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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Diversity

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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 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

Professional Formation of Engineers Program on NACE Career Competency through Ambition Levels and Completion RatesABSTRACTThe Professional Formation of Engineers (PFE) program at the University of South Florida(USF) comprises a series of three one-credit courses designed to develop essential competen-cies in engineering students. This course series emphasizes the application of ethical principlesand the impact of ethical engineering practices on both local and global communities, therebypreparing students for successful professional careers. The primary objective of the PFE pro-gram is to facilitate the optimal career development of USF Electrical Engineering (EE) stu-dents through engaging practical and professional

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

traditional full-length videos.3) Procedure: The survey was administered online using Microsoft Forms. Participants receivedan invitation via their university email addresses and were given a 15-day period to complete thesurvey at their convenience. Participation was entirely voluntary. To encourage participation,students were offered an optional extra credit opportunity, approved by the course instructor andin accordance with university policies.4) Ethical Considerations: This study received approval from the Institutional Review Board(IRB) at the University. All procedures performed in the study involving human participantswere in accordance with the ethical standards of the institutional research committee and with the1964 Helsinki Declaration and

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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Diversity

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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 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

complex task of identifying keywords. However, LLMs such as ChatGPT can also produceerroneous output [5]. A common problem with LLMs is their tendency to hallucinate, a problemthat may be inherent to their architecture [6]. Borji defines eleven types of LLM errors, includingproblems with reasoning, logic, humor, ethics, and bias [7]. The evidence of LLM bias, includingracial bias, is particularly troubling. For example, LLMs show prejudice related to dialect markersassociated with Black English [8] and stereotypes associated with student names [9]. Thisevidence of bias suggests that caution is warranted when LLMs are used in any task wherejudgement is required.1.3 AI in Education ResearchTo date, there has been limited research on the potential

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

over a decade-and-a-half of industry experience within tech and education space as aFounder/Co-Founder, EdTech Professional and Advisor to companies, public and privateorganizations, Taiwo continues to establish himself as a forward-thinking innovator at the nexusof Engineering, AI and Education. His research interests include competency development andleveraging AI tools, technologies and methodologies to enhance ethical research and classroomengagement for advanced problem-solving. Taiwo has developed two pioneering frameworks forintegrating AI into qualitative research, which are currently under review for U.S. copyrightprotection.Varun Kathpalia, University of GeorgiaVarun is a PhD student in Engineering Education Transformations Institute

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

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

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

computations. Achieving a balance between computational efficiency and highdetection accuracy is critical for real-time performance in dynamic driving environments.Additionally, AV deployment raises regulatory and ethical issues. In critical scenarios, AVsystems may need to make moral decisions, such as choosing between two harmful outcomes,which introduces complex ethical dilemmas [25]. Furthermore, the lack of standardizedregulations governing AV deployment across regions creates additional barriers to large-scaleadoption. Mask R-CNN Mask R-CNN is a groundbreaking model in deep learning, designed to perform instance segmentation by identifying and segmenting individual objects at the pixel level. Introduced by He et al. (2017

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

range within K-12 education, these curricula are often tailored tospecific grade levels to address the unique needs and abilities of each group. Table 3 provides anoverview of these curricula, categorized by targeted age levels and primary topics.Several data science curricula has been analyzed by researchers, with the goal of discovering howpre-collegiate data science education is taught [95]. The main topics appearing in the analyzedcourses were the nature of data, ethics, data sources, data inquiry, distributions and variability,measures of center, computer programming, variable associations, data visualization, samplingand simulating, and machine learning. Many of these topics will already be covered in existingK-12 courses, but the data

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

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

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

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

additionalfeatures in their platform, such as a calendar and mobile access, which would be ideal forstudents who prefer not to sit at a computer and instead want the convenience of managing tasksand chatting on their phones. Although the students did not voice any concerns regarding ethics or privacy concerns, itis important to keep these two issues in mind at all times. Thus, the recommendations fordevelopers of AI-powered platforms are to keep the needs of the students at the forefront,including helping them understand privacy concerns and how the data is handled in easy terms,continue advertising the program, and share the positive and negative aspects of the usage of theAI-powered counseling services [20].Limitations This study has several

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

topic in engineering. Additionally, they were encouraged to think beyond justthe technical aspects by incorporating social, ethical, and personal implications. Students wereprompted to explore how these concepts intersect with their career aspirations, societal impact,and everyday life. The concept map had to be created digitally and clearly structured, showingdifferent levels of hierarchy and connections.The survey employed a Likert scale 10 to measure students’ perceptions of the assignment’simpact. Specifically, it assessed whether the assignment helped students understand how thecourse connected to other topics, with response options ranging from Strongly agree (5) toStrongly disagree (1). The scale was also used to evaluate whether the

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

learning support. Such a systemwould interface with existing Learning Management Systems (LMS) through APIs to providereal-time insights for both students and educators. Future work will focus on refining the DKTmodel, addressing class imbalance, and testing the platform across more diverse datasets.Practical testing will be a key component, involving real-world classroom settings to evaluateusability, gather feedback, and ensure the system aligns with educational needs. Pilot studieswith students and educators will guide interface refinement and assess the platform'seffectiveness in fostering personalized learning. Additionally, ethical AI practices, includingprivacy safeguards and explainability, will remain a priority to ensure trust and

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

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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Diversity

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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 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

into smaller chunks reduces cognitiveload, making learning more effective. These foundational principles align with microlearning'sstructuring of educational content for better learner outcomes. Bartram [41] further validated theeffectiveness of such strategies by demonstrating how bite-sized simulations in medical trainingenhanced engagement and reduced cognitive overload.Despite its potential, the integration of AI in microlearning faces challenges. Issues such as biasin AI-generated content, ethical concerns, and over-reliance on automation remain critical areasof discussion. Ivanov and Soliman [35] cautioned against the lack of depth and critical analysisin AI-generated materials, which could lead to surface-level understanding if not

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

) 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

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

comprehensive overview of Python libraries and implications,” in Ethics, Machine Learning, and Python in Geospatial Analysis, 2024, pp. 22, doi: 10.4018/979-8-3693-6381-2.ch004.[16] L. A. Rossman, Storm Water Management Model User’s Manual, 2010.[17] M. Rocklin, “Dask: Parallel computation with blocked algorithms and task scheduling,” in Proc. 14th Python in Sci. Conf., 2015, pp. 126–132.[18] S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, 2009.[19] B-E. B. Semlali et al., “Hadoop paradigm for satellite environmental big data processing,” Int. J. Appl. Environ. Inf. Syst., vol. 11, no. 1, pp. 23–47, 2020.[20] W. McKinney, Python for Data Analysis: Data Wrangling

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

-Move Frameworkcollection is ongoing to further expand the study’s scope and enhance the framework’svalidation. All conversations were recorded with informed consent, anonymized duringtranscription, and securely stored in compliance with ethical guidelines.3.2 Data Collection ProcessThe mentoring sessions encompass three primary types of interactions that map ontoour five-category Talk-Move Framework in different ways: • Academic Planning: Students sought guidance on course selection, workload management, and long-term career goals. These interactions typically map to the Goal Setting and Planning category when focusing on objective-setting, and to the Feedback and Support category when providing guidance on