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

participants collaborate with graduate studentmentors, engage in discussions with faculty members engaged in digital health research, explorereal datasets, and create grade-appropriate lesson plans. This paper focuses on the overallprogram design and the experiences of an elementary STEM teacher who participated in theprogram and implemented the lesson with her students. Literature ReviewArtificial Intelligence (AI) and Machine Learning (ML) in Elementary Curriculum The integration of AI and ML into elementary education is an emerging area of interestthat has the potential to equip young learners with foundational skills critical for the future [1].As technology continues to evolve, it is becoming

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

ASIST&T, and his research interests are focused on Human-Computer-Interaction, Big Data, and Data Analytics. ©American Society for Engineering Education, 2025 Shaping Future Innovators: A Curriculum Comparison of Data Science Programs in Leading U.S. and Chinese InstitutionsASEE submission:Data Science & Analytics Constituent Committee (DSA)1. IntroductionThe Data Science field has been evolving rapidly both in the United States and in China in recentyears. More and more day-to-day and business applications are depending on data sciencetechnologies such as data mining, machine learning, data management, and artificial intelligence[1]. With this rise in such data science technologies and

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

can often hinge on extra-departmental fundingopportunities—institutional research centers and external grant competitions. As engineeringprograms seek to invest in the next generation of engineers, research administrators canoperationalize research effort data to identify (1) near-term undergraduate and graduate studentexperiential opportunities; (2) top-performing teacher-scholars poised to lead studentexperiences; (3) features of teacher-scholars that can be predictive of early-stage interventionsthat support their success as fundable grantees. Data visualizations in service to engineering andSTEM programs provide a high-context field of opportunity for administrators, faculty, andstudents, supporting the continued growth of the engineering

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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be heavily impacted by incoming student preparednessand highly correlated to performance in first-semester technical courses such as math, physics, chemistry, andprogramming. Recent years have seen changes in the types and predictive power of incoming studentpreparedness information, as a result of the movement toward test-optional admission criteria. This paperpresents a quantitative study of current and longitudinal data regarding success in the University of KentuckyPigman College of Engineering as a function of first semester performance, including how that performancehas changed post-Covid overall and within key demographic groups.Three analyses are presented: 1) 6-year graduation within the Pigman College of Engineering as a functionof

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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and prioritize resources effectively by transformingdata into actional information. This pivot toward integrating digital information into highereducation administration has been motivated by several considerations to include emergingtechnological innovations and evolving labor market demands [1]-[2]. Communicating dataeffectively can be challenging in higher education, where typical business metrics such asrevenue, cost, and hours, among others may not have analogs.The term Business Intelligence (BI) has been used for nearly four decades, evolving fromtraditional industries into higher education. It is a technology driven process of gathering andanalyzing data that is organized and portrayed as actionable information to help leaders

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

topics (performance expectations, collaboration andplanning, skill development, problem solving, and evaluation) across the reports from bothInstitutions and were reported by the authors in a previous publication [1].Building on this work, the authors repeated the analysis techniques on the data collected duringthe subsequent project offering. Additionally, the authors used the themes identified from theinitial offering to train a classifier. The classifier was used to label and categorize the studentreflections from the second cohort based on the themes uncovered or “learned” when analyzingthe first cohort of responses.By replicating the previously reported analyses and using the previous work as the training dataset for labeling the results

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

evaluations, the study aims toprovide a comprehensive assessment of team effectiveness, highlighting areas for growth forindividual students. The findings suggest that this approach not only increases the efficiency ofthe evaluation process, but also possibly improves student engagement, and the overall quality ofteamwork amongst student groups.IntroductionA language can be defined as a system of rules or symbols that combine to express or broadcastinformation, ultimately shaping how we perceive and communicate within different culturalcontexts [1]. Because not all users are familiar with machine-specific languages, NaturalLanguage Processing (NLP) has emerged as a subfield of Artificial Intelligence (AI) that enablescomputers to understand

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

years being particularly decisive, as this period sees the highest dropout rates [1-2]. This phenomenon has significant implications at multiple levels: it impacts institutionalaccreditation processes, educational management, and public policies, while also posingeconomic and emotional challenges for the families involved [3-4].Several factors contribute to dropout during this stage, including difficulties adapting to theuniversity environment and the high academic demands of higher education [1-3, 5]. Thesechallenges can lead to frustration and demotivation, thereby increasing the likelihood ofstudent withdrawal [4]. The effects of dropout are not limited to individuals; educationalinstitutions experience declines in quality, reputation, and

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

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

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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-depth scale (0-3).The results indicate that while there is no significant pre- to post-assessment change in the factorscores derived from the 28-item questionnaire, students demonstrated a significant increase in thedepth of their responses to the open-ended essay questions, with theme depth increasing froman average of 1.4 to 1.6. These are discussed alongside recommendations for future AI ethicscurriculum design for computer science graduate students.Keywords: Ethical AI, Ethical decision-making, Curriculum Development, Machine learning Cur-riculum, AI Fairness, Privacy, Explainability, Transparency.1 IntroductionWhile artificial intelligence (AI) promises to improve our quality of life by automating tasks, ad-vancing healthcare, and

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

and across time. Although ChatGPT cansuccessfully complete different types of tasks, current models still show errors in logic, factualinformation, arithmetic, grammar, reasoning, coding, and even the model’s own self-awareness[1]. Assessing the performance of these tools is an ongoing task, and one that engineeringstudents, faculty, and industry professionals must engage with when deciding how to use theresponses they get from a GAI tool.This exploratory study aims to showcase student, faculty, and industry perceptions about thecapabilities of GAI to perform various tasks, as well as how they approach testing thisperformance. The methods, results, and discussion sections offer various insights to theengineering education community; 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

CS programs of an R1 public university,we demonstrate how universities may tackle the challenges of using AI for admissions. Our workprovides evidence that demographic features like age, gender, birth nation, and race may lead toinferred bias and highlights the importance of bias detection to create fair AI admissionssystems.1. IntroductionOver the last few decades, jobs in the technology industry have become far more competitive,with more students earning master's and doctorate level degrees for jobs motivated by nearly a20% higher salary than bachelor's degree holders as per the U.S. Bureau of Labor Statistics [1].According to the National Center for Education Statistics (NCES) [2], the number of graduateswith a master's degree has grown

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

required by employers. As more data and analytical methods becomeavailable, more aspects of the economy, society, and daily life will become dependent on data-driven decision-making. Recognizing this shift, the National Academies of Sciences (2018)emphasizes that academic institutions must prioritize developing "a basic understanding of datascience in all undergraduates" to prepare them for this new era [1]. This is particularly crucial forSTEM graduates, who must develop varying levels of expertise in working with data – the abilityto understand, interpret, and critically evaluate data, as well as to use data effectively to informdecisions. The recent emergence of large language models (LLMs) such as ChatGPT, which arebecoming increasingly

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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Data Science and Artificial Intelligence (DSAI) Constituent Committee

,affecting their performance and retention rates. Conversely, a well-structured curriculum thatbalances rigor and manageability can enhance student success by providing a clear path to degreecompletion. Previous studies suggest that while curricular complexity can enrich the educationalexperience, it can also lead to higher dropout rates and a prolonged time to graduation if notproperly managed [1, 2]. This study aims to rigorously estimate the causal effect of curricularcomplexity1 on four-year graduation rates across 26 U.S. universities. Extending our previouswork that identified initial links between curricular complexity and graduation rates[4], this studyintroduces a more advanced methodological framework that incorporates multiple causal

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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, HBCU, HSI, R1, andR2 universities. Each university participant uploaded the curricula associated with eachof their undergraduate academic programs to the website http://CurricularAnalytics.org.The total number of curricula collected, across all institutions (accounting for degree con-centrations/emphases) was 3,830.In this study, a curriculum refers to the set of courses (along with the corresponding setof course prerequisites) that, if successfully completed, would allow a student to earn thedegree associated with the curriculum. An example electrical engineering curriculum isprovided in Figure 1 (a). This curriculum is represented as a graph, where the verticesare the required courses in the curriculum, and the directed edges (arrows

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

role of professional development as a sustainable model forimproving the AI literacy of the current and future workforce.IntroductionIn this era of rapid technological advancements, Artificial Intelligence (AI) is transformingacademic and professional landscapes, driving innovation across disciplines and sectors [1].Engineering education, as a field that intersects deeply with technological evolution, stands at theforefront of integrating AI into pedagogy, research, and professional practice [2]. Generative AI(GAI) has emerged as a valuable tool, with the potential to enhance teaching and learning throughautomation, creativity, and personalized education [3]. However, the pervasive adoption of GAItechnologies has also raised significant

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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©American Society for Engineering Education, 2025 1 Understanding Research Dynamics at the University of Arizona: An AI-Driven Metadata Analysis Iqbal Hossain , Thomas Harman , Wesley Nguyen, Ravneet Chadha University of Arizona Knowledge Map (KMap) University of Arizona Emails: {hossain, harman, wesngu28, rschadha}@arizona.edu Abstract This study explores the complex research landscape of the University of Arizona, which boasts over $955 million in annual research expenditures. By analyzing an

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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landscape, trends, and impacts of strategic education through employment, as in Figure 1. It may bebroadening participation in engineering (BPE) initiatives both helpful to go even further as well, by looking at currentbroadly and at their institutions. Achieving and sustaining demographics to establish who the students of the future willBPE is a daunting challenge with known benefits [1]. Despite be.significant investments by the National Science Foundation This holistic, longitudinal view allows us to establish on-(NSF), Black, Indigenous and other People of Color (BIPOC) going trends in BPE (or lack thereof). Such trend analysis is&