Conference Session

Computers in Education Division (COED) Track 2.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Runu Proma Das, University of Georgia; Tathyana Moratti, University of Georgia; Shari Gasper, University of Georgia; Beshoy Morkos, University of Georgia

Tagged Divisions

Computers in Education Division (COED)

demographic survey using Qualtrics onthe computer screen, which reflects their education level, understanding of manufacturing class,and knowledge of Gen-AI tools in education. Following the survey's completion, participantsengaged in a selection process for manufacturing problem topics, where they chose and solvedthree out of four provided problems (e.g., Bending, Extrusion, Forging, Machining). Continuingwith the experiment procedure, participants solved the three selected problems through the pen-and-paper format. Participants were not informed about the origins (e.g., Textbook, AI,Textbook+AI) of the problem generation during the problem selection and solving period; suchdetails were revealed at the end of the experiment. On average, participants

Conference Session

Computer Engineering Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Timothy Sellers, Mississippi State University; Tingjun Lei, Mississippi State University; Chaomin Luo, Mississippi State University; Gene Eu Jan; Zhuming Bi, Purdue University, Fort Wayne

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Diversity

Tagged Divisions

Computers in Education Division (COED)

Paper ID #41764A Hybrid Pedagogy through Topical Guide Objective to Enhance StudentLearning in MIPS Instruction Set DesignTimothy Sellers, Mississippi State University Timothy Sellers received the B.S. degree in robotics and automation technology and applied science in electro-mechanical engineering from the Alcorn State University, Lorman, MS, USA in 2020. He is currently pursuing a Ph.D. degree in the Department of Electrical and Computer Engineering at Mississippi State University, Mississippi State, MS, USA. He is currently a Graduate Teaching Assistant for Senior Design II (ECE4542/ECE4522) and was for Advance

Conference Session

COED Programming Education 1: Students, Motivation, and Mastery

Collection

2023 ASEE Annual Conference & Exposition

Authors

Thomas Rossi, Penn State Behrend; Paul C. Lynch, Penn State Behrend

Tagged Divisions

Computers in Education Division (COED)

Economy Teaching Award in 2018. Dr. Lynch received the Outstanding Industrial Engineering Faculty Award in 2011, 2013, and 2015, the Penn State Industrial & Manufacturing Engineering Alumni Faculty Appreciation Award in 2013, and the Outstanding Advising Award in the College of Engineering in 2014 for his work in undergraduate education at Penn State. He worked as a regional production en- gineer for Universal Forest Products prior to pursuing his graduate degrees. He is currently an Associate Professor of Industrial Engineering in the School of Engineering at Penn State Erie, The Behrend College. ©American Society for Engineering Education, 2023The Combination Approach: Increasing Student

Conference Session

ML and Generative AI Tools and Policies

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sofia M Vidalis, Pennsylvania State University; Rajarajan Subramanian, Pennsylvania State University; Fazil T. Najafi, University of Florida

Tagged Divisions

Computers in Education Division (COED)

familiarizing and grouping past interactionsautomatically. (Rodos, June 2020). Some examples of AI used daily include voice assistants like Siri andAlexa. AI also customizes the daily feeds when one uses social media accounts and assists in our everydaylives.On November 30, 2022, the Chat Generative Pre-Trained Transformer (ChatGPT) occurred.ChatGPT is an extensive language model-based chatbot that OpenAI developed. Therefore, the useof chatbots is still considered new. They are already being utilized in education, providing studentswith immediate access to information and support. Additionally, AI-powered tools can assisteducators in tasks such as speech recognition for students with disabilities, improving lessondelivery, and providing feedback on

Conference Session

Cybersecurity Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mahmoud K Quweider, The University of Texas Rio Grande Valley; Liyu Zhang; Alexis Aaron De La Cruz

Tagged Divisions

Computers in Education Division (COED)

from the University of Texas at Brownsville. He recently obtained a Master’s in Digital Forensics from Champlain College after which he founded the B.Sc. in Cyber Security. After graduation, he was employed at several corporations including Pixera, a digital multimedia processing company in Cupertino, CA, 3COM, a networking and communication company in Schaumberg, IL, and Mercantec, an E-Commerce company in Naperville, IL. He has more than 50 publications in the field and has served as a reviewer and moderator for several scientific and educational journals and conferences. He joined UTB (UTRGV) in the Spring of 2000. His areas of interest include AI/Machine Learning, Networking and Cyber Security, and Digital

Conference Session

Cybersecurity Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Walter W Schilling Jr., Milwaukee School of Engineering

Tagged Divisions

Computers in Education Division (COED)

companies in the Midwest. In addition to one U.S. patent, Schilling has numerous publications in refereed international conferences and other journals. He received the Ohio Space Grant Consortium Doctoral Fellowship and has received awards from the IEEE Southeastern Michigan and IEEE Toledo Sections. He is a member of IEEE, IEEE Computer Society and ASEE. At MSOE, he coordinates courses in software verification, real time systems, operating systems, and cybersecurity topics. ©American Society for Engineering Education, 2024Increasing Faculty Cybersecurity Experience through Externship ExperienceAbstractIn modern world, cybersecurity has become an increasingly important field. Graduates withexperience

Conference Session

Cybersecurity Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Anyi Liu, Oakland University; Bruce R Maxim, University of Michigan, Dearborn; Xiaohong Yuan, North Carolina A&T State University; Yuan Cheng, Grand Valley State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

among mobiledevices and sensors. However, the increasing attention to pervasive computing introduces newsecurity issues and challenges. Thus, equipping students with the knowledge and skills to handlethe security issues of pervasive computing is crucial yet challenging for educators.Prior efforts have shown initial success in training students with hands-on cybersecurity labs fo-cusing on cloud and mobile computing. However, some fundamental knowledge areas (KAs) andknowledge units (KUs) have not been adequately studied. While significant effort has been in-vested in constructing cloud-based infrastructures or testbeds 1,2,3,4 , network security labs 5,6 , andmobile security labs 7 , educational materials related to specific topics of pervasive

Conference Session

Cybersecurity Topics

Collection

2024 ASEE Annual Conference & Exposition

Authors

Heena Rathore, Texas State University; Henry Griffith, San Antonio College

Tagged Divisions

Computers in Education Division (COED)

, Games, and Gamification in Security Education, (3GSE 14), 2014. 10[10] Michael Serra, “Pirate Math Treasure hunt puzzles with cryptography”, CMC-S Palm Springs Fall, 2012.[11] D.W. Johnson, and R.T. Johnson, “Social skills for successful group work”. MAA notes, pp.201-204, 1997.[12] M. Sweet, and L.K. Michaelsen eds., “Team-based learning in the social sciences and hu- manities: Group work that works to generate critical thinking and engagement”. Taylor & Francis, 2023.[13] Cryptography Scavenger Hunt, https://inl.gov/content/uploads/2023/04/Cryptography- Scavenger-Hunt-Lesson-Plan.pdf [Accessed on August 17, 2023][14] Jonestastic Math, Cryptography Worksheet and Scavenger

Conference Session

Computers in Education Division (COED) Best of CoED Paper Session (Track 1.B)

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jesan Ahammed Ovi, Colorado School of Mines; Gabriel Tomas Fierro, Colorado School of Mines; C. Estelle Smith, Colorado School of Mines

Tagged Divisions

Computers in Education Division (COED)

focuses on human-computer interaction, human-AI interaction, and social and collaborative computing. Since 2023, Dr. Smith has been continuously involved in efforts to assess and understand student adoption of Generative AI (GenAI) across campus. She participated in writing institution-wide policies for Mines, and she has given numerous guest lectures and organized numerous workshops on the ethics and use of GenAI in engineering education. ©American Society for Engineering Education, 2025 Assessing Student Adoption of Generative Artificial Intelligence across Engineering Education from 2023 to 2024AbstractGenerative Artificial Intelligence (GenAI) tools and models have the

Conference Session

Computers in Education Division (COED) Poster Session

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ibukun Samuel Osunbunmi, Pennsylvania State University; Stephanie Cutler, Pennsylvania State University; Viyon Dansu, Florida International University; Yashin Brijmohan, University of Nebraska, Lincoln; Bolaji Ruth Bamidele, Utah State University; Abasiafak Ndifreke Udosen, Purdue University, West Lafayette; Lexy Chiwete Arinze, Purdue University, West Lafayette; Adurangba Victor Oje, University of Georgia; Deborah Moyaki, University of Georgia; Melissa J Hicks, Pennsylvania State University; Bono Po-Jen Shih, Pennsylvania State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

Ethics Institute and the Leonhard Center for Enhancement of Engineering Education—to facilitate exchange and collaboration between philosophers and engineers. Prior to joining Penn State, he was a postdoctoral research fellow at the Science History Institute working on the history of engineering ethics education. Shih earned his PhD and MS in science and technology studies (STS) from Virginia Tech. He also has a graduate certificate in engineering education (ENGE) from Virginia Tech and a Bachelor of Science in electrical engineering from National Taiwan University. ©American Society for Engineering Education, 2024 Generative Artificial Intelligence (GAI) Assisted Learning: Pushing the

Conference Session

COED Modulus Topics

Collection

2023 ASEE Annual Conference & Exposition

Authors

Adam Steven Weaver, Utah State University; Jack Elliott, Utah State University

Tagged Divisions

Computers in Education Division (COED)

. (Engineering Education) graduate student at Utah State University. His M.S. research is in experimental fluid dynamics, his Ph.D. work ex- amines student social support networks in engineering education, and his other research activities include developing low-cost technology-based tools for improving fluid dynamics education. ©American Society for Engineering Education, 2023 Uncovering Student Social Networks: Entity Resolution Methods for Ambiguous Interaction DataIntroduction Over the last century, cognitive psychologists have proposed that social interactions are akey component of student learning [1]–[4]. For example, Albert Bandura’s Social LearningTheory [5] posits

Conference Session

COED Modulus Topics

Collection

2023 ASEE Annual Conference & Exposition

Authors

Mudasser Fraz Wyne, National University; Alireza Farahani; Esmaeil Atashpaz-Gargari; Lu Zhang, National University

Tagged Divisions

Computers in Education Division (COED)

such as faculty qualification and interests in topics, as well as their availability andpreference in teaching onsite or online classes. The 0/1 integer programming solution had elegantconstraint formulations and provided flexibility for the inclusion of complex constraints.However, the complexity of the problem rapidly grows with an increase in the number of variablesof the problem. This prompted the investigation into alternative solution approaches that caneffectively handle practical problems with a greater number of faculty members, courses, andconcurrent programs to be scheduled. Scheduling and staffing in general have been studied in various papers and different applicationdomains. Authors in [2] proposed a new algorithm for staffing

Conference Session

ML and Generative AI Tools and Policies

Collection

2024 ASEE Annual Conference & Exposition

Authors

Jason M. Keith, Mississippi State University; Amin Amirlatifi, Mississippi State University; Shahram Rahimi; Subash Neupane, Mississippi State University; Sudip Mittal

Tagged Divisions

Computers in Education Division (COED)

this tool, from the perspective of a prospectivestudent, a current student with academic affairs needs and a current student with student affairsneeds.We have functionality to handle: - general queries (meant for alumni, parents and general users external to the university) - respond to information about scholarships and financial aid - to interact with prospective students to give more tailored departmental selections - to handle students considering multiple majors (or deciding between them)We can also handle undecided students by asking some self-reflection questions. These include: - "What subjects or topics are you genuinely interested in or excited about?" - "What are some activities or projects where you feel most confident and

Conference Session

ML and Generative AI Tools and Policies

Collection

2024 ASEE Annual Conference & Exposition

Authors

Alyson G. Eggleston, Pennsylvania State University; Robert J. Rabb P.E., Pennsylvania State University

Tagged Divisions

Computers in Education Division (COED)

GPT-2. OpenAI recommends using in conjunction with additional approaches. (95% accuracy) 2) Giant Language Model Text Room (GLTR) [24]: (From Harvard and IBM) (Strobelt & Gehrmann, 2019) Detects likelihood that words were predicted by a bot. Color-coded results aid interpretation. 3) GPTZeroX [25]: Created by Princeton University student Edward Tian for educators. Supports large text inputs and file uploading, claims to identify portions written by AI. Scores on “perplexity” and “burstiness, where perplexity is a measure of randomness and likelihood the next word was bot-generated, and burstiness refers to variations in sentential length and complexity, as these are known features of human writing

Conference Session

Teaching with ML and Generative AI

Collection

2024 ASEE Annual Conference & Exposition

Authors

Abdulrahman AlRabah, University of Illinois Urbana-Champaign; Sophia Yang, University of Illinois Urbana-Champaign; Abdussalam Alawini, University of Illinois Urbana-Champaign

Tagged Divisions

Computers in Education Division (COED)

upper-levelundergraduate and graduate students at the University of Illinois Urbana-Champaign. The datasetcontains a mix of 100 correct and 400 incorrect submissions and underwent an extensivefine-tuning process with OpenAI’s advanced GPT-3.5-turbo-1106 model [15]. Therefore, ourresearch questions include: • RQ1: How can a proof of concept be designed and implemented to assess the feasibility of utilizing a generative AI model for providing semantic error feedback in educational settings, ensuring that the system avoids disclosing correct answers while enhancing the learning experience? • RQ2: How does the feedback from the fine-tuned GPT model differ in specificity and relevance compared to standard GPT models in the

Conference Session

Teaching with ML and Generative AI

Collection

2024 ASEE Annual Conference & Exposition

Authors

Han Kyul Kim, University of Southern California; Aleyeh Roknaldin, University of Southern California; Shriniwas Prakash Nayak, University of Southern California; Xiaoci Zhang, University of Southern California; Muyao Yang, University of Southern California; Marlon Twyman, University of Southern California; Angel Hsing-Chi Hwang, Cornell University; Stephen Lu, University of Southern California

Tagged Divisions

Computers in Education Division (COED)

Manufacturing Engineering at University of Southern California. His current professional interests include design thinking, collaborative engineering, technological innovation, and education reform. He has over 330 ©American Society for Engineering Education, 2024 ChatGPT and Me: Collaborative Creativity in a Group Brainstorming with Generative AIIntroductionThe emergence of generative AI (genAI), exemplified by ChatGPT, offers unprecedentedopportunities to the education system. However, as this technological advancement gainsmomentum, concerns surrounding hallucination [1, 2] and academic integrity [3, 4] have beenraised, casting doubt on its applicability in educational

Conference Session

Computers in Education Division (COED) Track 2.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Venkata Alekhya Kusam, University of Michigan - Dearborn; Zheng Song, University of Michigan - Dearborn; Khalid Kattan, University of Michigan - Dearborn; Bruce R Maxim, University of Michigan - Dearborn

Tagged Divisions

Computers in Education Division (COED)

, zhesong, bmaxim, kkattan}@umich.edu Department of Computer and Information Science, University of Michigan-Dearborn, USAAbstractThis paper presents an investigation into the use of Generative AI (GenAI), specifically ChatGPT,to automate quiz generation in higher education by conducting a case study in a graduateArtificial Intelligence (AI) course. The study aims to compare the quality and relevance ofAI-generated quizzes with manually created ones, addressing a critical question in computerscience education: Can Generative AI effectively support educators in creating assessments thatalign with course learning objectives?We conducted the study in a graduate-level AI course, which involved 47 students, one instructorand one

Conference Session

Simulations and Virtual Learning

Collection

2024 ASEE Annual Conference & Exposition

Authors

J Cecil, Oklahoma State University

Tagged Divisions

Computers in Education Division (COED)

university level [28]This paper discusses the design and impact of next generation Virtual Learning Environments inteaching engineering concepts to university students (both undergraduate and graduate students).The term Extended Reality (XR) refers to 3 types of virtual environments: Virtual Reality (VR)Environments, Augmented Reality and Mixed Reality (MR) environments. In this paper, theimpact of adopting VR and MR based learning environments to teach engineering concepts isdiscussed. In general, such Virtual Learning Environments (VLEs) have the potential to be usedto teach engineering topics and concepts ranging from robotics assembly to more complex spacesystems design. Virtual Learning Environments (VLEs) can be viewed as subset of

Conference Session

COED: AI and ML Topics

Collection

2023 ASEE Annual Conference & Exposition

Authors

Shatha Jawad, National University; Ronald P. Uhlig, National University; Pradip Peter Dey; Mohammad N. Amin, National University; Bhaskar Sinha, National University

Tagged Divisions

Computers in Education Division (COED)

back on trackfaster by alerting teachers to potential problems. This paper proposes a Deep Learning NeuralNetworks approach that helps students select their best-fit specialization in a specific category.Deep learning is a subset of machine learning, but it can determine whether a prediction isaccurate through its own neural network- no human help is required [1]. The proposed systemwill use a dataset that contains student data that is related to the general education coursesrequired for their program, such as grades, the number of hours spent on each course's materials,the opinion of the student about the content of each course, and the course(s) that the studentenjoyed the most. Additional data will be included in the dataset such as the

Conference Session

COED: AI and ML Topics

Collection

2023 ASEE Annual Conference & Exposition

Authors

Zhao Sun, Hampton University; Laura Camila Peralta; Myles Anthony Ragins; Niara Renee Chaney

Tagged Divisions

Computers in Education Division (COED)

challenges in course development are as the following: 1. How to make the course comprehensive enough to cover the fundamentals of machine learning and robotics, while also being understandable to students with varying levels of experience and knowledge. © American Society for Engineering Education, 2023 2023 ASEE Midwest Section Conference 2. How to find the right balance between teaching the fundamentals of machine learning and introducing more advanced topics? And how to make the course up to date with the latest advancements in the field, while also providing a solid foundation in the basics. 3. How to incorporate hands-on activities and projects to help students

Conference Session

COED: AI and ML Topics

Collection

2023 ASEE Annual Conference & Exposition

Authors

Nebojsa I. Jaksic, Colorado State University, Pueblo; Bahaa Ansaf, Colorado State University, Pueblo

Tagged Divisions

Computers in Education Division (COED)

milliseconds which is not attainable when using the cloud computing paradigm.Instead, edge computing, which occurs physically close to the sensors and actuators, isimplemented. Thus, it is important for engineering students to gain hands-on experience with edgecomputing devices capable of performing AI tasks.What follows are sections on Previous Work justifying experiential learning in general, then,Description of AI Development Kits, Comparative Analysis, and Summary and Conclusions.2. Previous Work This section provides a short review of education literature related to the developments ofan experientially-based educational continuum as well as the AI in edge computing. Over 80 yearsago, Dewey [1] recognized that practical laboratory

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

Tagged Divisions

Computers in Education Division (COED)

to help a student understand a specific topic and trusting the resource poses a challenge.Still, faculty have found useful ways to integrate these into their courses as assessments forprofessional development [16], pre-class lectures [17], or as an activity like developing homeworkproblems based on a video [18].Generative AI tools. Beyond traditional outside resources like problem solutions and videos,modern large language models (LLMs) are quickly gaining popularity in engineering education[19]. As with other technologies, there is speculation on where generative AI fits in education. Atface value, generative AI tools are the next step in on-demand help provided by problem-solutionwebsites, and it is not difficult to see why, given the

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

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

Paper ID #47960BOARD # 98: WIP: Understanding Patterns of Generative AI Use: A Studyof Student Learning Across University CollegesDaniel Kane, Utah State University Daniel Kane is a third-year Ph.D. student in the department of engineering education at Utah State University. His research interests include spatial ability, accessibility for students with disabilities, artificial intelligence in education, and enhancing electric vehicle charging system infrastructure. Daniel has contributed significantly to the development of the Tactile Mental Cutting Test (TMCT) which is a significant advancement in assessing

Conference Session

Spotlight on Diverse Learners

Collection

2024 ASEE Annual Conference & Exposition

Authors

Manuel A. Pérez-Quiñones, University of North Carolina; Debarati Basu, Embry Riddle Aeronautical University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

College (HBCU), which typically has 90% of their population African-American, might not be interested in seeing differences across racial groups (they are pretty homogeneous). Instead, their social groups might include first-generation, gender, or other social markers. • Evaluate the outcome of your educational mission according to the social groups that you serve. The outcome could be grade distribution in your introductory courses, enrollment vs graduation, retention rate, and even first employment after obtaining a degree from your program. You should not find disparate outcomes across social groups. • If you determine that there are differences in the outcomes across the social groups of interest, then

Conference Session

COED: Computing in K-12 / Early Childhood Education

Collection

2023 ASEE Annual Conference & Exposition

Authors

Safia Malallah, Kansas State University; Lior Shamir, Kansas State University; William Henry Hsu, Kansas State University; Joshua Levi Weese, Kansas State University; Salah Alfailakawi, Kansas State University

Tagged Divisions

Computers in Education Division (COED)

primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science ed- ucation in Kansas including PK-12 model standards in 2019 with an implementation guide the following year. Work on CS teacher endorsement standards are also being developed. Dr. Weese has developed, organized and led activities for several outreach programs for K-12 impacting well more than 4,000 stu- dents.Mr. Salah Alfailakawi, Kansas State University Salah Alfailakawi is a PhD student in Educational Technology (ET) Graduate Programs at Kansas State University’s College of Education. His areas of interest include social/cultural issues in ET, the impact of ET on learners and teachers, as well

Conference Session

Spotlight on Diverse Learners

Collection

2024 ASEE Annual Conference & Exposition

Authors

Isabella Gransbury, North Carolina State University; Monica M. McGill, Institute for Advancing Computing Education; Leigh Ann DeLyser

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

the CER community through the ACM SIGCSE, IEEE CollaboretecForum, NSF INCLUDES forum, and CSTA Discussion Forum. We also recruited participantsfrom the CS Graduate Student and CSforALL slack channels. Finally, we emailed 889 authors ofpublished K-12 CER literature from the publicly available article database on the K-12Computing Education Research Resource Center [25]. The survey was distributed on January 4th,2023 and closed on January 25th, 2023. If participants completed the survey and gave their emailaddress, they were entered into a random drawing for one of four $50 dollar gift cards.3.3 ParticipantsFor a participant’s response to be included in the final analysis, the participants had complete theopen-ended barrier question in the

Conference Session

Computers in Education Division (COED) Track 6.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Abdulrahman AlRabah, University of Illinois at Urbana - Champaign; Zepei Li, University of Illinois at Urbana Champaign; Meredith Blumthal, University of Illinois at Urbana - Champaign; Sotiria Koloutsou-Vakakis, University of Illinois Urbana-Champaign; Volodymyr Kindratenko, University of Illinois Urbana-Champaign; Tomasz Kozlowski, University of Illinois Urbana-Champaign; Abdussalam Alawini, University of Illinois Urbana - Champaign

Tagged Divisions

Computers in Education Division (COED)

Language Models for Education,” Learning and Individual Differences, vol. 103, p. 102274, 2023.[16] M. Abedi, I. Alshybani, M. R. B. Shahadat, and M. Murillo, “Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education,” Qeios, 2023.[17] D. Baidoo-Anu and L. O. Ansah, “Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning,” Journal of AI, vol. 7, no. 1, pp. 52–62, 2023.[18] F. Osasona, O. O. Amoo, A. Atadoga, T. O. Abrahams, O. A. Farayola, and B. S. Ayinla, “Reviewing the Ethical Implications of AI in Decision Making Processes,” International Journal of Management &

Conference Session

Computers in Education Division (COED) Track 4.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Marlee Jacobs, Utah State University; Daniel Kane, Utah State University; Rosemary Yahne, Utah State University; Wade H Goodridge, Utah State University

Tagged Divisions

Computers in Education Division (COED)

variety of complex technical topics, students face challenges in understandingand applying theoretical knowledge. AI technologies such as AI-assisted tutoring systems,performance predictions models, and generative AI tools are effective in enhancing studentinteractions with engineering curriculum improving student understanding and engagement[1][2]. By enabling real-time feedback, personalized learning experiences, and interactiveproblem-solving environments, AI tools are creating new opportunities for engineering education[3][4].The advancement of AI technology, particularly generative AI systems such as ChatGPT fosterscritical thinking and collaboration among students. In a study done by Abril students used AItools such as ChatGPT to obtain and

Conference Session

Computers in Education Division (COED) Track 3.E

Collection

2025 ASEE Annual Conference & Exposition

Authors

Samuel B Mazzone, Marquette University; Dennis W Brylow, Marquette University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

development process began with identifying equitable WD topics that wouldallow all students to embrace their own interests, identities, or cultures. Culturally responsive andsustaining curriculum design is a key pillar of equitable CS education 28 . Topics include creatingwebsites on various interests, such as favorite animals or board games. They also cover practicalscenarios, like selecting items for their school garden or writing articles as if contributing to theirschool newspaper. Later in the curriculum, students design websites for fictional yet realisticbusinesses, such as an event organizing service or a photography business. Legend: HTML Concepts CSS Concepts Lesson Number Concept Covered

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

Tagged Divisions

Computers in Education Division (COED)

Singapore, 2019, pp. 171–181.[15] A. I. Adekitan and O. Salau, “The impact of engineering students’ performance in the first three years on their graduation result using educational data mining,” Heliyon, vol. 5, no. 2, p. e01250, 2019.[16] O. W. Adejo and T. Connolly, “Predicting student academic performance using multi-model heterogeneous ensemble approach,” J. Appl. Res. High. Educ., vol. 10, no. 1, pp. 61–75, 2018.[17] M. Adil, F. Tahir, and S. Maqsood, “Predictive analysis for student retention by using neuro-fuzzy algorithm,” in 2018 10th Computer Science and Electronic Engineering (CEEC), 2018, pp. 41–45.[18] A. A. Sabourin, J. C. Prater, and N. A. Mason, “Assessment of mental health in doctor of pharmacy