Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Thomas Rossi, University of New Haven; Ekaterina Vasilyeva, University of New Haven; Ren Oberdorfer, University of New Haven; Jhansi Sreya Jagarapu, University of New Haven

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Computers in Education Division (COED)

BackgroundAcademic performance can be influenced by poor mental health stemming from the multifacetedchallenges faced by post-secondary students. These challenges include but are not limited to:social pressures, bullying, anxiety, identity issues, and lack of support systems. According to astudy conducted on 72 post-secondary students, approximately 72 percent of participantsself-reported an anxiety score of 8 or above on the GAD-7 system [1]. A score of 8 on the GAD-7system marks the point that an individual is recommended to seek professional help. Moreover, inanother study that was conducted in 2018-2019 on 62,171 colleges, 57 percent of students withanxiety or depression have not used counseling resources or therapy [2].In order to combat mental health

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Christine Wusylko, University of Florida; Rachel Still, University of Florida; Pavlo Antonenko; Brian Abramowitz, University of Florida; Jeremy A. Magruder Waisome, University of Florida; Victor Perez; STEPHANIE KILLINGSWORTH, University of Florida

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Computers in Education Division (COED)

University of Florida (UF). Her research focuses on self-efficacy and critical mentorship in engineering and computing. She is passionate about broadening participation and leveraging evidence-based approaches to improve the engineering education environment for minoritized individuals.Victor PerezSTEPHANIE KILLINGSWORTH, University of Florida ©American Society for Engineering Education, 2025 WIP: One Teacher’s Experience Adapting an Innovative, Flexible Computer Vision Curriculum in a Middle School Science ClassroomIntroductionArtificial intelligence (AI) is predicted to be one of the most disruptive technologies in the 21stcentury [1], and to prepare all young people to live and work in an AI

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Anthony Cortez, Point Loma Nazarene University; Paul Schmelzenbach, Point Loma Nazarene University

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Diversity

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Computers in Education Division (COED)

Education: Insights into Metacognition and Problem-Solving Patterns1 IntroductionThe widespread and rapid emergence of large language models (LLMs) such as ChatGPT,Claude, and Gemini may be fundamentally transforming how students approach their academicwork. This transformation is clear in higher education, where AI tools can now successfullygenerate solutions to many problems that undergraduates previously struggled with for hours [1].Rather than simply supplying answers, educators have experimented with using LLMs in theclassroom in various ways such as brainstorming, proofreading, providing feedback, givingencouragement, and checking for understanding [2–4]. By their widespread availability andincreasing capabilities

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Gerald Tembrevilla, Mount Saint Vincent University; Mohosina Jabin Toma, University of British Columbia, Vancouver

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Diversity

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Computers in Education Division (COED)

, calculator, and grocery list, one can see that the average fruit or vegetabletravels more than 2,400 km from farm to family dinner table. Research indicates that such traveladds 17 times more emissions to the atmosphere than if the food had been bought locally [1]. InMarch 2005, Vancouver couple Alisa Smith and James MacKinnon embarked on a culinaryexperiment: to survive for a year on food produced within 160 km or 100 miles of their home inVancouver, B.C. [2]. The year-long experiment turned into a book on the 100-mile diet, realityTV shows, and a series of invited talks across North America. A local food movement thatincluded the growing, cooking, and living of the 100-mile diet ethos grew and expanded [3].The 100-mile diet experiment led the

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Nicholas Cameron Amely, California State University, Fresno; Wei Wu, California State University, Fresno; Jesus Leyva, California State University, Fresno

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Computers in Education Division (COED)

evaluation andlearning assessment with peer students in ECE. As a proof of concept, this paper explored howstudent-led development of VR content and experience might offer a solution to a commonobstacle faced by many STEM educators who are interested in exploring VR, which is the lackof readily adoptable VR content. This study contributes to better understanding the role andimpacts of learner-as-creator/co-creator in engaging student learning in educational technology-integrated learning environments.1. Introduction & backgroundThe objective of this study was to explore student-led development of virtual reality (VR)applications as an alternative solution to enhance student learning and engagement in the field ofelectrical and computer

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

MALEK EL KOUZI, Queen's University; Haley Clark, Queen's University; Richard Reeve, Queen's University

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Diversity

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Computers in Education Division (COED)

augmented reality tools to enhance student comprehension in lessons. His recent research focuses on the collaboration within augmented reality educational applications and its impact on student skills. Personal Website: https://malekelkouzi.com/ Google Scholar : https://scholar.google.ca/citations?user=9yHaley Clark, Queen’s UniversityRichard Reeve, Queen’s University ©American Society for Engineering Education, 2025AR AniMotion: Augmented Reality Application for Enhancing Speech Skills in Children with Speech Difficulties- A Work in Progress Abstract. Roughly 3% of children experience speech delays or difficulties globally [1]. A “speech delay” is defined as a child's speech or language development

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

John William Hassell, OU Polytechnic Institute; Christopher Freeze, The University of Oklahoma; Ahmed Ashraf Butt, The University of Oklahoma; H. Glen McGowan III, Google; William Ray Freeman

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Computers in Education Division (COED)

Polytechnic Institue Polytechnic Institute Polytechnic Institute University of Oklahoma University of Oklahoma University of Oklahoma Tulsa, OK, United States Tulsa, OK, United States Tulsa, OK, United States hassell@ou.ed christopher.freeze@ou.edu ahmed.ashraf.butt-1@ou.edu H. Glen Mcgowan William R. Freeman Google Polytechnic Institute Tulsa, OK, United States Tulsa, OK, United States gmcgowan@google.com

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Christopher Allen Calhoun, University of Cincinnati; David Reeping, University of Cincinnati; Siqing Wei, University of Cincinnati; Aarohi Shah, University of Cincinnati

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Computers in Education Division (COED)

find information on their phones that might havetaken us hours to track down in the library." [1, p. 1]. This newfound instantaneous access toinformation has provided students with significantly more online resources, some provided by theinstructor and others discovered by the students. As instructors often lament, students seldom usedtheir textbooks [3] and did not frequently visit office hours [4] to help develop their problem-solving skills - even before this explosion of learning supplements. Perhaps now more than ever,with the various resources at their disposal, students must leverage considerable metacognitiveskills to navigate them, as students cannot rely on the filtering provided by the instructor's expertiseand must experiment with

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Katie Vu, University of Michigan; Avery Mitchell Maddox, University of Michigan; Caleb William Tonon, University of Michigan; Guli Zhu, University of Michigan; Tyler Wang, Stony Brook University; Rafael Mendes Opperman, University of Michigan; Qiuyi Ding, University of Michigan; Zifei Bai, University of Michigan; zhanhao liu, University of Michigan; Ziyi Wang, University of Michigan; Arvind Rao, University of Michigan; Daniel Yoon, University of Michigan

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Computers in Education Division (COED)

change the way people write, think, and learn. Such tools,trained on dozens to hundreds of terabytes of data and built around multi-billion parametertransformer-based architectures, are capable of interpreting and generating language at anunprecedented level of similarity to humans. Available in a wide array of readily accessibleinterfaces, from mobile applications to web sites, and now fully integrated into the operatingsystems of the latest smart phones [1], LLMs are being increasingly adopted into more-and-moreaspects of human life. One of the most active areas of LLM adoption has been in education. Like a personaltutor, LLMs can provide direct answers to complex questions, a distinct jump in ease-of-usefrom course material and search

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

MALEK EL KOUZI, Queen's University; Omar I.M Bani-Taha, Carleton University; Richard Reeve, Queen's University

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Computers in Education Division (COED)

experience that enhances understanding of complex biological processes. Keywords: Augmented Reality, Elementary classroom, Interactive Learning, Educational Technology, Speech to Text.1 Introduction &BackgroundEducators widely concur that the integration of technology into the learning process iscrucial for student success [1]. The adaptability of augmented reality (AR) as a learningresource has been demonstrated to effectively support student success across alleducational levels [2] and AR has been rapidly adopted and integrated into manyeducational settings [3], [4]. AR represents a substantial advancement towards atechnology-driven educational setting, primarily due to its adaptable features thatdistinguish it from

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Joel Nirupam Raj; Ashwath Muppa, Thomas Jefferson High School for Science and Technology; Rhea Nirmal; Teo W. Kamath; Achyut Dipukumar; Aarush Laddha; Mihai Boicu, George Mason University

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Diversity

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Computers in Education Division (COED)

laboratory conditions, the research aims to provide practical insights for educatorsconsidering these tools. The findings will contribute to broader discussions about technology-enhanced learning and the evolving relationship between artificial intelligence and humaninstruction in technical disciplines.Literature ResearchRecent advances in LLMs have shown their potential to transform educational settings, particularlyin programming courses where timely, detailed feedback is important. Fagbohun et al. [1] statesthat LLMs can automate grading with personalized feedback but that they still require carefulhandling of biases combined with human supervision to ensure that LLMs are fair and efficientand to reduce the occurrence of ethical risks like

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Daniel Kane, Utah State University; Wade H Goodridge, Utah State University; Linda Davis Ahlstrom, Utah State University; Oenardi Lawanto, Utah State University; Michaela Harper, Utah State University; Cassandra J McCall, Utah State University

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Diversity

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Computers in Education Division (COED)

student learning and how students view their engagement with coursecontent. However, recent studies have indicated that student use of AI has led to enhancedcreativity [1-3], greater comprehension of conceptual material [4], and increased motivation tolearn difficult material [2-5]. Further studies have indicated that AI can have a positive effect onstudents’ visualization and simulation of new ideas [2], [6]. A key feature of AI that separates itfrom other learning resources is its ability to tailor learning materials to the needs of individualstudents through conversational approaches, smart assessments, and customized feedback, all ofwhich contribute to enhanced learning [2], [7].While the benefits of AI are numerous, its integration into

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kangxuan Rong, Cornell University; Campbell James McColley, Cornell University; Ted Karanja Mburu, University of Colorado Boulder; Alexandra Werth, Cornell University

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Computers in Education Division (COED)

students enrolled in the BME 2081 Experiential Learning Seminarcourse during the Fall 2024 semester, a 1-credit advanced biomedical engineering course with adesign concentration. The cohort was predominantly women (72.97%) and racially andethnically diverse. Additionally, 60.5% of students reported having at least one parent with amaster’s degree or higher, indicating a majority with familial exposure to advanced education.Comprehensive demographic data can be found in Appendix G.Course Structure and Teamwork Instruction Students were explicitly taught teamwork skills through lectures, shared value setting,and structured exercises. They participated in group projects designed to simulate real-worldproblem-solving scenarios, including the

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Eesha tur razia babar; Ahmed Ashraf Butt, University of Oklahoma

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Computers in Education Division (COED)

evolving landscape of education, the integration of data-driven insights using EducationalData Mining (EDM) techniques has revolutionized how educators understand and enhancelearning processes. EDM is an interdisciplinary field that uses techniques from data analysis,machine learning, and statistics to extract insights from educational data [1]. These advances haveenabled significant progress in innovative educational practices, such as personalized learning,and predictive analytics[2]. However, a prominent constraint of most EDM techniques is the lackof transparency[3] in their decision-making process.Most traditional data mining algorithms, such as classification, have the tendency to provide highprediction accuracy in various educational tasks

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ahmed Ashraf Butt, University of Oklahoma; Saira Anwar, Texas A&M University; Asefeh Kardgar, Texas A&M University

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Computers in Education Division (COED)

, particularly with the emergence of Large Language Models (LLMs). The LLMmodels, in general, are trained on a large corpus of data to produce human-like responses in text,images, or other sources [1]. Their ability to generate human-like responses has made them aninvaluable tool in education, particularly for automating and enhancing various educational tasks[2-4]. One key area that significantly impacts LLMs’ ability to understand and process the textand generate responses is based on the prompt language used to instruct LLMs [5]. Due to theircapability, researchers have used LLMs to perform various educational tasks, from simpleconversation (e.g., [6]) to complex text analyses (e.g., [7]). For instance, in a study [8], theauthors showed that the LLM

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jinyi Jiang, Nanyang Technological University; Ibrahim H. Yeter, Nanyang Technological University

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Diversity

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Computers in Education Division (COED)

identifies gaps that require intersectional andlongitudinal research. By centering female students' experiences, we provide future researchdirections and pedagogical strategies to cultivate inclusive, interdisciplinary AI literacy.1. IntroductionThe pervasive integration of AI across industries necessitates comprehensive AI literacy as afundamental skill within higher education. (Timon Sengewald & Tremmel, 2024) AI literacyencompasses not only technical knowledge—such as algorithms and computational logic—butalso ethical awareness, societal implications, and practical applications. (Ng et al., 2021;Matthias Carl Laupichler et al., 2022) As AI increasingly influences communication,decision-making, and knowledge creation, students must be equipped

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Chafic Bousaba, Guilford College

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Computers in Education Division (COED)

deeper engagement with key concepts in computing and IT infrastructure.By exploring these cluster architectures, this paper demonstrates the potential of BYOCC inmaking cluster computing accessible and practical for educational environments, promotingtechnical learning while encouraging innovation in cloud technologies and IT practices.1. IntroductionThe integration of technology in education has transformed traditional, outdated learningenvironments, to provide more interactive, hands-on, and engaging experiences for students.Innovative solutions such as Bring Your Own Device (BYOD) and Bring Your Own Technology(BYOT) have become the norm in modern classrooms. These strategies encourage students tobring their personal devices to school, and

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Zifeng Liu, University of Florida; Yukyeong Song, University of Florida; Qimao Yang, University of Florida; Wanli Xing, University of Florida; Jing Guo, University of Florida

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Computers in Education Division (COED)

exploring students’feedback on the lab. The results demonstrated significant improvements in students’ quantumcomputing knowledge (p < .001), medium-to-high engagement and perceived usability scores(M = 3.90, SD = 1.06), and no significant changes in attitude. This study introduces aninnovative learning tool for undergraduate quantum computing education and provides empiricalevidence supporting the effectiveness of the tool in enhancing QC learning.1 IntroductionQuantum computing (QC), or Quantum Information Science and Technology (QIST), is anemerging field grounded in the principles of quantum mechanics, offering the potential torevolutionize industries by addressing complex problems far more efficiently than classicalcomputers [1]. Over the

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Valerie Elise Sullivan, University at Buffalo, The State University of New York; Rachel N. Bonnette, University at Buffalo, The State University of New York

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Diversity

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Computers in Education Division (COED)

) andneurodivergent instructor (n=2) perspectives through interviews and focus groups throughout asemester to understand their experiences with and recommendations for inclusive practices. Ourfindings demonstrate that a combination of practices is required to support these students.IntroductionComputer Science instructors can better teach neurodivergent students when they have thenecessary training to enhance learning and belonging for these students [1]. Neurodiversity is aterm that captures how natural, biological variation in neurological development is a fact andbenefit of the human population [2], [3]. Here, we include any neurominority within the termneurodivergent, including, but not limited to, autism, dyslexia, and mental health disorders [4],[5

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Tayo Obafemi-Ajayi, Missouri State University; Naomi L Dille, Missouri State University; Dhanush Bavisetti, Missouri State University; Sherrie Ilene Zook

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Computers in Education Division (COED)

©American Society for Engineering Education, 2025 Integrating Machine Learning into Middle and High School Curricula using Alzheimer’s Disease Prediction Models Naomi Dille,1,† Sherrie Zook,2,† , Dhanush Bavisetti 3 and Tayo Obafemi-Ajayi3,* 1 Cherokee Middle School, Springfield Public Schools, Springfield, MO 65802 USA 2 Kickapoo High School, Springfield Public Schools, Springfield, MO 65802 USA 3 Missouri State University, Springfield MO 65897 USA †: Both authors contributed equally. * tayoobafemiajayi@missouristate.edu

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Priyanshu Ghosh, Mission San Jose High School; Mihai Boicu, George Mason University

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Computers in Education Division (COED)

practicalrecommendations for educational platforms, underscoring strategies to refine feedbackmechanisms, elevate course design, and enhance the overall learning experience.1. IntroductionOnline education platforms like Coursera rely heavily on user-generated reviews to attractprospective students and provide valuable insights to course developers (Dalipi et al., 2021).While numerical ratings offer a quick overview of student satisfaction, written reviews expandopinions, capturing nuanced feedback that can be analyzed for richer insights. Traditionalsentiment analysis tools, such as VADER (Hutto & Gilbert, 2014) and AFINN (Nielsen, 2011),are popular for their simplicity and efficiency, but they often fall short in interpreting the subtlecontext and

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Rawan Adnan Alturkistani, Virginia Tech Department of Engineering Education; Mohammed Seyam, Virginia Polytechnic Institute and State University

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Computers in Education Division (COED)

approaches and technologiesthat connect GenAI and PAs in K-12 education, providing a guide to those who are interestedin these fields.1. Introduction The use of Artificial Intelligent (AI) has become essential in our daily lives. Researchershave been investigating this area for a long time, but recently it has become widely used inalmost every field [72]. To keep up with this technology, researchers were interested inapplying AI techniques to education, particularly Generative AI (GenAI), which is atechnology that allows users to create content, initiate conversations, or seek particularinformation by generating content by prompts [52]. Before the AI boom, Pedagogical Agents(PAs) and Immersive technology were and still are some of the leading

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Hannah Oluwatosin Abedoh, Morgan State University; Blessing Isoyiza ADEIKA, Morgan State University; Pelumi Olaitan Abiodun, Morgan State University; Oludare Adegbola Owolabi P.E., Morgan State University; Abiola Olayinka Ajala, Morgan State University; OLUWATOYOSI OYEWANDE, Morgan State University

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Diversity

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Computers in Education Division (COED)

to process vast amounts of data, identify patterns, andgenerate insights allows educators to understand student needs better and tailor teaching methodsaccordingly [1].The transformative impact of AI in education is evident in its applications across various domains.Adaptive learning systems, for instance, employ algorithms to adjust content delivery based onindividual student performance, ensuring an optimized learning experience [2]. Additionally, AI-powered tools, such as virtual tutors and automated grading systems, reduce the administrativeburden on educators, enabling them to focus on more interactive and creative aspects of teaching[3].Among the most significant advancements in educational AI is the emergence of generative AI,which

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jessica Thomas, The Ohio State University

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Computers in Education Division (COED)

MATLAB courseIntroductionThe structure and timing of instructional material and courses has the potential for significantimpacts on student outcome and retention of content. An example can be found in the practice ofmassed learning, where learning and study time is uninterrupted, versus spaced learning, wherelearners encounter the same material multiple times with spaces in between sessions [1]. Thenecessity for repetition in learning has been recognized for well over a century, as mentioned inthe 1885 writings of Ebbinghaus, who observed that facts learned all at once before an exam andleft unreinforced afterwards are soon forgotten [2], [3]. The body of literature further indicatesthat spaced learning produces better recall than massed

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Siddharthsinh B Jadeja, University at Buffalo, The State University of New York; Corey T Schimpf, University at Buffalo, The State University of New York; A Lynn Stephens

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Computers in Education Division (COED)

present, technology interventions have spread inmost domains, including education. While AI experts are involved in designing intelligent systemsfor education and knowledge, learning scientists are interested in learning in real-worldenvironments [1] proposed a complex system at the intersection of Artificial Intelligence (AI) andLearning Sciences (LS) which sheds light on how to design software that can address the learner'sneeds to interact with that environment [2]. The complex system in [2] illustrates the advantage ofsimulation, but in real-world situations such a system faces challenges. In early literature,Intelligent Tutoring Systems (ITS) were used to create a learning environment, give support,provide feedback on requests, and evaluate

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Colin Jacob Soule, Bucknell University; Lea Wittie, Bucknell University; Sing Chun Lee, Bucknell University

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Computers in Education Division (COED)

andquantitative measures. Qualitatively, we will assess student engagement and self-efficacy throughLikert-scale surveys. Quantitatively, we will compare task completion times and scores to eval-uate learning outcomes. By automating tree validation and grading, the tool not only enhancesengagement but also improves teaching efficiency.1 IntroductionParse trees, or syntax trees, are essential in computer science education as they represent thehierarchical structure of programming language expressions. They are fundamental in under-standing syntax analysis, compiler construction, and language processing algorithms. However,traditional teaching methods often involve manually constructing syntax trees through static dia-grams or hand-drawn exercises. While

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Duncan Johnson, Tufts Center for Engineering Education and Outreach; Ethan E Danahy, Tufts University; Elliot Benjamin Roe, Georgia Institute of Technology

Tagged Divisions

Computers in Education Division (COED)

) is a powerful data structure for storing and interacting with ordereddata. Search, insertion, and deletion in a BST can be significantly more efficient thanlinearly-ordered data [1]. To fully teach students about BSTs, instructors must explain complexconcepts such as recursion, which have been shown to overwhelm middle school students,especially if taught in a manner that includes implementation details and syntax [2] [3].While CS education for younger students often focuses on fundamental programming syntax, webelieve it’s important for young students to engage with a vast array of core computing conceptsand skills in addition to learning programming language syntax. Our goal is to leverage the visualrepresentation of BSTs so students learn

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sumaya Binte Zilani Choya, George Mason University; Mihai Boicu, George Mason University

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Computers in Education Division (COED)

trustworthy detection techniques.IntroductionThe COVID-19 pandemic has changed education around the world, accelerating the acceptance ofremote exams and online learning environments. This unexpected shift also introduced additionaleducational challenges, such as upholding academic integrity during online exams [1]. In theabsence of conventional in-person supervision, educators must deal with the severe problem ofdishonest practices like plagiarism, impersonation, and unapproved collaboration which erode publicconfidence in online learning and diminish the value of genuine academic accomplishments [2]-[4].Preserving academic integrity is crucial to maintaining educational institutions' legitimacy andensuring student evaluations fairly represent

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Joshua Coriell, Louisiana Tech University; Ankunda Kiremire, Louisiana Tech University; Krystal Corbett Cruse, Louisiana Tech University; William C. Long, Louisiana Tech University

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Computers in Education Division (COED)

. The responses from thesurveys, interviews, and student performance, provide a baseline for future adjustments to thethree-course sequence to accurately assess students on their basic programming skills in a worldwhere LLMs are becoming more prevalent.IntroductionIn recent years, large language models (LLMs) have rapidly developed [1] and have quicklybecome a daily resource for professionals and students alike [2]. LLMs are able to enhance thelearning experience through rapid content generation [3]. One study that surveyed students tobetter understand their use of LLMs revealed that some utilized them to answer questions onhomework and exams, write outlines and essays, and identify errors in code. They also identifiedwhat they felt were

Conference Session

Computers in Education Division (COED) Poster Session (Track 1.A)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Maria Elena Truyol, Universidad Andres Bello, Santiago, Chile; Monica Quezada-Espinoza, Universidad Andres Bello, Santiago, Chile; Genaro Zavala, Tecnologico de Monterrey, Monterrey, Mexico; Universidad Andres Bello, Santiago, Chile; Claudia Bascur, Universidad Andres Bello, Santiago, Chile

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Diversity

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Computers in Education Division (COED)

: artificial intelligence, higher education, technology adoption, ethicalchallengesIntroduction and Related WorkThe use of artificial intelligence (AI)-based tools has grown significantly in highereducation [1-3], driven by AI’s benefits in personalized learning [4-6], task automation[7], and administrative optimization [8]. Chu et al. [1] emphasize AI’s role in adaptingpedagogy, predicting academic performance, and identifying at-risk students. However,challenges like data privacy, equitable access, and algorithmic transparency must beaddressed to ensure AI’s effective integration in education.The lack of adequate infrastructure and training hinders the equitable adoption of AI indeveloping countries, particularly in vulnerable environments (de la