaboratively to facilitate dialog around experiences ofimplementing lessons that focus on social justice and ethical decision-making. Our data-drivenFLC and course transformation effort was initiated by
STEM and STEM Education.” [Online]. Available: https://www.nsf.gov/pubs/2023/nsf23593/nsf23593.htm[6] ASCE, “Code of Ethics,” Code of Ethics. Accessed: May 23, 2023. [Online]. Available: https://www.asce.org/career-growth/ethics/code-of-ethics[7] ASEE, “Persons with Disabilities Leadership Roundtable,” presented at the American Society for Engineering Education Annual Conference, Salt Lake City, UT, 2018.[8] E. A. Cech, “Engineering ableism: The exclusion and devaluation of engineering students and professionals with physical disabilities and chronic and mental illness,” J. Eng. Educ., vol. 112, no. 2, pp. 462–487, Apr. 2023, doi: 10.1002/jee.20522.[9] M. Svyantek, “Missing from the classroom: current
towards disability in general public and patientpopulations [10]. Development of fair AI/ML-enabled medical devices and performing bias-freeresearch of ML is significantly challenging the applicability of AI/ML in BME. [11] The U.S.Food and Drug Administration (FDA) recognized the necessity of addressing bias in clinicalmachine learning systems, first in the proposed regulatory framework published in April 2019[12] and later as a guiding principle in October 2021 [13].However, ML courses in BME programs around the U.S. are still rare, and teaching of bias inML systems remains largely scattered in computer science and ethics departments, which oftenfocus on privacy [14]. At the BME department of UC Davis, we recognize the importance ofarming our
curriculum was modified to address issues inengineering design, technical communication, problem-solving, engineering ethics, safety,teamwork, sociotechnical engineering problems, and innovation in addition to the topicspreviously covered. These topics were introduced using lectures, in-class activities, and asemester-long design project. Our goal was to provide students with a memorable and fun courseto boost enthusiasm around engineering at the beginning of their academic endeavors. Studentswere split into teams of six based on their declared engineering majors to build community andinclusion from the beginning of their undergraduate studies. For undeclared students, they werescattered throughout unfilled groups or placed in groups with other
. Semester Course Enrollment Fall 2023 ME Program Seminar 116 Spring 2024 Dynamics 55 Fall 2024 ME Program Seminar 117Codebook: The revised codebook based on the open-ended question responses collected in Fall2023 consists of 10 distinct codes that describe students’ perceptions of engineering practice: 1. Considers ethics 2. Considers safety 3. Considers efficiency 4. Considers complexity 5. Utilizes knowledge 6. Collaborates with others 7. Improves or makes new designs 8
Paper ID #47582Project-Based Learning (PBL) for Developing Critical Thinking Skills in EngineeringStudentsDr. Brainerd Prince, Plaksha University Brainerd Prince is the Associate Professor of Practice and the Director of the Center for Thinking, Language and Communication at Plaksha University. He teaches courses such as Reimagining Technology and Society, Ethics of Technological Innovation, and Art of Thinking for undergraduate engineering students and Research Design for PhD scholars. He completed his PhD on Sri Aurobindo’s Integral Philosophy from OCMS, Oxford – Middlesex University, London. He was formerly a Research
may be disregarded or simply ignored [3]. This isironic, given that at least one widely-accepted engineering code of ethics emphasizes its focus onpublic welfare [7]. It is also recognized that the fulfillment of beneficial public welfare goesbeyond the mechanics of problem solving because modern engineering problems are ill-defined,multifaceted and include factors beyond the scope of technology [3], [4], [6]. The optimalsolution for the public welfare may also lie beyond the requirements of a particular client orhighly influential governmental or economic body and/or at the expense of other stakeholders[3].A number of methods exist to characterize engineering identity, often through surveys orinterviews [2], [3], [4], [6], [8]. These methods