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

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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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 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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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 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 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

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

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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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 7: Natural Language Processing and LLM Applications

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alexis Frias, University of California Merced; Shrivaikunth Krishnakumar, San Jose State University; Ayush Pandey, University of California Merced

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

nonlinear systems, and the development of AI-powered auto grading system. His broader research background includes contributions to robotics, agricultural technologies, and education, with involvement in research programs such as LINXS, MACES, and NCAS. He is passionate about AI development and mentoring, aiming to contribute to the advancement of trustworthy and efficient AI systems.Shrivaikunth Krishnakumar, San Jose State University Shrivaikunth Krishnakumar is a Graduate Student in the Master of AI program at San Jose State University. He graduated from UC Merced with a bachelor’s degree in Computer Science and Engineering. His current work focuses on development of AI-driven educational tools, and combating AI

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Chuhao Wu, Pennsylvania State University; Sarah Zipf, Pennsylvania State University; Na Li, Penn State University; David Benjamin Hellar, The Pennsylvania State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #47715Data-Informed instruction: pedagogical responses and obstacles in using learninganalyticsMr. Chuhao Wu, Pennsylvania State University Chuhao Wu is a Ph.D. candidate in Informatics at the Pennsylvania State University. He holds a master’s degree in Industrial Engineering from Purdue University. His research focuses on the human-centered design of educational technology and the application of artificial intelligence in higher education.Sarah Zipf, Pennsylvania State University Sarah Zipf, Ph.D. is a researcher with Teaching and Learning with Technology at the Pennsylvania State University.Ms. Na Li, Penn State

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Paromita Nath, Rowan University; Melanie Amadoro, Rowan University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #48484Quantifying the Effects of Concept Maps on Student LearningDr. Paromita Nath, Rowan University Dr. Paromita Nath is an Assistant Professor in Mechanical Engineering at Rowan University. She earned her Ph.D. in Civil Engineering from Vanderbilt University. She is passionate about advancing engineering education through machine learning and data analysis, building on her expertise in uncertainty quantification, Bayesian inference, process design and control under uncertainty, and probabilistic digital twin. Her research spans diverse applications, including additive manufacturing and public health.Ms. Melanie

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Carrie Grace Aponte, Kansas State University; Safia Malallah, Kansas State University; Lior Shamir, Kansas State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Figure 1. Figure 1: Diagram of Paper FilteringKeywords, Databases, and Criteria The keywords used in the database queries (”Tools” Or”Technology” Or ”Resources” Or Pedagogy” Or ”Curriculum”) AND (”K-12” Or ”MiddleSchool” Or ”High School” or ”Elementary School” or ”Primary School” or ”Children”) AND”Data Science Education”. The search engines used were Google Scholar, IEEE Xplore, ACM,and K-State Libraries Search. The source types were limited to scholarly journals and conferencepapers or proceedings. Masters and doctoral dissertations were excluded, and the scope wasfurther narrowed to studies conducted in the United States within the last 10 years.ResultsOur general research questions are as follows: • RQ1 What

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alyson Grace Eggleston, Pennsylvania State University; Robert J. Rabb P.E., The Pennsylvania State University; Eric Donnell, The Pennsylvania State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

. Vorvoreanu, and K. Madhavan, “Using visualization to derive insights from research funding portfolios,” in IEEE Computer Graphics and Applications, 35(3), 91-c3, 2021.[2] L. Shaulska, L. Yurchyshena, and Y. Popovskyi, “ Using MS power BI tools in the university management system to deepen the value proposition,” in 2021 11th International Conference on Advanced Computer Information Technologies (ACIT), pp. 294-298, IEEE, September 2021.[3] M. D. Tamang, V. K. Shukla, S. Anwar, and R. Punhani, “Improving business intelligence through machine learning algorithms,” In 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), pp. 63-68, IEEE, April 2021.[4] R. Heyard and H. Hottenrott, “The value of

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Karl D. Schubert FIET, University of Arkansas; Carol S Gattis, University of Arkansas; Stephen R. Addison, University of Central Arkansas; Tara Jo Dryer, University of Arkansas; Adam Musto, Arkansas Department of Education

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

by helping develop the next generation of STEM workforce. He has patents in various technology areas and is the author and co-author of several books. Dr. Schubert is a Senior Member of the IEEE, Senior Member of ACM, and Senior Member of IISE. He is also Vice Chair of the Ozark Section of the IEEE Computer Society and is the ASEE Data Science & Artificial Intelligence (DSAI) Constituent Delegate to the Commission on P-12 Engineering Education (CP12) and the DSAI Delegate to the Interdivisional Town Hall.Dr. Carol S Gattis, University of Arkansas Carol S. Gattis is an Associate Dean Emeritus and Adjunct Associate Professor at the University of Arkansas. She has over 34 years of experience in STEM education

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Declan Kirk Bracken, University of Toronto; Sinisa Colic Ph.D., University of Toronto

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #45749Automating Structured Information Extraction from Images of AcademicTranscripts Using Machine LearningDeclan Kirk Bracken, University of Toronto Declan Bracken is an M.Eng. student at the University of Toronto in the department of Mechanical and Industrial Engineering pursuing an emphasis in Analytics. This paper is the final product of an 8 month M.Eng. project supervised by Professor Sinisa Colic and it’s work is intended for implementation into the admissions process at the University of Toronto’s M.I.E department.Dr. Sinisa Colic Ph.D., University of Toronto Dr. Colic is an Assistant Professor

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kristina A Manasil, The University of Arizona; Gregory L. Heileman, The University of Arizona; Melika Akbarsharifi, The University of Arizona; Roxana Akbarsharifi, The University of Arizona; Aryan Ajay Pathare, The University of Arizona

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

am responsible for developing tools and applications to improve student outcomes and support their success. My research interests include software engineering, machine learning, and data analytics. I am passionate about using these technologies to solve complex problems and make data-driven decisions.Aryan Ajay Pathare, The University of Arizona A Master’s student in Computer Science at the University of Arizona. His interests lie in software development, cloud computing, and machine learning. ©American Society for Engineering Education, 2025 The Graduation Project: Leveraging Data-Driven Interventions to Support Near Completers Kristi Manasil

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ananya Prakash, Virginia Polytechnic Institute and State University; Mohammed Seyam, Virginia Polytechnic Institute and State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Engineering, Human-Computer Interaction, and Computer Science Education. Additionally, he is the CS Department Coordinator for Experiential Learning, where he leads several initiatives to enhance students’ learning through out-of-classroom experiences, including the CS Study Abroad program. Mohammed has 20+ years of experience in teaching university level courses, and he presented and conducted multiple talks and workshops in different countries. Among other courses, he taught: Software Engineering, Database Systems, Usability Engineering, and Software Project Management. ©American Society for Engineering Education, 2025 Can AI Transform Graduate Computer Science Admissions

Conference Session

DSAI Technical Session 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Julie M. Smith; Jacob Koressel; Sofia De Jesus, Carnegie Mellon University; Joseph W Kmoch; Bryan Twarek

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46182Can LLMs Assist with Education Research? The Case of Computer ScienceStandards AnalysisDr. Julie M. Smith Dr. Julie M. Smith is a senior education researcher at the Institute for Advancing Computing Education. She holds degrees in Software Development, Curriculum & Instruction, and Learning Technologies. Her research focus is computer science education, particularly the intersection of learning analytics, learning theory, and equity and excellence. She was a research assistant at MIT’s Teaching Systems Lab, working on a program aimed at improving equity in high school computer science programs; she is