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

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

become an essential toolfor academic and professional growth. Over the past couple of years, the use of GenerativeArtificial Intelligence (GAI) in academia has been the subject of several debates, with discussionsfocusing on its ethical implications and how to use it to aid teaching and learning effectively. AsGAI technologies become increasingly prevalent, raising awareness about their potential uses andestablishing clear guidelines and best practices for their integration into academic settings isessential. Without proper understanding and frameworks in place, the misuse or over-reliance onthese tools could undermine the educational goals they aim to support. Workshops and seminarsplay a critical role in addressing these concerns by not only

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

University. Her research interests focus on three key areas: data science curriculum and ethics, retention of minority students in STEM degree programs, and organization and classification of big data.Dr. Qiping Zhang, Long Island University Dr. Qiping Zhang is an Associate Professor in the Palmer School of Library and Information Science at the C.W. Post Campus of Long Island University, where she also serves as director of the Usability Lab. Dr. Zhang holds a Ph.D. and an M.S. in information and library studies from the University of Michigan, Ann Arbor, and an M.S. and a B.S. in cognitive psychology from Peking University in Beijing, China. Prior to joining Long Island University in 2006, she worked at Drexel

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

technologiesconnect to real-world problems, while also enhancing their knowledge and skills, learningattitudes, and interests in technology [12].Teacher Professional Development (PD) for ML The success of integrating Machine Learning (ML) into the elementary curriculum isheavily dependent on the preparedness of educators. Traditional teacher professionaldevelopment (PD) programs often focus on subject-specific content or pedagogical strategies,but with the growing importance of AI and ML, there is a clear need for professionaldevelopment that specifically targets these areas. Research highlights that teachers requirefoundational training in both the technical and ethical aspects of AI and ML to feel confident inteaching these topics [1, 13]. Thus

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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Data Science and Artificial Intelligence (DSAI) Constituent Committee

by grouping like items in the surveyactivities. For example, the second item (Q2) generally asked participants to identify theirconfidence in ChatGPT to provide useful citations. To contextualize this general question: “Howconfident are you that ChatGPT can give you citations that you can use for your work?” for eachparticipant, we adapted the discussion of work to their specific tasks. Specifically, Q2 wasspecified as: “How confident are you that ChatGPT can,” “... give you citations that you can usefor your DLA (final project),” for the students; “give you appropriate academic citations that youcould use for a paper about the ethics of AI,” for the faculty; and “give you correct and relevantcitations you can use for your work,” for the

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

qualities, experience, and beliefs. These include essays on leadership, academicresearch, community service, and personal and professional ethics. Therefore, the data consistsof numerical features such as standardized examination scores and Grade Point Averages (GPA),along with textual data from the essays and letters of recommendation. Applications also collectpersonal information including but not limited to the applicant's name, address, gender, andethnicity. Figure 1 details the potential stages in the admissions pipeline where bias couldemerge and where AI is currently used as per the Intelligent survey [4].In the context of university admissions, features like gender and ethnicity are usually examinedfor bias, as done by Kahlor et al. [13

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

) Year 3 – Spring (UA) DASC 2594 Multivariable Math for Data SEVI 2053 Business Foundations Scientists INEG 2313 Applied Probability and Statistics INEG 2333 Applied Probability and Statistics for Engineers I for Engineers II DASC 2133 Data Privacy & Ethics DASC 3203 Optimization Methods in Data Science DASC 3103 Cloud Computing & Big Data DASC 3213 Statistical Learning RRRR NNN3 Required Concentration Course RRRR NNN3 Required Concentration Course 16 hours Total