be less likely to rely too heavily on generative AI to completetheir assignments if instructors teach them how to use it effectively and appropriately instead ofbroadly prohibiting its use. This paper presents the results of a survey on students’ perceptions ofand experience with Generative AI/ChatGPT. Identical surveys were administered to students intwo different sections of the same junior-level writing course for engineering majors. In onesection, students were given prior instruction in the focused, ethical use of ChatGPT with aspecial emphasis on Generative AI’s professional impact. These students were then asked topractice prompt engineering using the CLEAR framework described by Lo [1]: Concise, Logical,Explicit, Adaptive, Reflective
). ©American Society for Engineering Education, 2024Work-In-Progress: Holistic, Multi-disciplinary Systems Approach to TeachingSustainable and Contextual Engineering Concepts for Undergraduate StudentsABSTRACTThe urgent global need for sustainable engineering solutions necessitates a paradigmshift in engineering education. This work-in-progress advocates for a comprehensive,multi-disciplinary approach in teaching sustainable and contextual engineering toundergraduate students. The multidimensional challenges of sustainable developmentrequire engineers to understand the complex interplay of ecological, social, economic,and ethical factors. This paper highlights the imperative of embracing a holisticpedagogical framework that combines engineering
respond to the complex ethical, social, political, andenvironmental challenges of today, they may begin to eschew traditional case studies that portrayengineering as objective and apolitical. In this way, they may begin to “transgress” againstdominant views of engineering that can limit students’ critical thinking and engagement withsocio-political issues within engineering contexts. Liberatory pedagogy also disrupts the statusquo of power dynamics and practices in the postsecondary classroom, opening up space for newclassroom activities and assessments that create a more collaborative and equitable learningenvironment [1].In this paper, I explore the redesign of an undergraduate engineering technology and societycourse in relation to the idea of
) ● sub-disciplinary cultures (Gilbert, 2008; Godfrey, 2007; Murphy et al., 2007) ● national cultures (Downey and Lucena, 2005) ● assessment cultures (Borrego, 2008).Godfrey [9] also cites studies of cultural change in engineering education, related to the role of ● institutional culture in effecting change (Covington and Froyd, 2004; Kelly and Murphy, 2007; Kezar and Eckel, 2002; Merton et al., 2004), and ● measuring cultural change (Fromm and McGourty, 2001; Lattuca, Terenzini, and Volkwein, 2006).4.1.2 Observable Engineering Education Cultural Beliefs and ValuesBeliefs such as meritocracy, ethics, complexity, difficulty, worthiness and stress are beingstudied with respect to engineering education culture and the impact on
programmingeducation and real-time feedback, relieving teachers’ workload while giving studentspersonalized curricular information tailored to their needs. Additionally, AI is usually used as adata analytics tool to predict student performance. The reviewed articles focus on AI’s cognitiveand affective impact on students and found positive effects on those variables. At the same time,AI allows for better analysis and utilization of data on student behavior while programming.Limitations in the current reviewed articles on AI in K-12 CS education include insufficientattention to theoretical adoption, ethical concerns, and methodological issues like small samplesizes. This review highlights the critical role of AI in K-12 CS education and illuminatesdirections
individual values into a netaggregate public value. We discuss an important limitation of this approach, namely thatassessing the “value of a sunset” may be biased for those who are visually impaired, colorblind,or photosensitive. This work highlights the convergent approach known as the nurse+engineer,where transdisciplinary integration across two diverse professions is used to solve a pressingsocietal challenge, in this case a more inclusive meaning of public value constructed from acollection of individual values expressed by individual people in response to the question, “whatis the value of a sunset”.IntroductionLicensed, professional civil engineers have an ethical obligation to protect the health, safety, andwelfare of the public [1]. But how
command for complaints. Students also are informed about resources and agencies affiliated with LSU who are available to support them should they face an academic dilemma.Career Development WorkshopsThe career development workshops were professional development oriented including fourworkshops based on the National Association of Colleges and Employers (NACE) competencies,and practical resume writing, and an ethics workshop. Each workshop was assessed for learningoutcomes and perceived value.Teamwork and Communications WorkshopThe teamwork and communication workshop teaches basic skills of workplace teamwork andcommunication aligned to the NACE competencies. Information in the workshop includedstages of team development, writing
Paper ID #42754Board 360: Reflections from Graduates on the Impact of Engineers WithoutBorders USA Experiences on Professional PreparationLazlo Stepback, Purdue University, West Lafayette Lazlo Stepback is a PhD student in Engineering Education at Purdue University. His current research interests focus on engineering ethics, the connections between personal morals and professional ethics, and how students ethically develop as engineers. He earned a B.S. in Chemical and Biochemical Engineering at the Colorado School of Mines (Golden, CO) in 2020.Paul A. Leidig P.E., Purdue University, West Lafayette Paul A. Leidig works in
towards JEDI in engineering practices. Particularly, students will learn about the historical temporal dimension of engineering and social justice through a series of case studies, recognizing that the impacts of engineering span multiple generations, irrespective of whether these effects are positive or negative. This realization will empower students with a sense of continuity and a need for collective efforts, it will enable them to break the barriers of individual accountability, micro-ethics, and direct causality commonly established in engineering practice [17]. This mindset shift acknowledges the need for continued social justice work beyond individual lifetimes, fostering a sense of interconnectedness and
the University of Toronto. Her research interests include engineering culture, engineering careers in the public sector, and ethics and equity in STEM. Dimpho has several years of experience in thDr. Emily Moore P.Eng., University of Toronto Emily Moore is the Director of the Troost Institute for Leadership Education in Engineering (Troost ILead) at the University of Toronto. Emily spent 20 years as a professional engineer, first as an R&D engineer in a Fortune 500 company, and then leadingDr. Andrea Chan, University of Toronto Andrea Chan is a Senior Research Associate at the Troost Institute for Leadership Education in Engineering | University of TorontoMs. Emily Macdonald-Roach, University of Toronto
ways, which supports the idea that there is not just one use forAI in the classroom. Based on the results of both surveys, AI could improve many parts of theeducational learning and teaching process. Addressing ethical considerations in the creation andapplication of AI tools in education is, of course, crucial. The findings of the Chegg pollhighlight the necessity for universities/colleges to have open policies that instruct students on theresponsible and productive use of artificial intelligence (AI) in the classroom.A study by MIT researchers examined the productivity effects of generative AI technology in thecontext of mid-level professional writing tasks [3]. In their experiment, they assigned writingtasks to college-educated
effectively with a range of audiences 4. an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts 5. an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives 6. an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions 7. an ability to acquire and apply new knowledge as needed, using appropriate learning strategiesTable 2: ASCE’s Civil Engineering
research interest in engineering education. Her technical expertise is computational intelligence and digital systems. Primary engineering education work includes infusing ethics into computing courses and enhancing transferable skills through active and universal design for learning methods. ©American Society for Engineering Education, 2024 Educational Infographics, A Review PaperAbstractThis paper endeavors to inspire educators and instructional designers to more fully embraceinfographics, leveraging their unique capabilities to enrich the teaching and learning landscape andprepare students for an increasingly visual world. To inspire, this review seeks to offer a coherentframework
[1], it is paramountfor engineering education to grow into a more inclusive and innovative practice to fulfill societalneeds. While some progress has been made in introducing innovation during the first and fourthyears of undergraduate education, the middle two years, burdened with core engineering courses,have seen limited change [2]. As we re-develop these courses, integrating “innovativeentrepreneurship” in parallel with social ethics and EDI could be a great catalyst for positivechange. Literature has shown its inclusive impact on the job market [3] and the economies ofnations [4]. Education based on an entrepreneurial mindset relies on collaborations acrossdisciplines, effective group work and productive communication [5], all pillars
challenges of using this AI-based model are discussed, as well asthe ethical and social issues that arise from its implementation. Suggestions andrecommendations for future research and practice in this emerging and interdisciplinary field arerequested as this study will contribute to advancing knowledge and innovation in STEMeducation and inspire more researchers and educators to explore the potential of AI and CV inenhancing teaching and learning.Literature ReviewAs Lombardi et al [8] described, active learning is a broad term among educators. They assertedthat the existing comprehension from the literature on active learning is excessively broad andlacks precise particulars, impeding the ability to conduct effective research and enhance
]. Specifically, graduates from an engineering program leave with the overallimpression that engineering decisions made in the real world are completely objective and without bias.General consensus in the field firmly believes that engineering and science can be separated from politicaland social concerns as long as “rigorous” engineering and scientific methods of design and inquiry arefollowed. But if we consider some recent history of engineering, we find many examples and exceptionsthat disprove this supposed neutrality rule [18-20]. From the Space Shuttle Challenger disaster [21] to theVolkswagen “Dieselgate” scandal [22] to Democratic Republic of Congo conflict minerals ethics [23] toCOVID-19 vaccinations [24], decisions regarding and perceptions of
instruction of specific lessons from Units 1 and 2of the e4usa curriculum. For this paper, Lessons 1 and 7 from Unit 1 and Lessons 1, 4, 6, and 7 from Unit2 were examined. These lessons emphasize the importance of engineering communication methods andthe continuous evolution of the definition of engineering and an engineer's role. They also highlightcollaboration within teams, addressing ethical considerations, understanding the broader applications ofengineering, and tackling societal challenges. A full description of the lessons, along with theirsubsequent learning outcomes and thread connections are detailed in Table 1.Table 1Overview of Analyzed Lessons from Units 1 and 2Unit and Name of
artificial intelligence can be used in education in a creative and ethical way.Prof. Catalina Cortazar, Pontificia Universidad Cat´olica de Chile Catalina Cort´azar is a Faculty member in the engineering design area DILAB at the School of Engineering at Pontificia Universidad Cat´olica de Chile (PUC). Catalina holds a Ph.D. in Engineering Science with a focus on Engineering Education from PUC, an MFA in Design and Technology from Parsons The New School for Desing, an MA in Media Studies from The New School, and a bachelor’s degree in Civil Engineering, with a concentration in Structural Design.Dr. Jorge Baier, Pontificia Universidad Cat´olica de Chile He is an associate professor in the Computer Science Department
Math department on Pre-calc preparedness. Plan to move more math topics into Rat coursesEthics and Professional Practice Slightly Low Steady Emphasize ASCE Code of Ethics in Construction ManagementEngineering Economics Good Upward Continue to include in Construction ManagementStatics Low Upward Contine to Emphasize the Minimum "C" RequirementDynamics Good UpwardMechanics of Materials Slightly Low Upward Continue to Emphasize the Minimum "C" RequriementMaterials Good
guidelines and training on ethical GenAI use in academia.These advantages and risks underscore the need for measured integration of GenAI in ways thatmaximize benefits while proactively addressing challenges.Future directionsIn developing ethical guidelines for GenAI use, institutions could require transparent indicationof AI-generated content in academic work through explicit citations or notations. Studentsutilizing GenAI for assignments may be asked to submit prompt engineering logs documentingtheir process of formulating, iterating, and refining prompts. This would create accountabilitywhile allowing innovative GenAI applications. With appropriate oversight, GenAI can assiststudents in the development of foundational skills.As a starting point
theirdevelopment as skilled communicators. Relying solely on AI can lead to a decline in criticalthinking and creativity. It is important to carefully consider the ethical implications of using AI-generated content, particularly in academic and professional settings, where the boundarybetween AI assistance and plagiarism could become less clear. Additionally, the potential misuseof personal information and data security concerns related to AI writing tools should bethoroughly examined. It's worth noting that AI tools may encounter challenges in understandingcomplex contexts, cultural references, and emotional subtleties, potentially leading tomisinterpretations in the generated content.The ”AI Writing Tools” used for the analysis are listed in Table 1
them and their risks is notsomething built into our engineering curriculum, with the exception of students who enroll in ournetwork security elective.There also is a strong ethical aspect of this work. As a consulting company, employees aredirectly connected to clients’ networks, either through remote access, or preferably, clientsupplied devices which are maintained by the client’s IT organization. This environment placesemployees in potentially ethically challenging environments, as it is likely they may identifypotential vulnerabilities inside of a client’s environment that could be exploited by an externalentity. However, the company is not authorized to investigate or fix these issues. Thus, a strongculture of reporting issues that are
interaction, and post-assessments, this research intends to providevaluable data that can inform educational practices. This study aims to identify key challenges,such as potential cheating and diminished learning outcomes, while also exploring how AI canbe ethically integrated into computer science education. The proposed findings will guide theredesign of assessments to mitigate risks while harnessing AI's benefits, ultimately providingeducators with a framework to improve student assessment in an AI-enhanced academicenvironment.KeywordsArtificial Intelligence, AI-Assisted Learning, ChatGPT, Computational Thinking, ComputerScience Education, Learning Outcomes, Academic Integrity, Critical Thinking, AssessmentDesign, Introductory Programming
Research Overviews Part I 4.93 0.27 DAY 1: ATP-Bio Research Overviews Part II (hands on) 4.79 0.43 DAY 1: REU Alumni Panel 4.93 0.27 DAY 1: How to Read a Scientific Paper 4.79 0.43 DAY 2: Lab tours 4.64 0.63 DAY 2: Ethical Lab and Data Practices 4.93 0.27 DAY 2: Scholar Panel 4.86 0.53 Section 2Rate the following experiences from very poor (1) to excellent (5)Survey Item
groupdiscussions, Mentimeter online surveys to collect immediate feedback from the whole group, andnetworking breaks. Lunch was provided on both days and dinner was provided on Day 1.Day 1 was dedicated to understanding perspectives from stakeholders regarding electricityaccess and sustainable business. Keynote speakers Mou Riiny, CEO of SunGate Solar in SouthSudan and Dr. June Lukuyu, Assistant Professor of Electrical and Computer Engineering at theUniversity of Washington shared insights on the challenges of working in South Sudan andUganda. Themed discussions focused on enhancing the classroom experience and sustainable,ethical, and beneficial projects as well as a student panel. Table 2 shows the schedule for Day 1:Table 2: Day 1 Schedule Day 1
. Her research interests include empathy, design education, ethics education and community engagement in engineering. She currently teaches Cornerstone of Engineering, a first-year two-semester course series that integrates computer programming, computer aided design, ethics and the engineering design process within a project based learning environment. She was previously an engineering education postdoctoral fellow at Wake Forest University supporting curriculum development around ethics/character education. ©American Society for Engineering Education, 2024 Student Engagement – IoT-Based Learning Materials and ProjectsAbstractEven with a return to in-person learning by many institutions
. She holds graduate degrees in engineering and business administration from the University of Michigan, and began teDr. Katie Snyder, University of Michigan Dr. Snyder is a lecturer for the Program in Technical Communication at the University of Michigan. She teaches design, ethics, and technical communication as social justice to students in the College of Engineering.Sara Elizabeth Eskandari ©American Society for Engineering Education, 2024Connecting Campus and Community: applying virtual reality technologies to facilitate energy justice and emerging technology literacy Aditi Verma, Sara Eskandari, Kellie Grasman, Katie SnyderIntroductionThe history of energy technology
can sometimes be overlooked by faculty andadministrators.Furthermore, students’ engagement in out-of-class activities has been connected with otherpositive outcomes, including improved analytical, group, and leadership skills [5], increasedstudent-faculty interaction [6], ethical development [7], and greater interest in pursuing andremaining in engineering careers [8] [9] [10]. Despite these positive outcomes, it can sometimesbe challenging to engage students outside of the classroom. Major et al [3] identified schedulingissues as a major factor deterring student involvement. Additional research has indicated thatengineering students devote more time preparing for class than students in other disciplines and,therefore, may be less likely to
and the University of Virginia. William has degrees in literature and Science and Technology Studies, and has taught courses in English, philosophy, and sociology in universities in the USA and Mexico. His current research investigates the ethical and social implications of technology, including those related to artificial intelligence, automation, bioethics, machine ethics, and post and trans-humanism.Dr. Bryn Elizabeth Seabrook, University of Virginia Bryn Seabrook is an Assistant Professor in Science, Technology, and Society at the University of Virginia. She received her Bachelor of Arts in Humanities, Science and Environment with a minor in Vocal Performance in 2012, a Master of Science and TechnoloJoshua
individual function and performwithin a work environment at the highest level. Examples of professional skills includecommunication skills, teamwork, time management, creativity, work ethic, leadership, conflictmanagement, and stress management, among others.Professional skills can be taught and reinforced using expository, guided, and active strategies[1]. The integration of such skills in the curriculum can occur via lectures (expository),demonstrations (expository), project work (guided), simulations (guided), role playing (active),brainstorming (active), and coaching (guided) [1]. Engineering students are exposed to soft skillsusing one of the following three learning methodologies: expository (lectures, seminars,conferences, and demonstrations