Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

Brainerd Prince, Plaksha University; Siddharth Siddharth, Plaksha University; Subham Jalan; Hibah Ihsan Muhammad, Plaksha University, Punjab; Chaitanya Modi

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #47576AI-Driven Multimodal System for Enhancing Non-Verbal Communication inPublic SpeakingDr. 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 Tutor

Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nandan Reddy Muthangi, University of Toledo; Ananya Singh, The University of Toledo

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

of Engineering program in Materials Science and Engineering in Fall 2025.Ananya Singh, The University of Toledo Ananya Singh is a Bachelor’s student at the University of Toledo, majoring in Computer Science and Engineering. She is an undergraduate research assistant at the RIM Lab, where her research focuses on machine learning and its applications in IoT. Her work includes integrating AI with IoT systems to develop innovative solutions for real-world problems such as wildfire detection, where she led the development of drone-based sensing systems and predictive analytics for early fire alerts. Ananya has co-authored research paper in the areas of explainable AI, autonomous systems, and drone technology. She

Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

Handan Liu, Northeastern University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46592A Unique Course Designed for Graduate Students: Integrating High-PerformanceParallel Computing into Machine Learning and Artificial IntelligenceDr. Handan Liu, Northeastern University Handan Liu is a Full Teaching Professor of Multidisciplinary Master of Science (MS) programs (Software Engineering, Data Architecture, Information Systems) in the College of Engineering at Northeastern University. Her research interests include heterogeneous high-performance computing, programming structure and algorithms, machine learning and AI, NLP research and development, LLM reasoning and AI agent in engineering courses

Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

D. Matthew Boyer, Clemson University; Lukas Allen Bostick, Clemson University; Ibrahim Demir, The University of Iowa; Bijaya Adhikari; Krishna Panthi, Clemson University; Vidya Samadi, Clemson University; Mostafa Saberian, Clemson University; Carlos Erazo Ramirez, The University of Iowa

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #48109WIP: Formative Findings from the First Year Implementation of a Water andData Science WorkshopDr. D. Matthew Boyer, Clemson University Dr. D. Matthew Boyer is a Research Associate Professor of Engineering & Science Education and an Educational Proposal Writer in the College of Engineering, Computing and Applied Sciences at Clemson University.Lukas Allen Bostick, Clemson UniversityProf. Ibrahim Demir, The University of IowaBijaya AdhikariKrishna Panthi, Clemson UniversityVidya Samadi, Clemson UniversityMostafa Saberian, Clemson UniversityCarlos Erazo Ramirez, The University of Iowa

Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

Dong Hun Lee, Purdue University at West Lafayette (COE); Anne M Lucietto, Purdue University at West Lafayette (PPI); Diane L Peters P.E., Kettering University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46512Enhanced Scene Recognition and Object Detection for Autonomous DrivingEnvironments Using Machine Learning ”Work in Progress” (WIP)Dong Hun Lee, Purdue University at West Lafayette (COE)Dr. Anne M Lucietto, Purdue University at West Lafayette (PPI) Dr. Lucietto has focused her research in engineering technology education and the understanding of engineering technology students. She teaches in an active learning style which engages and develops practical skills in the students.Dr. Diane L Peters P.E., Kettering University Dr. Peters is an Associate Professor of Mechanical Engineering at Kettering University

Conference Session

DSAI Technical Session 7: Natural Language Processing and LLM Applications

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kaiwen Guo, New York University Tandon School of Engineering; Malani Snowden, New York University Tandon School of Engineering; Rui Li, New York University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

statements or words written in human languages [2]. Foundationaltheories by scholars such as Schank (on conceptual dependencies) and Chomsky (on syntax)paved the way for modern NLP, highlighting the complexities of semantics, morphology, andpragmatics [3][4]. More recently, advancements in NLP toolkits and libraries—such asTextBlob—have made sentiment analysis and text classification accessible, thereby enablingmore nuanced, context-sensitive applications [5][6][7].In tandem with these technological advances, large language models (LLMs) and prompt-engineering strategies have become increasingly prevalent, revealing new possibilities andchallenges in text generation, reasoning, and named entity recognition [8][9][10][13]. Forinstance, NER can parse

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gregory L. Heileman, The University of Arizona; Chaouki T Abdallah, Georgia Institute of Technology; Kristina A Manasil, The University of Arizona; Melika Akbarsharifi, The University of Arizona; Roxana Akbarsharifi, The University of Arizona

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #48858Are Engineering Degrees Really More Complex? Characterizing the Complexitiesof Academic Programs by DisciplineProf. Gregory L. Heileman, The University of Arizona Gregory (Greg) L. Heileman currently serves as the Associate Vice Provost for Academic Administration and Professor of Electrical and Computer Engineering at the University of Arizona, where he is responsible for facilitating collaboration across campus tProf. Chaouki T Abdallah, Georgia Institute of Technology ˜ Chaouki T. Abdallah started his college education at the Ecole SupA

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ashraf Badir, Florida Gulf Coast University; Ahmed S. Elshall, Florida Gulf Coast University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #45388Data Science in Environmental Engineering CurriculumProf. Ashraf Badir, Florida Gulf Coast University Dr. Badir is a Professor in the Bioengineering, Civil Engineering, and Environmental Engineering Department at the U.A. Whitaker College of Engineering in Florida Gulf Coast University. He earned his B.Sc. (1982) in Civil Engineering and M.Sc. (1985) in Structural Engineering. He also holds a M.Sc. (1989) and a Ph.D. (1992) in Aerospace Engineering from Georgia Institute of Technology. Dr. Badir is a licensed Professional Engineer in Florida, and a civil engineering program evaluator for ABET.Ahmed S. Elshall

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Elizabeth Milonas, New York City College of Technology; Qiping Zhang, Long Island University; Duo Li, Shenyang Institute of Technology

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

ASIST&T, and his research interests are focused on Human-Computer-Interaction, Big Data, and Data Analytics. ©American Society for Engineering Education, 2025 Shaping Future Innovators: A Curriculum Comparison of Data Science Programs in Leading U.S. and Chinese InstitutionsASEE submission:Data Science & Analytics Constituent Committee (DSA)1. IntroductionThe Data Science field has been evolving rapidly both in the United States and in China in recentyears. More and more day-to-day and business applications are depending on data sciencetechnologies such as data mining, machine learning, data management, and artificial intelligence[1]. With this rise in such data science technologies and

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Cristian Saavedra-Acuna, Universidad Andres Bello, Concepcion, Chile; Monica Quezada-Espinoza, Universidad Andres Bello, Santiago, Chile; Danilo Alberto Gomez, Universidad Andres Bello, Concepcion, Chile

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46172A Predictive Model for Academic Performance in Engineering StudentsMs. Cristian Saavedra-Acuna, Universidad Andres Bello, Concepcion, Chile Cristian Saavedra is an assistant professor at the School of Engineering at the University Andres Bello in Concepcion, Chile. He holds a bachelor’s degree in Electronics Engineering and a master’s degree in Technological Innovation and Entrepreneurship. Cristian is certified in Industrial Engineering, University Teaching, Online Hybrid and Blended Education, and Entrepreneurship Educators. He teaches industrial engineering students and carries out academic management

Conference Session

DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction

Collection

2025 ASEE Annual Conference & Exposition

Authors

Robert J. Rabb P.E., Pennsylvania State University; Ivan E. Esparragoza, Pennsylvania State University; Jennifer X Wu

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #48405Data Analytics for Engineering Student Success and College OperationsDr. Robert J. Rabb P.E., Pennsylvania State University Robert Rabb is the associate dean for education in the College of Engineering at Penn State. He previously served as a professor and the Mechanical Engineering Department Chair at The Citadel. He previously taught mechanical engineering at the United States Military Academy at West Point. He received his B.S. in Mechanical Engineering from the United Military Academy and his M.S. and PhD in Mechanical Engineering from the University of Texas at Austin. His research and teaching

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Michael T Johnson, University of Kentucky; Johné M Parker, University of Kentucky

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Computer Science Engineering and Engineering with Electrical Concentration from LeTourneau University in Longview, TX.Prof. John´e M Parker, University of Kentucky John´e M. Parker is an Associate Professor of Mechanical Engineering and Associate Dean of Access and Community Engagement in the Pigman College of Engineering at the University of Kentucky. She received her BME, MSME and Ph.D. degrees from the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. ©American Society for Engineering Education, 2025Engineering Student Success based on Performance in First Semester Foundational CoursesAbstractStudent success in engineering programs is known to

Conference Session

DSAI Technical Session 4: Workshops, Professional Development, and Training

Collection

2025 ASEE Annual Conference & Exposition

Authors

Olatunde Olu Mosobalaje, Covenant University; Moses Olayemi, The University of Oklahoma

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

the 2025 Spring Cohort of the prestigious ’Empowering-the-Teachers’ (ETT) fellowship, at the Massachusetts Institute of Technology (MIT).Dr. Moses Olayemi, The University of Oklahoma Moses Olayemi is an Assistant Professor of Engineering Pathways at the University of Oklahoma. He is the Founding President of the African Engineering Education Fellows in the Diaspora, a non-governmental organization that leverages the experiences of African scholars in engineering education to inform and support engineering education policy, practice, and pedagogies in Africa. His research revolves around the professional development of STEM educators and researchers in low-resource contexts for which he employs culturally relevant

Conference Session

DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Collection

2025 ASEE Annual Conference & Exposition

Authors

Taiwo Raphael Feyijimi, University of Georgia; VARUN KATHPALIA, University of Georgia; Sarah Jane Bork, University of Georgia

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #48217Mapping Essential Competencies for Entry-Level Electrical Engineers: AHybrid NLP and Thematic Analysis StudyTaiwo Raphael Feyijimi, University of Georgia Taiwo is a highly skilled AI Engineer, Researcher, and Doctoral Student at the University of Georgia who completed his MS in Electrical and Computer Engineering in the College of Engineering. He is currently leveraging AI to tackle simple and longstanding problems in engineering education. With over a decade of industry experience as a Technology Strategist and Technical Lead, he has established himself as a forward-thinking innovator in AI and EdTech

Conference Session

DSAI Technical Session 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Pallavi Singh, University of South Florida; Joel Howell; Joshua Karl Thomas Ranstrom, University of South Florida; Wilfrido A. Moreno P.E., University of South Florida

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

this vision byenabling adaptive, scalable frameworks that simulate educational systems and facilitate person-alized, competency-based learning experiences[9][12].These methods resonate with the PFEprogram’s objectives of leveraging technology-driven innovations to enhance career readinessand competency development.The Professional Formation of Engineers (PFE) program at the University of South Florida(USF) exemplifies this philosophy, providing students with a structured pathway to developcritical competencies essential for engineering careers. Originally developed as part of anNSF/RED award [5], the PFE program has evolved over several years, addressing gaps inengineering education by introducing an individualized PFE Qualification Plan (QP

Conference Session

DSAI Technical Session 4: Workshops, Professional Development, and Training

Collection

2025 ASEE Annual Conference & Exposition

Authors

yilin zhang, University of Florida; Bruce F. Carroll, University of Florida; Jinnie Shin, University of Florida; Kent J. Crippen, University of Florida

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #48016PEER HELPER (Peer Engagement for Effective Reflection, Holistic EngineeringLearning, Planning, and Encouraging Reflection) Automated Discourse AnalysisFrameworkyilin zhang, University of FloridaDr. Bruce F. Carroll, University of Florida Dr. Carroll is an Associate Professor of Mechanical and Aerospace Engineering at the University of Florida. He holds an affiliate appointment in Engineering Education. His research interests include engineering identity, self-efficacy, and matriculation of Latine/x/a/o students to graduate school. He works with survey methods and overlaps with machine learning using

Conference Session

DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Collection

2025 ASEE Annual Conference & Exposition

Authors

Juan Alvarez, University of Illinois at Urbana - Champaign; Max Fowler, University of Illinois at Urbana - Champaign; Jennifer R Amos, University of Illinois at Urbana - Champaign; Yael Gertner, University of Illinois at Urbana - Champaign

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46194WIP: Students’ metacognition and how it relates to their performance inconceptual problem-solving introductory Engineering courses.Juan Alvarez, University of Illinois at Urbana - Champaign Juan Alvarez joined the Department of Electrical and Computer Engineering at University of Illinois faculty in Spring 2011 and is currently a Teaching Assistant Professor. Prior to that, he was a Postdoctoral Fellow in the Department of Mathematics and Statistics at York University, Canada, a Postdoctoral Fellow in the Chemical Physics Theory Group at the University of Toronto, Canada, and a Postdoctoral Fellow in the

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ibukun Samuel Osunbunmi, Pennsylvania State University; Taiwo Raphael Feyijimi, University of Georgia; Lexy Chiwete Arinze, Purdue University at West Lafayette (COE); Viyon Dansu, Florida International University; Bolaji Ruth Bamidele, Utah State University; Yashin Brijmohan, Utah State University; Stephanie Cutler, The Pennsylvania State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

STEM education, sustainable energy, and material characterization.Taiwo Raphael Feyijimi, University of Georgia Taiwo is a highly skilled AI Engineer, Researcher, and Doctoral Student at the University of Georgia who completed his MS in Electrical and Computer Engineering in the College of Engineering. He is currently leveraging AI to tackle simple and longstanding problems in engineering education. With over a decade of industry experience as a Technology Strategist and Technical Lead, he has established himself as a forward-thinking innovator in AI and EdTech. His expertise spans Exploratory Data Analysis (EDA), Machine Learning (ML), Natural Language Processing (NLP), and Prompt Engineering Techniques (PETs) with

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiang Zhao, Alabama A&M University; Mebougna Drabo, Alabama A&M University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

, Additive Manufacturing, Thermoelectric Devices for Energy Harvesting, Digital Twinning Technology, Nuclear Radiation Detectors, Nuclear Security and Safety, Small Nuclear Modular Reactors (SMR), Material Characterization (X-ray Photoelectron Spectroscopy & Infrared Microscopy), Nanotechnology, Data Analytics and Visualization, Biofuels Applications, Computational Fluid Dynamics analysis, Heat Transfer, Energy Conservation in building, and Multi Fuel Optimization. ©American Society for Engineering Education, 2025 2025 ASEE Annual Conference and Exposition Enhancing Data Science Education for Critical Infrastructure Security with Project-Based

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiaoning Jin; Sagar Kamarthi, Northeastern University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #48061Personalized Learning Paths: LLM-Based Course Recommendations in ManufacturingEducationProf. Xiaoning Jin Prof. Xiaoning (Sarah) Jinˆa C™s research focus is in the area of modeling and analysis for intelligent and advanced manufacturing processes and systems, with a specialization in diagnostics and prognostics (D&P), control and predictive decision making.Dr. Sagar Kamarthi, Northeastern University Sagar Kamarthi is a Professor of Mechanical and Industrial Engineering and the Founding Director of the Data Analytics Engineering Program at Northeastern University, Boston. He received his MS and Ph.D

Conference Session

DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Faiza Zafar, Rice University; Carolyn Nichol, Rice University; Matthew Cushing, Rice University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

(Research Experience for Teachers) program is a 6-week, paidvirtual summer internship designed to immerse computer science, technology, and programmingteachers in discovery-based STEM research. Funded by the NSF Engineering Research Centerentitled Precise Advanced Technologies and Health Systems for Underserved Populations(PATHS-UP; EEC-1648451) and NSF Expeditions in Computing grant entitled Seeing Underthe Skin (NSF #: CCF-1730574), the SWITCH RET program offers teachers the opportunity togain hands-on experience in computer science, engineering, and health technologies. Theprogram aims to enhance teachers’ understanding of STEM fields, provide them with researchexperience, and help them develop classroom-ready lessons to inspire students to

Conference Session

DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction

Collection

2025 ASEE Annual Conference & Exposition

Authors

Selena Johnson, Rowan University; Paromita Nath, Rowan University; Smitesh Bakrania, Rowan University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

interviews are greatly appreciated for making this researchpossible.References [1] K. Mangaroska and M. Giannakos, “Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning,” IEEE Transactions on Learning Technologies, vol. 12, no. 4, pp. 516–534, 2018. [2] D. B. Knight, C. Brozina, and B. Novoselich, “An investigation of first-year engineering student and instructor perspectives of learning analytics approaches.” Journal of Learning Analytics, vol. 3, no. 3, pp. 215–238, 2016. [3] O. Talbi and A. Ouared, “Goal-oriented student motivation in learning analytics: How can a requirements-driven approach help?” Education and Information Technologies, vol. 27, no. 9, pp. 12

Conference Session

DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction

Collection

2025 ASEE Annual Conference & Exposition

Authors

Clara Fang, University of Hartford

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #47138Data-Driven Research Experience for Undergraduate StudentsDr. Clara Fang, University of Hartford ©American Society for Engineering Education, 2025 Data-Driven Research Experience for Undergraduate StudentsABSTRACTData analysis is essential to modern engineering systems and processes. With advancedcomputational tools, large datasets can be stored, processed, and analyzed to uncover keycharacteristics and trends. Developing the ability to make data-driven inferences and predictionsis a crucial skill for today’s engineering students. This paper discusses the integration ofinnovative Artificial Intelligence (AI

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Indu Varshini Jayapal, University of Colorado Boulder; James KL Hammerman; Theodora Chaspari, University of Colorado Boulder

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #47825Expanding AI Ethics in Higher Education Technical Curricula: A Study onPerceptions and Learning Outcomes of College StudentsMiss Indu Varshini Jayapal, University of Colorado BoulderJames KL HammermanDr. Theodora Chaspari, University of Colorado Boulder Theodora Chaspari is an Associate Professor in Computer Science and the Institute of Cognitive Science at University of Colorado Boulder. She has received a B.S. (2010) in Electrical & Computer Engineering from the National Technical University of Athens, Greece and M.S. (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern

Conference Session

DSAI Technical Session 7: Natural Language Processing and LLM Applications

Collection

2025 ASEE Annual Conference & Exposition

Authors

Mikayla Friday, University of Connecticut; Michael Thomas Vaccaro Jr, University of Connecticut; Arash Esmaili Zaghi P.E., University of Connecticut

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #49192Leveraging Large Language Models for Early Study Optimization in EducationalResearchMikayla Friday, University of ConnecticutMr. Michael Thomas Vaccaro Jr, University of Connecticut Michael Vaccaro is a fourth-year Ph.D. student in the School of Civil and Environmental Engineering at the University of Connecticut. He received his Bachelor of Science in Civil Engineering from the University of Connecticut in 2021. In addition to his work in structural engineering, Michael’s interests in teaching and learning have inspired him to pursue interdisciplinary research spanning the fields of engineering

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Lauren Singelmann, Minnesota State University, Mankato; Jack Elliott, Minnesota State University, Mankato; Yuezhou Wang, Minnesota State University, Mankato; Jacob John Swanson, Minnesota State University, Mankato

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Utah State University as an NSF Graduate Research Fellow. His research includes student social support networks in engineering education, experimental fluid dynamics, and developing low-cost technology-based tools for improving fluid dynamics education.Dr. Yuezhou Wang, Minnesota State University, Mankato Dr. Yuezhou Wang is an associate professor in both Iron Range Engineering and Twin Cities Engineering programs. He received his B.S. in Mechanical Engineering from Shanghai Jiaotong University, China (2008) and Ph.D. in Materials Science and Engineering from University of Minnesota, Twin Cities (2017). His leading teaching competencies are in areas of materials science, structural analysis, finite element modeling

Conference Session

DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Collection

2025 ASEE Annual Conference & Exposition

Authors

Majd Khalaf, Norwich University; Toluwani Collins Olukanni, Norwich University; David M. Feinauer P.E., Virginia Military Institute; Michael Cross, Norwich University; Ali Al Bataineh, Norwich University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Electrical and Computer Engineering at Norwich University teaching classes in the areas of circuits, electronics, energy systems, and engineering design. His research interest is in energy systems, specifically battery electric vehicles and their impact on the electric grid. Cross received degrees from the Rochester Institute of Technology and the University of Vermont.Ali Al Bataineh, Norwich University ©American Society for Engineering Education, 2025 Future-Ready Students: Validating the Use of Natural Language Processing to Analyze Student ReflectionsIntroductionFirst-year Electrical and Computer Engineering (ECE) students from Norwich University andVirginia Military Institute

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Md. Yunus Naseri, Virginia Polytechnic Institute and State University; Vinod K. Lohani, Virginia Polytechnic Institute and State University; Manoj K Jha P.E., North Carolina A&T State University; Gautam Biswas, Vanderbilt University; Caitlin Snyder; Steven X. Jiang, North Carolina A&T State University; Caroline Benson Sear, Virginia Polytechnic Institute and State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #45880Integration of Data Science Modules Across Interdisciplinary Courses at MultipleInstitutions: Analysis of Students’ and Faculty PerspectivesMr. Md. Yunus Naseri, Virginia Polytechnic Institute and State University Yunus Naseri is a Ph.D. candidate in the Department of Civil and Environmental Engineering at Virginia Tech (VT). He joined VT as a master’s degree student through a Fulbright Scholarship in 2018. His research focuses on data science literacy integration across STEM+C disciplines and data science application in water use across different economic sectors.Dr. Vinod K. Lohani, Virginia Polytechnic

Conference Session

DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sri Krishna Chaitanya Velamakanni, Pennsylvania State University; Suman Saha, Pennsylvania State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #47854Snackable Study: Boosting Micro-learning with Bite-Size VideosSri Krishna Chaitanya Velamakanni, Pennsylvania State University Sri Krishna Chaitanya is a Software Engineer at Walmart, where he focuses on enterprise backend payment applications. He holds a Master’s degree in Computer Science from The Pennsylvania State University. His research interests lie at the intersection of Natural Language Processing (NLP), financial technology (fintech), and the applications of Large Language Models (LLMs).Suman Saha, Pennsylvania State University Suman Saha received a Ph.D. degree in computer science from Pierre and

Conference Session

DSAI Technical Session 7: Natural Language Processing and LLM Applications

Collection

2025 ASEE Annual Conference & Exposition

Authors

Suman Saha, Pennsylvania State University; Fatemeh Rahbari, The Pennsylvania State University; Farhan Sadique, Kansas State University; Sri Krishna Chaitanya Velamakanni, Pennsylvania State University; Mahfuza Farooque, Pennsylvania State University; William J. Rothwell, Penn State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

substantial contributions to dependable and secure computing during his doctoral studies. He has held positions at several prestigious National and International Research Labs, including Microsoft, Cambridge, U.K., Harvard University, Cambridge, USA, and the National Institute for Research in Digital Science and Technology (Inria), Paris, France.Fatemeh Rahbari, The Pennsylvania State University Fatemeh Rahbari is currently pursuing an MSc in Computer Science and Engineering (CSE) at The Pennsylvania State University (Penn State). With a research background spanning image processing and data privacy, Fatemeh’s recent work focused on leveraging artificial intelligence (AI) for generating microlearning materials, including