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

personal abilities (Ownership), define cleargoals and actionable steps (Wisdom), habitually advance toward these goals while reflecting onprogress (Execution), and self-regulate while accessing supportive resources (Resilience) [19].Building on insights from the pilot program that the developers completed, the following are thekey features of the POWER platform: 1. Non-Directive Coaching: Facilitates self-discovery by asking questions rather than giving direct advice, encouraging students to take control of their learning and decisions. 2. Personalized Interactions: Customizes conversations per student, providing guidance that aligns with each individual's unique situation and goals. 3. Goal Setting and Tracking: Aids

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

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 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

, suggesting that shorter,focused content enhances memory retention and helps maintain attention on specific learningtasks [20]. A systematic review and meta-analysis [11] demonstrated that microlearningsignificantly improves academic performance in higher education compared to traditional macro-learning approaches [1]. The study attributes this improvement to reduced cognitive load,flexible learning environments, promotion of ‘self-directed learning, and timely feedback.The widespread popularity of platforms such as YouTube and TikTok underscores theeffectiveness of delivering bite-sized content, reflecting a growing preference for concise andaccessible information dissemination. TikTok, in particular, has been studied within theframework of

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

selection, model design, model evaluation, thencommunicating results and proposing action. Given this structured approach to data science, it iscrucial to address how these principles can empower individuals, especially young learners, tonavigate a world increasingly shaped by data.Data science education and data literacy in today’s youth are important not only to create andmaintain a well-educated society, but also to combat the increasing issues of widespreadmisinformation, disinformation, misleading data, and privacy violations [1]. Incorporating datascience into K-12 education can equip students with the skills to critically analyze data, identifydiscrepancies, and avoid falling victim to misinformation and misleading data representations

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

). The QPserves as a career development roadmap, emphasizing self-regulated learning, ethical practices,and targeted action plans supported by reflective assessments. Moreover, experiential learningactivities within the PFE program foster a service orientation among students, significantly en-hancing their social agency, academic self-confidence, and critical thinking skills, all vital forengineering success [8][1].Initially, the QP framework relied on Excel sheets and Google Forms to collect data on students’qualification development plans. Over six years of data were refined to simplify implementa-tion and analysis of the QP. This led to the development of the QP App, a semi-automatedplatform enabling students to select action items, assign

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

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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

Selena Johnson, Rowan University; Paromita Nath, Rowan University; Smitesh Bakrania, Rowan University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

. Analysis of the student interview datasuggests that course design, instructor feedback, and content delivery influence studentengagement in online courses. Integrating LMS-based learning analytics data with studentperspectives has the potential for educators to create engaging, student-centered onlineenvironments that bridge skill gaps, improve learning experiences, and better address studentneeds for success.IntroductionLearning analytics (LA) has become increasingly significant in higher education due to thetransition to digital and online learning environments. “Learning analytics holds the potential to:1) explain unexpected learning behaviors, 2) identify successful learning patterns, 3) detectmisconceptions and misplaced effort, 4) introduce

Conference Session

DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Collection

2025 ASEE Annual Conference & Exposition

Authors

Juan Alvarez, University of Illinois at Urbana - Champaign; Max Fowler, University of Illinois at Urbana - Champaign; Jennifer R Amos, University of Illinois at Urbana - Champaign; Yael Gertner, University of Illinois at Urbana - Champaign

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

introductory Engineering courses.1 IntroductionMetacognition refers to the self-regulation process that learners can use to measure their ownunderstanding and, thus, how effectively they are studying. Researchers have identified twolevels of metacognition: knowledge and regulation. The level of Knowledge entails knowingfacts about oneself and the demands of the task, procedural knowledge on strategies pertain-ing to the task, and knowing which strategies to apply in different situations. Regulationrefers to students’ ability to plan, monitor, and evaluate the effectiveness of their strategiesas well as debug when facing difficulties[4, 11].Metacognition skills have been shown to help students perform better academically[2, 3, 5, 6].Moreover, lack

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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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Diversity

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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 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

improving the application of course knowledge, suggesting that traditionalscoring may not fully capture the educational value of concept maps. Overall, this studyhighlights the potential of concept maps as an effective pedagogical tool in engineeringeducation.IntroductionA concept map is a visual representation of information that displays the relationships betweendifferent ideas and concepts through nodes and links 1,2 . It is structured hierarchically, startingwith general concepts and branching out to more specific concepts. The concepts are connectedwith linking words clarifying the nature of the relationships. Additionally, crosslinks are used tohighlight connections between different branches, emphasizing interrelated ideas. Figure 1 showsthe

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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Diversity

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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 7: Natural Language Processing and LLM Applications

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alexis Frias, University of California Merced; Shrivaikunth Krishnakumar, San Jose State University; Ayush Pandey, University of California Merced

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Engineering Education, 2025AbstractComputer Science courses often rely on programming assignments for learning assessment. Au-tomatic grading (autograding) is a common mechanism to provide quick feedback to students andreduce teacher workload, especially in large classes. However, traditional autograders offer limitedpersonalized feedback and often require all students to solve the same predefined problem, restrict-ing creativity. In this paper, we address these limitations by developing an AI-based autograderthat (1) can grade diverse, open-ended assignments where students work on independent, creativeprojects, enabling a new set of assessments in CS1 (introductory programming) courses, and (2)provides personalized feedback using large language

Conference Session

DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction

Collection

2025 ASEE Annual Conference & Exposition

Authors

Clara Fang, University of Hartford

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

. With the adventof advanced computational tools, the ability to store, process, and analyze large datasets hasbecome a core skill for engineering professionals. Recognizing this need, teaching-focusedinstitutions like ours are integrating innovative Artificial Intelligence (AI) techniques intoundergraduate research to equip students with these essential competencies. Undergraduateresearch experiences have been shown to significantly enhance students’ learning, technicalskills, and confidence, as highlighted by Lopatto (2017) [1] in the Survey of UndergraduateResearch Experiences. Such experiences not only provide a platform for applying theoreticalknowledge but also foster critical thinking and problem-solving abilities, essential for

Conference Session

DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction

Collection

2025 ASEE Annual Conference & Exposition

Authors

Chuhao Wu, Pennsylvania State University; Sarah Zipf, Pennsylvania State University; Na Li, Penn State University; David Benjamin Hellar, The Pennsylvania State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

obstacles in using learning analytics Chuhao Wu, Sarah Zipf, Na Li, David Benjamin Hellar The Pennsylvania State UniversityAbstract: E-learning resources and educational technology are increasingly used in STEMeducation, generating vast amounts of student-level data. Learning analytics tools can utilize thisdata, enabling instructors to adjust their pedagogy to support student success. Despite thepotential benefits, the implementation of learning analytics does not always lead toimprovements in teaching practices. This paper, through two case studies, investigates challengesinstructors may face in adopting learning analytics. In Case Study 1, we examined how onlineactivity

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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Diversity

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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

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xingyu You, Arcadia University; Wang Wang, Arcadia University; Zhairui Shen; Yanxia Jia, Arcadia University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

to virtual therapy. A surveyconducted in the United States revealed that 70% of 320 outpatient participants expressed astrong interest in using mental health tools to manage their psychological well-being [1]. Thisstudy examines trends in mental health apps since 2009, including a comparison of app statisticsbetween the pre- and post-COVID periods. Additionally, analysis of app’s privacy policyinformation is also conducted.Privacy policies for mobile apps are crucial as they explain how user data is collected, used,stored, and shared. Studies have shown that many mobile Health applications have significantsecurity vulnerabilities, jeopardizing the privacy of millions of users [2]. For mental health apps,it is particularly critical for users

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

speaker may fail to engage with the audience by avoiding eyecontact, making minimal gestures, and holding a rigid posture. Despite being accurate, theverbal information might not be supported by nonverbal clues such as kinesics and voicemodulation. This gap can make the presentation seem disjointed and unconvincing.Body language and facial expressions are examples of nonverbal communication that arecrucial in enhancing spoken material. Studies conducted by Schneider & Aburumman, talkabout improving audience engagement, building credibility, and improving messageretention [1] [2]. Yet, engineering curricula predominantly focus on verbal articulation—structured arguments, technical jargon, and precise language—while relegating nonverbalelements

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

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

self-directed learning opportunities. In this course, students learn how togather and analyze data as part of the engineering design process, apply systems thinking to anengineering or societal phenomenon, collaborate with peers to find solutions, and effectivelypresent solutions to an audience. Moreover, students are exposed to the introduction of theapplication of machine learning techniques to environmental datasets and Google Earth enginefor remote sensing datasets.This work will aim at reporting four main issues, namely (1) the unique components of thecurrent integrated data science course, (2) an account of selected environmental engineeringprojects using Python, (3) a survey result collecting data on students’ perception about the

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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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

-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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiaoning Jin; Sagar Kamarthi, Northeastern University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

personalizedrecommendations, details the tests conducted, and discusses the results obtained. This advancedapproach enhances the adaptability and relevance of users' learning experiences. We have alsodemonstrated this approach in our recently developed open curriculum on Data Science forManufacturing, which showcases the system’s adaptability in real-world educational contexts.IntroductionLabor markets in the tech and data science industry have grown rapidly in recent years.However, a specific set of skills are required for a prospect to be hired or even considered bymost employers. While having degrees such as a B.S. or even an M.S. has been the standard foryears, there has been a recent shift toward considering hard skills [1]. Recently, graduatestudents are

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiang Zhao, Alabama A&M University; Mebougna Drabo, Alabama A&M University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

community.KeywordsData Analytics, Data Science, Project-Based Learning, STEM EducationIntroductionData analytics is the process of inspecting, cleaning, transforming, and visualizing data with thegoal of discovering insightful and critical information for decision making [1]. The integration ofdata analytics in STEM education has had a profound impact on the advancement in every sectorof industries, government, and academia today. A workforce equipped with essential data scienceskills is crucial for maintaining the United States’ competitiveness and strengtheninginfrastructure security in today’s highly interconnected digital world. By analyzing large volumesof data, data science techniques can identify patterns and anomalies that may indicate potentialsecurity

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

textual data. For instance, we recently codedapproximately 10,000 state K-12 computer science standards, requiring over 200 hours of workby subject matter experts. If LLMs are capable of completing a task such as this, the savings inhuman resources would be immense.Research Questions: This study explores two research questions: (1) How do LLMs compare tohumans in the performance of an education research task? and (2) What do errors in LLMperformance on this task suggest about current LLM capabilities and limitations?Methodology: We used a random sample of state K-12 computer science standards. We comparedthe output of three LLMs – ChatGPT, Llama, and Claude – to the work of human subject matterexperts in coding the relationship between each state

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

theoretical knowledge and practical application by providing content directly applicableto real-world scenarios [1], [18]. This method focuses on delivering specific, actionableinformation that learners can immediately use in their daily tasks or professional activities.Core computer science courses require a solid grasp of algorithms, programming logic, complexcomputations, and the principles of computer systems. These subjects necessitate focusedattention from students. Educators play a crucial role in incorporating real-world scenarios intocourse materials to elucidate logical concepts or computational theories, making students feelempowered and integral to the learning process. For instance, when teaching propositional logic,the implication "𝑝

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

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

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Declan Kirk Bracken, University of Toronto; Sinisa Colic Ph.D., University of Toronto

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

format [1]. While OCR technology is mature and widelyused, challenges persist in detecting and reconstructing the format of complex tables, particularlyin specialized documents like academic transcripts. Recent advancements in computer vision fordocument analysis, including TableNet, CascadeTabNet, and LayoutLM, have greatly improvedtable detection and structure recognition tasks. TableNet, designed specifically for table detectionin document images, has shown promise in recognizing tables from structured documents like fi-nancial statements [2]. While its performance on structured tables is strong, it faces challengeswhen dealing with academic transcripts, which often feature a mix of tabular and non-tabularcontent, making traditional layout

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kristina A Manasil, The University of Arizona; Gregory L. Heileman, The University of Arizona; Melika Akbarsharifi, The University of Arizona; Roxana Akbarsharifi, The University of Arizona; Aryan Ajay Pathare, The University of Arizona

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

focused intervention strate-gies.Keywords: progress analytics, student success, student outcomes, learning analytics, program cur-riculum, graduation rates, educational data miningIntroductionWhile the number of students successfully completing their degrees has steadily increased sincethe beginning of the century,1 many students face new challenges that reflect a growing array ofacademic, financial, and personal obstacles.2 The traditional graduation timeline often proves dif-ficult to achieve due to factors such as credit misalignment, insufficient support systems, financialhardships, and competing personal responsibilities. For many students, these challenges compoundover time, creating barriers to degree completion that extend well beyond