engineering course, where all students in the course can use this toolthroughout the term. This will allow the researchers to investigate this intervention from aholistic perspective and understand some of the long-term benefits and shortcomings of thisproject management tool, which can be used to improve the tool.Ethical ConsiderationsWhile this study explores the potential benefits of AI-powered task distribution in project-basedlearning, it is equally important to acknowledge the ethical concerns and pedagogical challengesassociated with automating project management processes. The integration of AI in engineeringeducation, particularly in project management, raises critical questions about skill development,fairness, and long-term impacts on
. Totackle them, it is essential to incorporate diverse perspectives that reflect themultifaceted nature of the world. Different cultural contexts provide the foundation fortailoring global problems into localized solutions that are both practical and sustainable.Additionally, varied life experiences bring unique insights, enriching the understandingof ethical and social challenges within engineering, thereby fostering morecomprehensive and impactful approaches to problem-solving.Diversity is an absolute necessity for the industry, as it drives innovation, fosterscreativity, and enhances problem-solving by bringing together a wide range ofperspectives and experiences (Direito et al., 2021; Leever, 2020; Jones et al., 2020).Engineering solutions
participation of diverse perspectives in building a sustainable future. This paperdescribes the theoretical support and work in progress for our Engineering for One Planet (EOP)mini-grant. The objective of our mini-grant is to design an undergraduate course and assignmentthat integrates systems thinking, engineering ethics, design justice, and the EOP sustainabilityframework through the lens of ethnographic design. This assignment, and the course it is situatedwithin, are co-designed by an anthropologist who directs several design initiatives in the schoolof engineering and an associate professor of systems engineering at the authors’ home institution.Our process includes implementation and evaluation of our assignment in the Spring Semester2025. This
University. Her research interests focus on three key areas: data science curriculum and ethics, retention of minority students in STEM degree programs, and organization and classification of big data.Dr. Qiping Zhang, Long Island University Dr. Qiping Zhang is an Associate Professor in the Palmer School of Library and Information Science at the C.W. Post Campus of Long Island University, where she also serves as director of the Usability Lab. Dr. Zhang holds a Ph.D. and an M.S. in information and library studies from the University of Michigan, Ann Arbor, and an M.S. and a B.S. in cognitive psychology from Peking University in Beijing, China. Prior to joining Long Island University in 2006, she worked at Drexel
Institutions. Subbian’s educational research is focused on asset-based practices, ethics education, and formation of professional identities.Gimantha N Perera, The University of Arizona Gimantha Perera is a Sri Lankan born researcher and educator from NC State University. He was inspired to be an engineer by his maternal grandfather Anil, who would consistently come home from work covered in grease and grime after climbing bodily into machines to fix them. He shares a promise with his grandfather, now departed, that he will continue to innovate, contribute, and revolutionize industry through engineering and teaching. His world view that can be summed up in two statements: ”Just because it works, doesn’t mean in can’t be
suggest that institutional constraints on providing equaleducational opportunities can limit women's career advancement in technology fields and affectthe broader engineering sector. The findings of this study show that women's participation andprogress in these fields can be greatly improved through changing information and communicationtechnology policies to better respond to their requirements.Machado-Taylor and Özkanli [28] emphasize differences in academic career developmentbetween male and female academics. They indicate the importance of institutional support toremove these obstacles. Atakan et al. [30] highlight the ethical principles of future managers andshow that female Turkish students adhere more to ethical principles in the
University of Pittsburgh. Dr. Barillas’s focus is on first-year engineering education, student engagement, interdisciplinary learning, and inclusive pedagogy. As Program Director for ID3EA, she has led curriculum development initiatives that integrate hands-on design, teamwork, and real-world problem-solving into the foundational course sequence. Her teaching emphasizes active learning, student-centered instruction, and the development of professional skills such as technical communication, collaboration, and ethical decision-making. Her research interests include interdisciplinary education, curriculum innovation, and the retention and success of underrepresented students in engineering. FYEE 2025
Engineering from the University of Pittsburgh. Dr. Barillas’s focus is on first-year engineering education, student engagement, interdisciplinary learning, and inclusive pedagogy. As Program Director for ID3EA, she has led curriculum development initiatives that integrate hands-on design, teamwork, and real-world problem-solving into the foundational course sequence. Her teaching emphasizes active learning, student-centered instruction, and the development of professional skills such as technical communication, collaboration, and ethical decision-making. Her research interests include interdisciplinary education, curriculum innovation, and the retention and success of underrepresented students in engineering
of this paper.AI is already used extensively in health care, finance, manufacturing, retail, and transportation.The challenges include data availability and accuracy; ethical considerations such as privacy,bias and transparency; lack of people with technical skills and knowledge to run AI systems; andintegrating AI systems into currently used systems. In many cases, it takes a highly skilledindividual to prompt the AI system for a program and recognize how to efficiently an deffectively modify it. New AI programming languages and frameworks are released regularly, sothese individuals must stay current and keep pace with the latest industry trends. [4]As AI platforms have emerged over the past half-decade, the debate has raged in the
C Creating Figure Plans Week 6 Identifying Research Gaps CDE Communicating Week 7 CDE Research Gaps Scientific Writing, Broader Week 8 EF Impacts and Intellectual Merit Week 9 Research Ethics CLaboratory reports were replaced with three formative assessments that prepared students toaccomplish expert-level cognitive tasks that ultimately allow
been a growing emphasis on integrating sustainability into STEM education,driven by the urgent need to address environmental and social challenges and equip future generationswith the knowledge and skills necessary to promote sustainable development. It is essential to adaptscience, technology, engineering, and mathematics education to contemporary needs, includingsustainable development, ethical competencies, and preparation for the evolving demands of theglobal STEM landscape. Numerous educational efforts are underway to incorporate thesecompetencies into STEM curricula, ensuring that future professionals can design innovative andsustainable solutions to emerging economic, environmental, and social challenges. To effectivelyintegrate the
learners, canfoster a more personalized learning experience. A key aspect of this is targetedfeedback, which plays a vital role in student development. This study presents astrategy that enables instructors in chemical engineering courses to create bespokeproblem sets and solutions tailored for their students. Ethical AI use and intellectualproperty contributions are discussed extensively in the text. The issues consideredwere (1) bias in AI-generated problem statements; (2) academic integrity andplagiarism; (3) data privacy and student information; (4) openness and explanation;(5) intellectual property and copyright; and most importantly, (6) the general frameworkfor ethical use of AI in engineering education.This approach leverages Python
and civic-minded • Roles: The Metaliterate Learner roles include collaborator, producer, publisher, researcher, participant, communicator, translator, author and teacher • Goals and learning objectives: o Actively evaluate content while also evaluating one’s own biases o Engage with all intellectual property ethically and responsibly o Produce and share information in collaboration and participatory environments o Develop learning strategies to meet lifelong personal and professional goalsMakerspaces: Known also as makerlabs, hackerspaces or fablabs, makerspaces vary frominstitution to institution, generally including diverse equipment to support educational activities,such as 3D
complex global challenges. ABET [8] has incorporated global competencyinto its student outcomes, encouraging curricula that foster global awareness, cross-culturalcollaboration, and ethical leadership. European initiatives similarly emphasize inclusivity, genderequity, and democratic principles, while programs such as the European Green Deal [9] and theErasmus+ framework [10] actively support sustainability and cross-border collaboration ineducation. Additionally, initiatives like the Grand Challenge Scholars Program [11] andEngineers Without Borders [12] promote experiential learning opportunities, encouragingstudents to address pressing global issues through innovative and community-centered solutions.These efforts collectively highlight a
genAI as a tool for their writing assignments (Table 2) and hadnegative feelings towards it (Table 3). This points to a group of students gaining AI literacythrough the class and then deciding to no longer use it. An example of a student who was part ofthis group and reflected this sentiment: “I don't like it in general. It may have its uses, but overallI don't like the growing trend of relying on AI for skills that we should be developing ourselvesduring college. I also have my doubts about any ethically sound way to use it.”Our survey answers reflect a high degree of skepticism and belief that the generated text is notreliable in contextual and scientific content. Students in this group rejected the use of AI due tothe low quality and
complex task of identifying keywords. However, LLMs such as ChatGPT can also produceerroneous output [5]. A common problem with LLMs is their tendency to hallucinate, a problemthat may be inherent to their architecture [6]. Borji defines eleven types of LLM errors, includingproblems with reasoning, logic, humor, ethics, and bias [7]. The evidence of LLM bias, includingracial bias, is particularly troubling. For example, LLMs show prejudice related to dialect markersassociated with Black English [8] and stereotypes associated with student names [9]. Thisevidence of bias suggests that caution is warranted when LLMs are used in any task wherejudgement is required.1.3 AI in Education ResearchTo date, there has been limited research on the potential
student queries and identify usage patterns across courses.For RQ4, we compare student prompts with course syllabi and the university’s student code toidentify and characterize instances of potential policy violations. We use natural languageprocessing (NLP) techniques to classify question types and patterns. This mixed-methodapproach will provide a comprehensive understanding of how students interact with the systemand how it supports their learning. This study aims to provide insights into the role of AI-drivensystems like AI-bot by investigating the different types of questions and their relevance insupporting student learning, while also addressing potential challenges and ethical considerationsin their use.2 Related WorkResearchers have
growing body of work on ST /SE skills assessment 27–32 . Together, these streams promise to educate innovative, “flexiblethinkers” capable of designing tomorrow’s complex products 6,33,34 .Curriculum-wide efforts to infuse ST / SE concepts are difficult. One challenge is that manyengineering faculty do not have a strong background in ST / SE fundamentals. These instructorsmay feel uncomfortable developing, delivering, and assessing ST / SE content in their courses. Asecond difficulty is that, similar to design and ethics education, multiple coordinated interventionsacross the curriculum provide better learning than a single standalone experience. Suchcurriculum-wide coordination requires the approval of a broad swath of faculty andadministrators
conclusions or recommendations expressedin this material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References[1] Austin Cory Bart, Dennis G. Kafura, Clifford A. Shaffer, and Eli Tilevich. Reconciling the promise and pragmatics of enhancing computing pedagogy with data science. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, Baltimore, MD, USA, February 21-24, 2018, pages 1029–1034, 2018.[2] Jeffrey S. Saltz, Neil I. Dewar, and Robert Heckman. Key concepts for a data science ethics curriculum. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018, Baltimore, MD, USA, February 21-24, 2018, pages
perspectives on actions they would take when performing poorly on an exam. Survey itemsincluded items on whether they would perform actions such as evaluating the reasons why itoccurred and strategizing next steps. These items slightly increased after completing the program(pre-M = 3.80, pre-SE = .184; post-M = 3.98, post-SE = .191).Research Skills and Knowledge: Overall, students’ understanding of research skills andknowledge such as proposal writing, presenting scientific work, research ethics, projectmanagement, usage of citations, data analysis, and problem solving increased (pre-M = 2.68,post-SE = .206; post-M = 3.88, post-SE = .236).Leadership and Teamwork Skills: Both before and after the program, students agreed thatoverall, they had
showcasing a cohesive theme across traditionally disparate undergraduate courses. Also, our team have endeavored to highlight critical topics such as ethics, sustainability, and resilience, all of which should increase the attractiveness of a CE engineering education to a broader spectrum of high- school students.This project is in progress and partial results are presented in this work-in-progress paper. Theproject aimed to evaluate the effectiveness of an educational video on multi-objectiveoptimization. Junior civil engineering students (n=38 students) at the second semester levelparticipated in this study, which involved a control group (n=24) and an experimental group(n=14). Participants were surveyed twice over a three
Virtual Annual Conference Content Access, Virtual On-line, 2020. [Online]. Available:10.18260/1-2—35561.[7] G. Townley, J. Katz, A. Wandersman, B. Skiles, M. J. Schillaci, B. E. Timmerman, and T. A.Mousseau, "Exploring the role of sense of community in the undergraduate transfer studentexperience," Journal of Community Psychology, vol. 41, pp. 277-290, 2013. [Online]. Available:https://doi.org/10.1002/jcop.21529.[8] B. Smith, Mentoring At-Risk Students through the Hidden Curriculum of Higher Education,Lanham, MD: Lexington Books, 2013.[9] M. Polmear, A. Bielefeldt, D. Knight, C. Swan, and N. Canney, "Hidden curriculumperspective on the importance of ethics and societal impacts in engineering education," ASEEVirtual Annual Conference Content
over a decade-and-a-half of industry experience within tech and education space as aFounder/Co-Founder, EdTech Professional and Advisor to companies, public and privateorganizations, Taiwo continues to establish himself as a forward-thinking innovator at the nexusof Engineering, AI and Education. His research interests include competency development andleveraging AI tools, technologies and methodologies to enhance ethical research and classroomengagement for advanced problem-solving. Taiwo has developed two pioneering frameworks forintegrating AI into qualitative research, which are currently under review for U.S. copyrightprotection.Varun Kathpalia, University of GeorgiaVarun is a PhD student in Engineering Education Transformations Institute
-hand controller trajectory to the robot trajectory, limiting therobot's movements in accordance with the geometry of the groove. These two observations demonstratethat the scenario-based project may be beneficial to the students by providing them with the learningopportunity to identify real-world challenges and adopt existing solutions or propose their own toaddress these challenges. 3) Ethical practices in user studies. Ethical practices are crucial for futureengineers and designers of artificial intelligence and robotic systems. The students were advised toconduct a user study to validate their system design. Before conducting any study involving humanparticipants, it is essential to secure approval from the Human Research Ethics Committee
. Stefano, “‘Mentoring is Ethical, Right?’: Women Graduate Students and Faculty in Science and Engineering Speak Out,” Int. J. Gend. Sci. Technol. Online, vol. 11, no. 1, pp. 108–133, 2019.[12] L. Anderson, K. Silet, and M. Fleming, “Evaluating and Giving Feedback to Mentors: New Evidence-Based Approaches,” Clin. Transl. Sci., vol. 5, no. 1, pp. 71–77, 2012, doi: 10.1111/j.1752-8062.2011.00361.x.[13] D. Feil-Seifer, A. Kirn, K. L. Stienhorst, and M. C. Parker, “WIP: Faculty Perceptions of Graduate Student Mental Health: A Productivity Framing,” in 2023 IEEE Frontiers in Education Conference (FIE), Oct. 2023, pp. 01–05. doi: 10.1109/FIE58773.2023.10343185.[14] B. L. Montgomery, J. E. Dodson, and S. M. Johnson, “Guiding
a “humanized socio-technical approach” that centers an ethical, social-justice paradigm in engineering education; training, evaluating and rewarding instructors for innovations in teaching and learning that meets the needs of students;• Broad and strategic collaborations that include industry, community, academia and accreditation partners that are created to cater to the specific needs, context and opportunities of engineering programs.The reports indicate that two broad factors are driving the need for these changes: (1) disruptivechanges in the sociotechnical landscape of engineering that are revolutionizing society andengineering at a breathtaking pace; and, (2) the continuing predominance of traditional
incorporating liberative pedagogies into a traditional technical engineering coursein thermodynamics. Riley discusses several course reforms suggested by liberal pedagogies andassesses those reforms. The reforms do bear some overlap with our study as they are ‘big ideas’rooted in a liberal arts context. Some examples of overlapping reforms include 1) creatingcommunity, 2) ethics, 3) de-centering Western civilization in the engineering classroom, and 4)problematizing science as objectivity and normalizing mistakes. Riley’s work succeeds inincorporating concepts from the liberal arts into engineering coursework for engineering students.Our project differs because we are trying to understand how engineering student curiosity can beleveraged to increase
bothscalable and personalized to individual needs. The incorporation of anonymity protocols ensuresthat personal data is protected, fostering trust among participants. However, challenges such asFigure 2: Model performance comparison for Perceived Stress Scale (PSS) label prediction. Thechart compares ROC-AUC and accuracy scores across various machine learning models.ensuring widespread adoption and addressing ethical considerations remain.The framework provides a forward-thinking approach for academic institutions seeking toimplement modern mental health support systems. It offers actionable, data-driven insights thatcan inform institutional policies and identify students who may be vulnerable to mental healthchallenges. Moreover, the system’s
ofuncertainty in the physical world. Once the activity results have been fully analyzed and asolution is obtained, the students must both verify and validate the solution. These concepts arememorable due to the engaging nature of the activity and produce an appreciation for historicalengineering methods as a resource. The analogue nature of the tool appears to improve theunderstanding and synthesis of the lesson, as opposed to memorizing a procedure.This paper provides a valuable and customizable lab activity for educators and curriculumdevelopers seeking to improve Freshman/Sophomore mechanical engineering lab courses. TheQuadrant Activity supports ABET learning outcomes 1 (solve complex engineering problems), 4(recognize ethical responsibility/make
,10.1093/jamia/ocae209. Challenges, and Ethical Considerations,” J Med Edu, vol. 22, no. 1, Jan. 2024, doi: 10.5812/jme-140890.[4] K. Gupta, R. Hajika, Y. S. Pai, A. Duenser, M. Lochner, and M.Billinghurst, “In AI We Trust: Investigating the Relationship between [9] F. Li and S. Betts, “(PDF) Trust: What It Is And What It Is Not,”Biosignals, Trust and Cognitive Load in VR,” in Proceedings of the 25th ResearchGate, Oct. 2024, doi: 10.19030/iber.v2i7.3825.ACM Symposium on Virtual Reality Software and Technology, in VRST ’19. [10