Paper ID #45510AI-Human Transference Learning and Assessment: Optimizing KnowledgeTransfer and Understanding through AI-Generated ContextualizationDr. Razvan Cristian Voicu, Robotics and Mechatronics Engineering, Kennesaw State University, Marietta,GA Dr. Razvan Cristian Voicu is a faculty member in the Department of Robotics and Mechatronics Engineering at Kennesaw State University. His research interests include artificial intelligence, robotics, and the development of AI-driven systems for knowledge transfer and adaptive learning. Dr. Voicu is dedicated to exploring innovative applications of AI to enhance learning and problem
Paper ID #45562Empowering Undergraduates with NLP: Integrative Methods for DeepeningUnderstanding through Visualization and Case StudiesNilanjana Raychawdhary, Auburn UniversityChaohui Ren, Auburn University [1] Mohamed, Abdallah. ”Designing a CS1 programming course for a mixed-ability class.” Proceedings of the western Canadian conference on computing education. 2019. [2] Shettleworth, Sara J. Cognition, evolution, and behavior. Oxford university press, 2009.Dr. Cheryl Seals, Auburn University Dr. Cheryl Denise Seals is a professor in Auburn University’s Department of Computer Science and Software Engineering. She
preparedness, compromising theirability to succeed and progress.This study introduces a new strategy to enhance retention by implementing a structured,proactive advising model that emphasizes early, personalized engagement between students andfaculty advisors. This advising framework prioritizes frequent and clear communicationregarding placement and academic progress through personalized emails, one-on-one Zoommeetings, and in-person advising sessions. Mathematics placement, given its foundational role inengineering curricula, serves as the centerpiece of this advising model. The framework providesstudents with academic planning support, fosters meaningful student-faculty interactions, andcultivates student ownership of their educational
engineering education, including a project introducing the humanities into environmental engineering education through a National Endowment for the Humanities grant. She has worked on projects to develop activities for K-12 students, and is the founder and director of the SWEET (Society of Women Engineers: Engineers in Training) Outreach Program at Mercer University, funded by the Engineering Information Foundation (EiF). Her research and educational work have most recently been funded by the NSF, U.S. EPA, and the EiF. She currently serves as the Co-Faculty Advisor for Mercer University’s Student Chapter of the Society of Women Engineers (SWE).Halley Elizabeth Smith, Mercer University ©American
Paper ID #45595Call to Action!Dr. Anna K. T. Howard, North Carolina State University at Raleigh Anna Howard is a Teaching Professor at NC State University in Mechanical and Aerospace Engineering where she has led the course redesign effort for Engineering Statics. She received her Ph.D. from the Rotorcraft Center of Excellence at Penn State University and is one of the campus leaders of Wolfpack Engineering Unleashed. She has launched and is currently chairing the College Teaching Committee for the NC State College of Engineering.Dr. Sally J. Pardue, Tennessee Technological University Sally Pardue, Ph.D., is an
future research as institutions are looking to use multiple assessment methods to moreprecisely evaluate their ABET SOs [10].Research GoalsThis study aims to examine (1) how students’ self-efficacy relates to their academic performanceas measured by GPA and FE exam scores, and (2) how academic performance may influencestudents’ perceptions of their abilities. Specifically, the study assesses the validity of indirectstudent assessments, using a survey given to outgoing senior students, to predict their mastery ofa subject through direct student assessments (i.e., FE exam data and cumulative GPA values).The study monitors how frequently students underrate or overrate their understanding of ABETSOs based on their measured performance. The goal is
was to score based on low,medium and high participation across all peer led team learning activities because participationdropped later in the course when students recognized the score in the class was high enough notto be impacted by reduced participation in the peer groups.Problem-solving through peer learning and group work allowed for an increase in engagement ofstudents with peers compared to simply memorizing and taking tests. Presenting students withchallenges that had to be solved through a small group systematic process helped studentsdevelop peer relationships beyond the instructor-student relationship. This was particularlyinsightful for best practices to support Veterans and active military adult learners that aretraditionally
critical thinking abilities they needto responsibly navigate and contribute to an AI-driven world.1. IntroductionArtificial Intelligence (AI) has become a transformative force across industries, redefining theworkforce and global problem-solving approaches, from healthcare innovations to environmentalsustainability efforts [1], [2]. Just like integrating computer science understanding and skills intothe curriculum has gained momentum in recent years, so is true for AI. Students need to betterunderstand how the technology works and how to use it properly. Despite the need for studentsto understand how AI works, disparities in Kindergarten through 12th grade (K-12) AI educationpersist. This leaves many students unprepared to navigate an AI
Learning in Cornerstone through Capstone Programs,” ASME International Design Engineering Technical Conferences and Computer in Engineering Conference, p. 143467, 2024.[16] G. P. Abbas Elamin, N. J. Washuta, James Righter, and K. Skenes, “Assigning Individualized Grades on a Team Capstone Project,” ASEE Southeast Section Conference Proceedings, 2024, doi: 10.18260/1-2--45507.[17] M. Leary and C. Burvill, “Enhancing the quality function deployment conceptual design tool,” Journal of Mechanical Design, vol. 129, no. 7, pp. 701–708, 2007, doi: 10.1115/1.2722787.[18] K. J. Ostergaard and J. D. Summers, “Development of a systematic classification and taxonomy of collaborative design activities,” Journal
Industry Academia ModelAbstractThis paper describes a collaborative industry-academia model for teaching medical devicedesign, which combines active learning with input from industry experts. The course coversinterdisciplinary topics such as biological testing, human factors, usability engineering, riskmanagement, and regulations, areas that go beyond the expertise of a single instructor. Industryprofessionals contribute through guest lectures, mentorship, and real-world case studies, ensuringthat students gain practical, industry-relevant knowledge. Students work on hands-on projectsthat simulate real-world scenarios, helping them develop critical thinking, teamwork, andproblem-solving skills. Active learning activities like sensor-based labs and
students'confidence in their ability to solve complex problems, collaborate with their peers, and developan understanding of computer science principles. In an effort to move beyond a solely screen-based experience, the camp incorporated "papercraft" activities, such as a printable binaryincrement Turing machine, logic gate “mazes” and paper versions of half and full adder circuits.These tangible tools provided a bridge between abstract concepts and concrete understanding,reinforcing key computational principles through literal hands-on learning.This paper examines the implementation and outcomes of the coding camp, focusing on theefforts made to enhance the virtual learning experience and their impact on student confidenceand skill development. By analyzing
, and working on communication skills [24],[25].As research discussed that undergraduate researchers could engage in their lived experiences formore authentic interpretations of data, we engaged our lived experiences, especially bothundergraduate researchers, through the writing of positionality statements. Positionalitystatements have been a growing phenomenon in engineering education research as part of theefforts to uncover researchers’ preconceived notions that can shape how they conduct theirresearch [26]. By reflecting on their positionalities, Alshanti and Thu as undergraduateresearchers engage their lived experiences as engineering students to interpret instructors’ beliefsand behaviors on test usage. This becomes the foundation of this
more accessible, with complex software becomingless of a barrier through tools such as Canva, Figma, Adobe Color, and Adobe Express. Thesetools provide well-designed templates, graphics, and AI features geared toward non-designers.Even if educators themselves grasp the importance of applying thoughtful graphic design tomultimedia instruction to prevent cognitive overload in students, how do they impress uponstudents to follow suit, especially if educators have no formal training or expertise in thefundamental principles of visual design? Importantly, how do educators then assess visual designelements if the topic is far outside their area of expertise?This work-in-progress paper describes electrical and computer engineering
, 2024. Retrieved from https://orchard-prod.azurewebsites.net/media/Framework/KEEN_ Framework_v5.pdf. 2. National Academy of Engineering. “Engineering of the future: Annual report,” 2019. Retrieved from https://www.nae.edu/File.aspx?id=237788. 3. The Lemelson Foundation. “The Engineering for One Planet Framework: Essential Sustainability-focused Learning Outcomes for Engineering Education,” 2022. Retrieved from https://engineeringforoneplanet.org/wp- content/uploads/2022_EOP_Framework_110922.pdf 4. D. Grasso, and M. Burkins. Holistic Engineering Education: Beyond Technology. Springer, 2010. 5. P. E. Arce, J. R. Sanders, A. Arce-Trigatti, L. Loggins, J. Biernacki, M. Geist, J. Pascal, and K. Wiant
: Curiosity, Connections, and Creating Value.We emphasize how these three components play a vital role in enhancing communication andcollaboration across disciplines, particularly within Foundry-guided activities. The secondcomponent describes preliminary work of student teams from a required second-year course in aNational Science Foundation National Research Traineeship (NSF-NRT) graduate levelprogram, which included 11 trainees. As part of this work, we showcase the outcomes of theirprojects, drawing connections to the three C's of the KEEN Mindset, with a specific focus onhow "Creating Value" is achieved through effective communication strategies.KeywordsRenaissance Foundry Model, KEEN Entrepreneurial Mindset, Holistic Professional, Foundry
as competent professionals, innovativethinkers, and problem solvers who can contribute more to the world while also succeeding intheir future endeavors. To aid this we have created a simple hands-on experiment that can beimplemented in the classroom: https://github.com/Hsuya01/Hands_On_CV_Exp.References1. Jesiek, B. K. (2013). The Origins and Early History of Computer Engineering in the United States. IEEE Annals of the History of Computing, 35(3), 6-18. https://doi.org/10.1109/MAHC.2013.22. Montoya, A. (2017). Computer Science for All: Opportunities Through a Diverse Teaching Workforce. Harvard Journal of Hispanic Policy, 29, 47-62. https://www.proquest.com/docview/1924502808?pq-origsite=gscholar&fromopenview=true&
-awareness, social skills, self-regulation, empathy, andmotivation is seen as art and lived out through practice [3], [5], [13]. The ability for aspiringproject engineers to hone EI, project management competencies, and understanding teamdevelopment can better equip them for the workplace and meet technical and interpersonal skillexpectations of employers.References[1] De Campos, D. B., de Resende, L. M., & Fagundes, A. “The importance of soft skills for the engineering,” Creative Education, 11, pp.1504-1520. 2020. https://doi.org.10.4236/ce.2020.118109[2] Kastberg, E., Buchko, A., & Buchko, K. “Developing emotional intelligence: The role of higher education,” Journal of Organizational Psychology 20(3), 2020. pp.64-72. https
civilinfrastructure workforce (either through a university or from any other educational background);and 2) the perception and base knowledge of civil infrastructure within the public at large. Thispaper summarizes these videos while accomplishing the following objectives: 1) assessing theireffectiveness relative to the two potential impact categories; and 2) describing ongoing videoplans that make use of the assessment’s findings.An abbreviated review of literature, with associated author commentary, related to the use ofvideos in education is provided in the next section. Thereafter, the videos themselves aredescribed before presenting the assessments. These assessments were in the form of fourquestionnaires/surveys given over a few year period beginning
too early to discernif there will be a complete paradigm shift, but there is certainly a need for greater exploration.References[1] N. Chomsky, I. Roberts, and J. Watumull. “The False Promise of ChatGPT.” The New York Times. March 8,2023. [Online][2] E. Mollick. Co-Intelligence: Living and Working with AI. Portfolio/Penguin, 2024.[3] K. A. Neeley and H. C. Leugenbiehl. “Beyond Inevitability: Emphasizing the Role of Intention and Ethical Responsibility in Engineering Design,” Philosophy and Design. Springer 2008.[4] K. Shelton and D. Lanier. The Promises and Perils of AI in Education: Ethics and Equity Have Entered the Chat. Lanier Learning, 2024.[5] B. McMurtrie. “Cheating Has Become Normal: Faculty members are
supportive environment.Our methodology involves four key elements: 1. Structured Lab Sessions: Students participate in 50-minute lab sessions that complement lecture topics. 2. Project-Based Learning: Projects such as LinkedSet are designed to reinforce concepts like interfaces, generics, and OOP. 3. Interactive Guidance: Instructors demonstrate key coding techniques and guide students through implementation challenges. 4. Learning Cycles: To maintain student engagement and balance stress levels in teacher- guided project-based labs, we implemented a learning cycle mechanism. This cycle consists of teacher-guided learning, self-paced code review, flipped classroom practices [3], and quiz- based
. (2012). Engagement in classroom learning: Creating temporal participation incentives for extrinsically motivated students through bonus credits. Journal of Education for Business, 87(2), 86-93. https://doi.org/10.1080/08832323.2011.570808[15] Guerrero, M., & Rod, A. B. (2013). Engaging in office hours: A study of student-faculty interaction and academic performance. Journal of Political Science Education, 9(4), 403-416. https://doi.org/10.1080/15512169.2013.835554[16] Trowler, V. (2010). Student engagement literature review. The higher education academy, 11(1), 1-15.[17] Schinske, J., & Tanner, K. (2014). Teaching more by grading less (or differently). CBE—Life Sciences Education, 13(2), 159-166. https://doi.org