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

Paper ID #46964Enhancing Data Science Education for Critical Infrastructures with Project-BasedLearningDr. Xiang Zhao, Alabama A&M University Dr. Xiang (Susie) Zhao, Professor in the Department of Electrical Engineering and Computer Science at the Alabama A&M University, has over 20 years of teaching experience in traditional on-campus settings or online format at several universities in US and aboard. Her teaching and research interests include programming languages, high performance algorithm design, data science, and evidence-based STEM teaching pedagogies. Her recent research work has been funded by DOE, USED, NASA

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

Paper ID #48861Graduation Project: Using Student Progress Tracking Analytics to ImproveGraduation RatesKristina A Manasil, The University of Arizona Kristi Manasil is a second-year PhD student in the College of Information Science at the University of Arizona. She received her bachelor’s degree in Computer Science from the University of Arizona. Her areas of focus are data visualization, machine learning, learning analytics and educational data mining.Prof. Gregory L. Heileman, The University of Arizona Gregory (Greg) L. Heileman currently serves as the Associate Vice Provost for Academic Administration and Professor of

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #49201Explore the Possibility of Monitoring Project Member Interactions UsingNatural Language ProcessingKaiwen Guo, New York University Tandon School of Engineering Computer Science Senior at New York UniversityMalani Snowden, New York University Tandon School of EngineeringProf. Rui Li, New York University Dr. Li earned his master’s degree in Chemical Engineering in 2009 from the Imperial College of London and his doctoral degree in 2020 from the University of Georgia, College of Engineering. ©American Society for Engineering Education, 2025 Explore the Possibility of Monitoring Project Member

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46681Future-Ready Students: Validating the Use of Natural Language Processingto Analyze Student Reflections on a Remote Learning Group ProjectMajd Khalaf, Norwich University Majd Khalaf recently graduated from Norwich University with a Bachelor’s degree in Electrical and Computer Engineering, along with minors in Mathematics and Computer Science. He is passionate about DevOps, embedded systems, and machine learning. Throughout his academic career, Majd contributed to various projects and research in natural language processing (NLP) and computer vision. He served as a Senior AI Researcher at Norwich University’s

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Neel Manmohan Parekh, University of Florida; Kevin Scroggins, University of Florida; Yolanda Gil, University of Southern California; Emmanuel J Dorley, University of Florida

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

understandthe competency of their team and the limits of the team as well as general appropriate goals whenscoping a project. These findings contribute to the growing knowledge of how to effectively teachand apply CPS skills, providing a promising pathway for improving science students’ ability towork well in groups.IntroductionCollaborative Problem Solving (CPS) is a multifaceted process in which individuals worktogether to address complex challenges by integrating varied perspectives, skills, and knowledge.It combines social skills, such as effective communication and conflict resolution, with cognitiveabilities, including problem representation and strategic thinking 1 . In an increasinglyinterconnected world, the ability to collaborate effectively

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

) deep learning techniques into undergraduate research at ateaching-focused institution, with an emphasis on data mining and infrastructure performanceprediction.The research projects highlighted in this paper involve analyzing Connecticut’s bridge inventoryand inspection data for forecasting bridge conditions, as well as examining driver behavior,including speed profiles and gap acceptance at roundabouts in Connecticut. These projects areeither internally and externally funded, providing students with opportunities to engage inresearch during the summer, throughout the academic year, or as part of their independent studycourses. Some key student research tasks in these projects include data collection, data cleaning,contextualization, analysis

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

models (LLMs). We present the design ofa new assessment strategy in introductory programming courses where each student works on anopen-ended problem for their summative assessment. We design generalized scaffolds (project pro-posal, schematic development, pseudocode, integration of files, and graphs) for these open-endedassessments so that each student completes a project of desired complexity. Existing autogradersrequire rigid structure of inputs and outputs, and therefore, cannot grade such assessments. Ourtool, FlexiGrader, integrates code execution verification and unit testing tailored to the specifica-tions of each student individually, followed by code analysis using LLMs to generate feedback andgrades. FlexiGrader is capable of handling

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

-dimensionalgeospatial data analysis, and Cartopy for n-dimensional geospatial data visualization.Throughout this course, students not only learn how to use these tools, but also how to leveragePython for the analysis and visualization of water and environmental data. Data is explored fromvarious sources such as NOAA, NASA, Copernicus, USGS, and Data.Gov. Data is handled withformats such as csv, shapefile, and NetCDF. Specialized resources tailored to students’ interestsare utilized, such as Geemap Python-API of Google Earth Engine (multi-petabyte catalog ofsatellite imagery and geospatial datasets), CMIP6 datasets for climate projections, and FloPyPython-API of MODFLOW for groundwater modeling. Additionally, students are introduced tothe application of machine

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

years of our projectperiod, we collected comprehensive data from both instructors and 1000+ students (based onstudent online surveys) from three participating universities. Instructor data, gathered throughsemi-structured online interviews conducted twice during the project, captured their experiencesduring module development and implementation. Student data, collected through pre- and post-implementation online surveys each semester, included demographic information and bothLikert-scale and open-ended questions about their perceptions of data science and self-perceivedlearning of specific module topics (for detailed methodology, see [10], [11], [16], [17]). Ourprevious work explored instructor perspectives on this integration approach (see [10

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

deep learning. Students achieve high speed and highperformance on heterogeneous cluster architectures, and apply them in multiple fields such asimage classification, speech recognition, and natural language processing, etc. The coursecurriculum balances theoretical concepts with hands-on labs and research projects, fostering bothanalytical and research skills.Course Design and StructureCourse Objectives:The primary objective of this course is to equip students with a comprehensive understanding ofhigh-performance computing (HPC) principles and the emerging paradigm of parallel-basedmachine learning and AI. By the end of the course, students will: 1. Understand the foundational principles of high-performance computing architectures and

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

, spearheading innovation in sustainable waterpractices, and broadening the adoption of computational methods across the field. The programseeks to cultivate a new generation of interdisciplinary scientists equipped to addresswater-related challenges by targeting graduate and undergraduate students, early careerresearchers, and faculty. The capstone projects encourage participants to apply data science toolsand concepts to real-world water science problems. This format fosters collaborative learning,enabling participants to leverage their diverse expertise while working under realistic constraints.In this work-in-progress paper, we share an iteration of a longer design-based study as we assessthe first year of the WaterSoftHack program to understand its

Conference Session

DSAI Technical Session 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Iqbal Hossain, The University of Arizona; Thomas Harman, University of Arizona; Wesley Nguyen, University of Arizona; Ravneet Chadha, The University of Arizona

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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Generation (RAG) system for research-related inquiries at the University of Arizona. Dr. Hossain has published over two dozen peer-reviewed articles in areas including data science, computer algorithms, graph theory, network visualization, information retrieval, information visualization, machine learning, natural language processing, and database systems. He actively collaborates with external groups, students, and researchers at the University of Arizona on a wide range of research projects. With over 20 years of professional experience in research, IT systems development, team management, and innovation, Dr. Hossain is passionate about designing data science systems and leading efforts to solve the university’s

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

pursue STEMcareers. Participants engage in virtual research through Rice University, develop lesson plansbased on their research projects, and create a research poster. Teachers also receive mentorshipfrom graduate students, attend weekly meetings with faculty, and collaborate on lessondevelopment with the program’s curriculum staff. Teachers are required to submit weekly blogposts, create lesson plans for publication, and implement the lessons in their classrooms duringthe following academic year. The program offers a stipend, networking opportunities, and accessto a community of educators and researchers, making it a valuable professional developmentexperience for STEM teachers wanting to learn more about machine learning. It is designed

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

. His expertise spans Exploratory Data Analysis (EDA), Machine Learning (ML), Natural Language Processing (NLP), and Prompt Engineering Techniques (PETs) with Large Language Models (LLMs). Taiwo is known for his ability to collaborate effectively within and across organizations to meet project goals and drive transformative results. He excels in leading technical teams, offering strategic IT consultations, and implementing solutions that enhance productivity.VARUN KATHPALIA, University of Georgia Varun Kathpalia, born and raised in northern part of India, joined EETI as a PhD student in the Spring of 2024. He completed his undergraduate degree in Mechanical Engineering from Chitkara Institute of Engineering and

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

many instructors feeling unprepared to teachethics-related concepts. This gap risks fostering a workforce that develops AI technologies with-out adequately considering responsible and ethical practices, potentially leading to serious societalconsequences. Here, we present results from a pilot curriculum that integrates various ethical top-ics related to AI into a graduate-level machine learning course. Activities include a combination ofcase studies, project-based learning, and critical classroom discussions on the ethical implicationsof AI systems design and deployment.Two research questions guided the study: (RQ1) How do computer science graduate studentsperceive ethical issues in AI design and implementation before taking the class? (RQ2

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

University ofCentral Arkansas. With 12 years of experience in education, he has taught various science courses at bothsecondary and post-secondary levels and has held multiple STEM-related positions within the ArkansasDepartment of Education. ©American Society for Engineering Education, 2025 Expanding a State-wide Data Science Educational Ecosystem to Meet Workforce Development NeedsAbstractThe University of Arkansas has been developing a State-wide Data Science (DS) EducationalEcosystem over the last five years. A new project, funded by a HIRED grant from the ArkansasDepartment of Higher Education, builds on this existing DS Ecosystem. The program componentsinclude: 1) DS Ecosystem Expansion

Conference Session

DSAI Technical Session 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jordan Esiason, SageFox Consulting Group; Talia Goldwasser, SageFox Consulting Group; Rebecca Zarch, SageFox Consulting Group; Alan Peterfreund, SAGE

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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

artificial intelligence to computer science education contexts.Talia Goldwasser, SageFox Consulting Group Talia Goldwasser is in her third year as a member of the data team at SageFox Consulting Group, where she is responsible for creating and maintaining a number of equity-related data visualizations used by clients. Talia graduated from Smith College in 2021 with a degree in Mathematics.Rebecca Zarch, SageFox Consulting Group Rebecca Zarch is an evaluator and a director of SageFox Consulting Group. She has spent almost 20 years evaluating and researching STEM education projects from K-12 through graduate programs.Dr. Alan Peterfreund, SAGE Alan Peterfreund is Executive Director of SageFox Consulting Group, an 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 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

Paper ID #49477Data Analysis: Evaluating the Impact of the Professional Formation of EngineersProgram on Career DevelopmentPallavi Singh, University of South Florida Pallavi Singh received a bachelor’s degree in Electronics and Communication Engineering from Guru Nanak Dev Engineering College (GNDEC), Bidar, in 2016 and a master’s degree in Electrical Engineering from University of South Florida, Tampa, FL, USA, in 2019. Pallavi worked as a data science engineer, embedded system engineer, computer vision engineer, system engineer, project manager, and systems engineer, In addition, Pallavi, has also served as a

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

Delivery Self-discipline Confidence Content videos relevance length style on material recency Figure 2: Factors negatively impacting student completion of lecture videosThese findings underscore the importance of optimizing the design and delivery of lecture videos,which are a vital element of online education. Creating videos that are concise, relevant, andclosely aligned with course objectives can improve their effectiveness and better address theneeds of a diverse student population.Engagement FactorsThe factors that impact students’ engagement in an online course were also studied. Figure 3shows 55% of students identified assignments as the primary factor. Projects and deadlines

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

;interdisciplinarity; prior grant performance; project spending; high impact publications; andexternal funding outcomes. Selected processes for automating scholar data collection aredescribed.Results from initial work were tailored for implementation within the Colleges of Engineeringand Medicine at Penn State University. This paper provides initial results from both case studiesand explores the data-driven decision-making process in the context of STEM programs.Challenges and operational bottle-necks to automating the data collection are discussed withpossible solutions outlined. The authors recognize the potential conflicts to privacy andpreference that can emerge during the dashboard-building phase. As communication becomesincreasingly visual, Power BI

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

projects aiming at enhancing petroleum engineering curriculum with digital and data analytics contents. In 2023, he was part of a team that designed and published a module on introducing Python coding to petroleum engineering undergraduates – an award-winning effort. Most recently, he played a significant role in a team that developed an integrated framework for the integration of data analytics and machine learning into the petroleum engineering curriculum. He recently founded and is coordinating the Petroleum Data Analytics Special Interest Group (PDA SIG) of students and young professionals. In teaching, Dr. Mosobalaje adopts a balanced blend of analogical reasoning, concept visualization, field application and

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

programs.Table 3: Classification for Nevada 2.AP.PD.2: “Give attribution (credit) when using the ideas andcreations of others while developing programs.” Human Categorization LLM Categorization ChatGPT Claude Llama different identical to, similar to, 9 26 18 or based on identical to, similar to, or different 11 1 1 based on Table 4: Summary of mismatches.These mismatches regarding the verdict of identical are particularly important in the context ofthis project, which was concerned with whether and how state standards differ from the CSTAstandards. Thus

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

services to boost productivity and streamline tasks. Google Scholar,for instance, provides a free database that helps students find scholarly articles, research papers,and other academic resources for their projects [15]. Notion serves as an all-in-one productivityplatform, combining note-taking, project management, and collaboration features, making itespecially useful for group work and managing busy schedules [15]. Grammarly, an AI-poweredwriting assistant, helps students refine their writing by checking for grammar, spelling,punctuation, and style while also offering suggestions for improving clarity and organization[14]. ChatGPT stands out as a powerful tool for homework assistance, test preparation,language learning, and other

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

at OCMS, Oxford, and formerly a Research Fellow at the Oxford Centre for Hindu Studies, a Recognized Independent Centre of Oxford University. He is also the Founding Director of Samvada International Research Institute which offers consultancy services to institutions of research and higher education around the world on designing research tracks, research teaching and research projects. His first book The Integral Philosophy of Aurobindo: Hermeneutics and the Study of Religion was published by Routledge, Oxon in 2017. For more information, please visit: https://plaksha.edu.in/faculty-details/dr-brainerd-princeSiddharth Siddharth, Plaksha University Dr. Siddharth is the Director of Human-Technology Interaction Lab

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

serves as Director of the Master of Engineering program in Computer Science and Engineering. She regularly mentors undergraduate and graduate research projects that have received institutional recognition and funding.William J. Rothwell, Penn State University ©American Society for Engineering Education, 2025 Paper ID #48259William J. Rothwell, PhD, DBA, SPHR, SHRM-SCP, RODC, FLMI, CPTD Fellow is a DistinguishedProfessor at Penn State, and is a leading expert in workforce development. With 300+ publications,including 170 books, and a legacy of top-ranked programs, he has profoundly shaped the future ofvocational education

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

∗ hall.carrie98@gmail.com, Safia@ksu.edu, lshamir@ksu.edu Kansas State UniversityAbstractData science careers are projected to grow by more than 30% by 2032, yet data science academicsare lacking and cannot satisfy the growing market demand for qualified data scientists.Additionally, K-12 data literacy rates are declining, introducing a gap between moderndata-driven society and the ability of members of society to understand data. Early experienceswith STEM subjects have been shown to influence and predict students’ long-term careeroutlooks and outcomes. In the context of data science, this means that early introduction at theK-12 level is crucial in order to develop and maintain the data science workforce. Although

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

groups of students, faculty, and industry professionals?ParticipantsThis study involved participants from three groups: students, faculty, and industry professionals.The student participants (n = 23) were enrolled in the Department of Integrated Engineering,which emphasizes project-based and cooperative learning. The program allows students tocustomize their education across disciplines such as mechanical, electrical, biomedical, andcomputer engineering. At the time of the study, all student participants were taking a 1-creditcourse titled Machine Learning for Engineers, offered in April 2024. The faculty participants(n=18) were faculty from a variety of disciplines who were enrolled in a professionaldevelopment course about the use of

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

Paper ID #49113Adaptive Learning in Higher Education: A Knowledge Tracing and ExplainableAI ApproachNandan Reddy Muthangi, University of Toledo Nandan Reddy Muthangi is a senior-year Computer Science and Engineering student at the University of Toledo, where he currently serves as the President of the Association for Computing Machinery (ACM) Student Chapter. He is actively involved in research and development across multiple interdisciplinary domains. Nandan works as a Research Assistant in the LONG and the Cyber-Physical Human Systems (CPHS) Lab, where his contributions span projects involving autonomous drone navigation

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

, artificial intelligence, and neuroscience. His recent work in these areas has been supported by his major advisor’s NSF MCA project and a transdisciplinary NSF Research Traineeship (TRANSCEND). Michael’s engineering education research explores artificial intelligence’s potential in K-12 science education, particularly in developing personalized learning environments.Prof. Arash Esmaili Zaghi P.E., University of Connecticut Arash E. Zaghi is a Professor in the Department of Civil and Environmental Engineering at the University of Connecticut. He received his PhD in 2009 from the University of Nevada, Reno, and continued there as a Research Scientist. His latest ©American Society for Engineering