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

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 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

-informed practices inengineering education. By providing a detailed analysis of in-demand competencies for entry-levelelectrical engineering positions in the southeastern U.S., this research empowers educators,policymakers, and industry stakeholders to make informed decisions regarding curriculumdevelopment, workforce training, and talent acquisition strategies.Keywords:Competency, Electrical Engineering, Computer Engineering, NLP, Machine Learning,Engineering Curriculum, Workplace Readiness.1. IntroductionIn an era marked by rapid technological advancements and shifting industry landscapes, preparinggraduates with the skills and knowledge required to meet real-world demands has become apriority in engineering education. Electrical engineering, a

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

face challenging coursework and professional developmentrequirements, peer mentors serve as invaluable guides who can relate to and support their peersthrough shared experiences. A comprehensive review of undergraduate mentoring programs hasdemonstrated that well-structured peer mentoring initiatives consistently yield positive outcomesacross multiple domains [1]. In engineering education, these benefits include enhanced academicperformance, strengthened leadership development, formation of engineering identity, and moreeffective career planning [2]. This peer-to-peer support system has proven particularly effective inhelping students transition through different stages of their engineering education, fromfoundational courses to specialized

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

emerged as powerful tools in education, offering thepotential to transform classroom dynamics through automation, personalization, and enhancedstudent engagement [1]. Educators have already begun utilizing LLMs to generate lesson plans,streamline grading, and provide personalized feedback to students [2]. Additionally, LLMs havebeen implemented as Intelligent Tutoring Systems, assisting students in gaining a deeperunderstanding of challenging topics by offering tailored explanations and interactive learningexperiences [3]. One particularly promising but underexplored application of LLMs in educationis their potential for personalized learning (PL), specifically in the realm of text adaptation.Unlike traditional PL approaches, which categorize

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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Diversity

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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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Data Science and Artificial Intelligence (DSAI) Constituent Committee

, 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

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

, Explainable AI(XAI). 1. Introduction and Related WorkRecent advancements in Artificial Intelligence are set to drive transformative changes acrossvarious domains, including healthcare [1], environmental sciences [2], business management [3],and most notably, education [4]. The keyways AI is being used in the field of education includepersonalized learning, Intelligent tutoring systems (ITS), optimizing administrative processes,and enhancing accessibility and engagement. By tailoring learning experiences to individualstudent needs, AI-powered systems have the potential to increase engagement, improveacademic performance, and provide more equitable access to education.Among the emerging AI-driven methodologies, Deep Knowledge Tracing (DKT) [9

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

computing requirements in machine learning and artificialintelligence highlights the importance of high-performance parallel computing (HPC). Manymodern AI systems require efficient algorithms that can take advantage of multi-core processors,multiple GPUs, and distributed systems [1-3].The convergence of data science with high-performance computing (HPC) and parallelismdevelopment is increasingly recognized as essential in both industry and academia. Therefore,both industry and academia are increasingly seeking professionals who are proficient in HPCprinciples and parallel development to address the challenges posed by massive data processing,machine learning, and AI [3-5].However, existing curriculum in academia often fails to provide a

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 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

systems that enhanceintelligent transportation networks' safety, reliability, and efficiency. Future directions includeleveraging edge computing and advanced AI architectures to improve decision-making processesand achieve Level 5 autonomy.IntroductionAutonomous vehicles rely ponderously on computer vision systems to interpret theirenvironments and make real-time decisions. As these systems become more integrated intotransportation, ensuring their accuracy and reliability is crucial [1]. A significant challenge inautonomous driving is detecting and segmenting objects such as pedestrians, vehicles, and trafficsigns in complex environments [2]. Errors in object detection can undermine the safety andreliability of autonomous systems, potentially

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

©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 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 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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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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&

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

©American Society for Engineering Education, 2025 WIP: Formative Findings from the First Year Implementation of a Water and Data Science WorkshopIntroductionThe increasing complexity of global water challenges, such as resource management, climateresilience, and sustainability, demands interdisciplinary approaches that integrate advancedcomputational methods with domain-specific expertise [1]. With its capacity to process, analyze,and model large datasets, data science has become a transformative tool in water science andhydrology. By leveraging computational tools such as machine learning, hydrological modeling,and geospatial analytics, researchers can address pressing questions in water resourcemanagement more

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

prolific in data generation and measurement. A credible report indicates that an averageoffshore platform generates about 2 TB/day of data [1]. Further still, the proliferation of open-source data mining softwares is encouraging the use of insights from data in critical businessdecisions: from sub-surface modelling through production optimization to investmentmanagement. Expectedly, a workforce skillful in data analytics technology is critical to theadoption and sustainability of the oilfield digital transformation. Career opportunities in PDA, MLand AI are emerging for graduate petroleum engineers, as a result of the ongoing oilfield digitaltransformation. AI and digitalization have been identified as major factors in meeting the demandsof