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

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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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 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

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 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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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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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

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

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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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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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 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

;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

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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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 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

U.S. course syllabi were examined, and content analysis was conducted usingcourse titles, course description, weekly topics, homework assignments, project descriptions, andclasswork. Similarly, a total of 42 Chinese course syllabi were examined and content analysiswas conducted using course title, course description, and course topics.Three domain experts developed codes based on ACM curricular framework. They coded thesample syllabus data, achieving an acceptable inter-coder reliability with over 85% agreement.4. Results4.1 Comparison of Data Science Knowledge and Skill in Core CurriculumBased on the framework for data science knowledge/skill in Table IV, we coded course topicsfor all 82 courses. Table IX lists the total number of course

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Melika Akbarsharifi, The University of Arizona; Ahmad Slim; Gregory L. Heileman, The University of Arizona; Roxana Akbarsharifi, The University of Arizona; Kristina A Manasil, The University of Arizona

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U.S., collected between 2000 and2022 as part of an Ascendium Foundation research project. Each institution provided anonymizedstudent-level data, including demographic information, academic performance metrics, and cur-ricular complexity measures. Table 2.1 summarizes the participating universities and the numberof programs and students contributed by each institution. 3 University Number of Pro- Number of Students grams University of Arizona 175 47410 Colorado State University 114 34471 Florida

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

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.Lexy Chiwete Arinze, Purdue University at West Lafayette (COE) Lexy Arinze is a first-generation PhD student in the School of Engineering Education at Purdue University and a Graduate Research Assistant with the Global Learning Initiatives for the Development of Engineers (GLIDE) research group. Lexy’s research interests include early career engineers, Artificial Intelligence, experiential learning, and global

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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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