Conference Session

Computers in Education Division (COED) Track 3.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Yuxuan Chen, University of Illinois Urbana-Champaign; Chenyan Zhao, University of Illinois Urbana-Champaign; Kangyu Feng, University of Illinois Urbana-Champaign; Mattox Alan Beckman, University of Illinois Urbana-Champaign; Mariana Silva, University of Illinois Urbana-Champaign

Tagged Divisions

Computers in Education Division (COED)

conceptual understanding, pseudocode interpretation, and basic Python programmingtopics. In Fall 2024, a total of 200 upper-level engineering students enrolled in structural analysis,fluid mechanics, and computational mechanics courses completed the CS1 assessment in a timeslot of 50 minutes. Table 5 shows the gender distribution of these students. Note that these (a) Part 1: NumPy (b) Part 3: InitializeEquationFigure 3: Examples from Jupyter notebook sections of the first mini-project. Students start withan introduction to NumPy and progress to creating arrays to support the assembly process in theFinite Element Method.students had not experienced the redesigned CS 101 course due to their academic

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

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Computers in Education Division (COED)

.”[2] Michael A. Eierman and George C.Pholip, “The Task of Problem Formulation,” Int J Inf Technol Decis Mak, vol. 2, no. 03, pp. 353–372, 2003.[3] C. D. Schunn, P. B. Paulus, J. Cagan, and K. Wood, “Final report from the NSF innovation and discovery workshop; The scientific basis of individual and team innovation and discovery,” 2006.[4] D. A. Cowan, “Developing a Process Model of Problem Recognition,” 1986.[5] J. A. M. Boulet, A. Lumsdaine, and J. F. Wasserman, “The Transition from Textbook Problems to Realistic Problems.”[6] A. R. Sloboda, “The effect of context on student perceptions of homework-style problems in engineering,” in ASEE Annual Conference and Exposition, Conference Proceedings

Conference Session

Computers in Education Division (COED) Track 4.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Reine Azzi, Lebanese American University

Tagged Divisions

Computers in Education Division (COED)

, 2023. [Online]. Available: https://doi.org/10.1016/j.caeai.2023.10017921. D. Mah and N. Groß, "Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs," Int. J. Educ. Technol. High. Educ., vol. 21, no. 1, 2024. [Online]. Available: https://doi.org/10.1186/s41239-024-00490-122. J. Rawls, The original position. A theory of justice, Harvard University Press, 2009, p. 118.23. B. Al-haimi, F. Hujainah, D. Nasir, and E. Alhroob, "Higher Education Institutions with Artificial Intelligence: Roles, Promises, and Requirements," in Applications of Artificial Intelligence in Business, Education and Healthcare, 2021. [Online]. Available: https://doi.org/10.1007

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)

. Guerra, and S. Duran Ballen, “ChatGPT to Support Critical Thinking inConstruction-Management Students,” in 2024 ASEE Annual Conference & ExpositionProceedings, Portland, Oregon: ASEE Conferences, Jun. 2024, p. 48459. doi: 10.18260/1-2--48459.[6] S. Vidalis, R. Subramanian, and F. Najafi, “Revolutionizing Engineering Education: TheImpact of AI Tools on Student Learning,” in 2024 ASEE Annual Conference & ExpositionProceedings, Portland, Oregon: ASEE Conferences, Jun. 2024, p. 47950. doi: 10.18260/1-2--47950[7] B. Qureshi, “Exploring the Use of ChatGPT as a Tool for Learning and Assessment inUndergraduate Computer Science Curriculum: Opportunities and Challenges”. 2023,https://arxiv.org/abs/2304.11214[8] M. O. Agbese, M. Rintamaki, R

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)

: (A) O(n2 ) (A) High branching and high depth (B) O(bd ) (B) Low branching and high depth (C) O(db ) (C) Low branching and low depth (D) O(n log n) (D) High branching and low depth Correct Answer: C Correct Answer: D Q2 Which of the following is a key difference between informed and Uniform Cost Search expands the node with the: uninformed search algorithms? (A) Lowest heuristic estimate. (A) Informed search uses a heuristic function (B) Shortest path cost so far. (B

Conference Session

Computers in Education Division (COED) Track 6.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Laura Albrant, Michigan Technological University; Michelle E Jarvie-Eggart P.E., Michigan Technological University; Leo C. Ureel II, Michigan Technological University

Tagged Divisions

Computers in Education Division (COED)

taken to resolve each and the sequence of changes made.5 ResultsFor the following analysis, the three intervention assignments for the Spring 2023 will be calledA, B, and C. The three assignments for Fall 2023 will be D, E, and F. The three assignments forSpring 2024 will be G, H, and I. Finally, the three assignments from Fall 2024 are referred to asJ, K, and L. Table 1 shows a general overview of the submissions made by students across theassignments/semesters. Submissions By Assignment Assignment Total Min Max Median Mean Spring 2023 A 21 0 7 0 0.32

Conference Session

Computers in Education Division (COED) Track 6.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ryan Edward Dougherty, United States Military Academy; Maria R. Ebling, United States Military Academy

Tagged Divisions

Computers in Education Division (COED)

one’s own writing.AcknowledgmentsWe thank the CS class of 2024 for helping support CS education research. The views ex-pressed in this article are those of the authors and do not reflect the official policy or positionof the Department of the Army, Department of Defense, or the U.S. Government.References [1] A. B. of Delegates Computing Area. Criteria for Accrediting Computing Programs. Baltimore, MD, Oct. 2022. url: https://www.abet.org/wp- content/uploads/ 2023/05/C001_CAC-Criteria_2023-2024.pdf. [2] V. W. Pine and M. L. Barrett. “What kinds of communication are required on the job?” In: J. Comput. Sci. Coll. 21.2 (Dec. 2005), pp. 313–321. issn: 1937-4771. [3] K. Anewalt and J. Polack. “Industry Trends in Software

Conference Session

Computers in Education Division (COED) Track 5.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Maverick Berner, Marquette University; Darcy Ronan, Sacred Heart University; Dennis W Brylow, Marquette University; Maximus Berner, Marquette University

Tagged Divisions

Computers in Education Division (COED)

” accessible,” in Proceedings of the 12th International Conference on Interaction Design and Children, ser. IDC ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 635–638. [Online]. Available: https://doi.org/10.1145/2485760.2485883 [9] M. Worsley and D. Bar-El, “Inclusive making: designing tools and experiences to promote accessibility and redefine making,” Computer Science Education, vol. 32, no. 2, pp. 155–187, 2022. [Online]. Available: https://doi.org/10.1080/08993408.2020.1863705[10] M. Resnick, J. Maloney, A. Monroy-Hern´andez, N. Rusk, E. Eastmond, K. Brennan, A. Millner, E. Rosenbaum, J. Silver, B. Silverman et al., “Scratch: programming for all,” Communications of the ACM, vol. 52, no. 11, pp. 60–67

Conference Session

Computers in Education Division (COED) Track 5.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Audrey Marie DeHoog, University of Florida; Jeremiah J Blanchard, University of Florida; Amy Wu, University of Florida; John R. Hott, University of Virginia

Tagged Divisions

Computers in Education Division (COED)

able to identify trends in programming languages andenvironments used, as well as course policies in regards to collaboration. The results of the surveyand our analysis provide insight into the disciplines in which computing coursework is becomingmore prevalent, use cases within those disciplines, and what techniques they employ. Our findingswill support future research of fundamental programming courses beyond computingdisciplines.References [1] P. K. Chilana, C. Alcock, S. Dembla, A. Ho, A. Hurst, B. Armstrong, and P. J. Guo, “Perceptions of non-CS majors in intro programming: The rise of the conversational programmer,” in 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp. 251–259, Oct. 2015. [2] P

Conference Session

Computers in Education Division (COED) Track 2.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sean P Brophy, Purdue University at West Lafayette (COE); Fadhla Binti Junus, Purdue Engineering Education

Tagged Divisions

Computers in Education Division (COED)

Paper ID #48290Analyzing Feedback of an AI tool for formative feedback of Technical WritingabilitiesDr. Sean P Brophy, Purdue University at West Lafayette (COE) Dr. Sean Brophy is a learning scientist, computer scientists and mechanical engineering who design learning environments enhances with technology. His recent research in engineering design focuses on students’ development of computational thinking through physical computing. His work involves students’ design of smart systems that integrate both hardware and software to achieve a client’s needs. In this work students communicate their ideas through proposal

Conference Session

Computers in Education Division (COED) Track 5.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jina Wilde, The University of Texas at San Antonio; Michael Zawatski, The University of Texas at San Antonio; Darean Wilde, The University of Texas at San Antonio; Emiliano Beltran, The University of Texas at San Antonio; Amanda S. Fernandez, The University of Texas at San Antonio; Timothy Yuen, The University of Texas at San Antonio

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

University of Texas at San Antonio Student researcher interested in how the delivery of professional development can impact a teacher’s ability to influence students to pursue STEM (and more specifically, CS-related) degrees.Dr. Amanda S. Fernandez, The University of Texas at San Antonio Amanda S. Fernandez an Assistant Professor of Computer Science at the University of Texas at San Antonio.Dr. Timothy Yuen, The University of Texas at San Antonio Timothy T. Yuen is the Associate Dean for Undergraduate Studies in the College of Sciences at the University of Texas at San Antonio. ©American Society for Engineering Education, 2025 Computer Science Professional Development for Middle and

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kwansun Cho, University of Florida; Sung Je Bang, Texas A&M University; Syeda Fizza Ali, Texas A&M University; Asefeh Kardgar, Texas A&M University; Saira Anwar, Texas A&M University

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Computers in Education Division (COED)

web-assisted personalized learning.Sung Je Bang, Texas A&M University Sung Je Bang is a Ph.D. candidate in Interdisciplinary Engineering at Texas A&M University, within the Department of Multidisciplinary Engineering. He serves as a graduate research assistant on multiple projects, where he focuses on user experience and psychological aspects of technology. His research interests include artificial intelligence, large language models, user experience design, and engineering education.Syeda Fizza Ali, Texas A&M University Syeda Fizza Ali is currently pursuing her PhD in Interdisciplinary Engineering at Texas A&M University. She works as a graduate research assistant at the Department of

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)

. (b) Coding Use Cases. (c) Writing Use Cases.Figure 2: Use Cases for LLM-Chatbots in Engineering Education in 2024 (n = 651). Y-axes: Usecase labels. X-axes: Frequencies normalized by # of respondents per department cluster.their knowledge and academic routines, rather than less savory motivations related to grades,overwhelm, etc. The most commonly reported motivations were to enhance understanding andgain deeper insights into subjects (n = 82, 57.7%), to improve the quality of academic orprofessional work (n = 57, 40.1%), and curiosity to experiment with cutting-edge AI tools(n = 47, 33.1%). These findings underline the multifaceted appeal of LLM-powered chatbots inengineering education, driven by a mix of

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)

Paper ID #47884BOARD # 82: WIP: A scoping review of AI agent systems supporting students’navigation of open-ended problems: Towards a model to support design thinkingMr. Siddharthsinh B Jadeja, University at Buffalo, The State University of New York Siddharthsinh Jadeja is a passionate and driven engineering education graduate research student in the Department of Engineering Education at the University at Buffalo, deeply committed to enhancing engineering education through innovative, human-centric design approaches. With a strong foundation in engineering principles and a keen interest in educational methodologies

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Alan Marchiori, Bucknell University

Tagged Divisions

Computers in Education Division (COED)

; def testbench():14 reg a, b; wire out; a = reg(); b = reg()15 andgate device (out, a, b); device = andgate(a, b)16 initial begin def monitor():17 $monitor("a=%b, b=%b, out=%b", print("a=%s, b=%s, out=%s" %\18 a, b, out); (a(), b(), device()))19 // step through the inputs # step through the inputs20 #10 a = 0; b = 0; a.set(0); b.set(0); monitor()21 #10 a = 0; b = 1; a.set(0); b.set(1); monitor()22 #10 a = 1; b = 0; a.set(1); b.set(0); monitor

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)

overlaying sine waves with randomized amplitude and frequency following thesespecifications: (a) Use randi() to generate a random integer between 1 and 10, and store it in the variable A (amplitude). (b) Use rand() to generate a random decimal value between 0.1 and 1, and store it in the variable f (frequency). (c) Create a range of 200 equally spaced values stored in the variable t (time) between 0 and 10 seconds. (d) Create the array y of the sine wave using the formula y = A sin(2πf t) (e) Make a plot of t and y, where t is the horizontal axis and y is the vertical axis. Use a solid line if the frequency is less than 0.5 Hz, and dashed line if it is greater than or equal

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

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

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Computers in Education Division (COED)

our students and how their grades correlate with their mindset,we divided the students into four groups based on the grade they received. As seen in Table 1,there were 127 students in the “A” group, 70 in “B”, 25 in “C” and 12 in “D”.To answer RQ1, we found that there were no differences between the groups in students’value or sense of belonging at the start of the course, suggesting that these measures aloneat the beginning are not strong enough to predict course outcomes. However the A studentshad higher expectations to do well. This is perhaps because some students come in withprior background knowledge, which increases their Expectancy measure and performance.It is also possible that students who expect to do well will do better

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Emre Tokgoz, State University of New York - Farmingdale; Alyssa Xiang

Tagged Divisions

Computers in Education Division (COED)

andMann-Whitney tests.Part A. What environmental factors impact (i.e. motivate or discourage) you to enjoy (i.e. like ordislike) an online course? Please mark the factors below that you believe impact you. 1. My computer 6. The organization of the course 2. My cell phone 7. Amount of feedback/support from 3. Professor professor 4. The educational environment 8. Amount of course work 5. Level of engagement in coursePart B. Which of the following impacts your learning from courses you completed? Please markthe factors below that you believe impact you. 1. Family

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Peter Jamieson, Miami University; Ricardo Ferreira, Universidade Federal de Viçosa; José Nacif, Universidade Federal de Viçosa

Tagged Divisions

Computers in Education Division (COED)

s e l f . prev clock = clock15 return self . state16 . . . SNIPPETS . . .17 # Connect widgets18 clock widget . observe ( update display , ’ value ’ )19 data widget . observe ( update display , ’ value ’ )20 # I n i t i a l display21 update display ()22 # Create layout23 w i d g e t s b o x = w i d g e t s . HBox ( [ c l o c k w i d g e t , d a t a w i d g e t ] )24 display ( widgets box )25 display ( output widget ) This code creates an interactive D Flip-Flop with the following features: Two checkbox widgets for Clock and Data inputs An SVG visualization showing: The D Flip-Flop symbol Input lines with colored indicators for Clock and Data states An output LED that changes

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Muhammed Yakubu, University of Toronto; Jasnoor Guliani, University of Toronto; Nipun Shukla, University of Toronto; Dylan O'Toole; Hamid S Timorabadi P.Eng., University of Toronto

Tagged Divisions

Computers in Education Division (COED)

generally viewed as a valuable supplement to course material,helpful for reinforcing learned material, applying concepts, and, to some extent, aiding in testpreparation. However, some students noted a mismatch between the AI-generated questions andthe actual test content, suggesting a need for better alignment with learning objectives andassessment criteria. Additionally, some feedback indicated that the questions might be moreuseful for test preparation than for promoting deep understanding, and that the tool could beimproved by tailoring the difficulty and content to students' diverse backgrounds and priorknowledge. See Appendix B for detailed participant testimonials for each section.7.0 - ChallengesThis study encountered several challenges. A

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Susannah Cooke, ANSYS, Inc.; Kaitlin Tyler, ANSYS, Inc.

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

use of innovative teaching methods includingincreased usage of programing in Electrical/Electronics engineering. Taking advantage of thePython libraries available with simulation tools allows students to gain coding skills and initialunderstanding of simulation software capabilities while learning engineering fundamentals.Similar design guidelines were followed as for the Fluids resource, with explanatory images,code cells hidden by default, and plots displayed within the Jupyter Notebook (Figures 3 & 4). (a) (b)Figure 3: S11 parameter of the Figure 4: Gain of the dipole Figure 5: (a

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

Tagged Divisions

Computers in Education Division (COED)

-technical users, to ML concepts such as classification, pre-processing,data reduction, visualization, explain-ability, etc [15]. Aliro is an open-source software package designed to au-tomate ML analysis through a web interface [8]. It has a built-in ML recommender system that guides usersthrough, (a) Choosing the right machine learning technique for a particular problem or dataset, and (b) Con-figuring hyperparameters to optimize the chosen algorithm’s performance. It is especially suited for researcherswithout computer science/data science training. By automating much of the complexity in data analysis, Aliroaccelerates research and help users focus on drawing insights from data rather than getting stuck in the weeds ofML model configuration

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)

identify neurodivergent-specific practices for individuals.Thus, we anticipate neurodivergent students with instructional supports designed to fit theirlearning needs will help them master the student role, contribute to the United Statescomputational workforce, and participate in Computer Science and STEM career pathways aftergraduation.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No.(NSF 2137725). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation.References[1] A. Vaccaro, M. Daly-Cano, and B. M. Newman, “A Sense of Belonging Among CollegeStudents

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)

. Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285 (2022). 13. Schuler, B. Applicable applications: Treatment and technology with practical, efficient and affordable solutions. Auditory Processing Disorders: Assessment, Management and Treatment, 411-416 (2019). 14. Chen, C. J., Lee, I. J., & Lin, L. Y. Augmented reality-based self facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. Research in Developmental Disabilities, 36, 396-403 (2015). 15. Mayer, R. E. Multimedia learning (2nd ed.). Cambridge University Press (2009). 16. Wang, F., Kinzie, M. B., McGuire, P., & Pan, E

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)

&lr=&id=GkaTDAAAQBAJ&oi=fnd&pg=PP1&dq=Qualitati ve+Research+in+STEM:+Studies+of+Equity,+Access,+and+Innovation.&ots=WBqDJY5uim&sig=_ 772GNzIiWHfP7IzvI1SPbQH6Pk[10] S. W. Tabsh, H. A. El Kadi, and A. S. Abdelfatah, “Faculty perception of engineering student cheating and effective measures to curb it,” in 2019 IEEE Global Engineering Education Conference (EDUCON), IEEE, 2019, pp. 806–810. Accessed: Jan. 09, 2025. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8725199/?casa_token=iN2QmiVVNVMAAAAA:X1G RG9aX8BkH2Jg3d56YHarCGv8k_9IlMwNVOO545dyQqfklqb5MsKyRxPLQJB3CSEcoW-HNSw[11] E. F. Gehringer and B. W. Peddycord, “Teaching strategies when students have access to solution

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)

the physical safety of a person or others around them, whichincludes an option for if the user is unsure. Finally, the list of resources is categorized by student,faculty, and emergency resources. The user can navigate this information by using tabs at the topof the page, which includes a tab that lists all resources. Some of the app functionality can beseen in the figures below. (a) Warning Signs (b) Emergency ResourcesFigure 1: App Functionality to Identify Warning Signs and Direct User to Appropriate Resources Figure 2: Resources SectionWhile the existing features of ChargerCare represent significant progress in solving the issuesidentified among the university

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

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Computers in Education Division (COED)

the code for HW4.5” (EPS) df (x) • solve dx = −a · f (x) + b (NEF) 2. Assignment Copy-Paste Queries: Students often pasted entire assignment questions or instructions directly into the AI-bot, seeking solutions. Due to the length and specificity of these queries, as well as their nature, it is not feasible to provide detailed examples here. However, this behavior was observed frequently across multiple courses.5 DiscussionIn our research, we investigated the use of a Generative AI for educational support, our researchincludes four primary questions. For RQ1, we explored which type of category students usuallylook for help. In comparison with programming

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)

Appendix B for the complete transcript). Despite the benefits of AI-generated prompts, some issues persist, notably withrepetitious phrases. As seen in Summary of Reflection 3, repetitive use of similar languagereduced student involvement and the depth of responses. In Question 2, the word “equalcontribution” produced a detailed and thoughtful response (R2) with specific examples.However, the AI used the same phrase in future questions (their remarks in Q4: “This answer isshort because I have typed a very similar response for the past 3 question Q3-Q5), phrasing it toquestions associated with collaboration, decision-making, consensus-building, andproblem-solving. This lack of variance resulted in repeated responses, restricting the

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

Tagged Divisions

Computers in Education Division (COED)

., Moro, A., Bergram, K., Purohit, A., Gillet, D., & Holzer, A. (2020). Bringing Computational Thinking to non-STEM Undergraduates through an Integrated Notebook Application. https://ceur-ws.org/Vol-2676/paper2.pdfFunk, C. (2018, January 9). Women and Men in STEM Often at Odds Over Workplace Equity. Pew Research Center. https://www.pewresearch.org/social-trends/2018/01/09/women-and-men-in-stem-often-at -odds-over-workplace-equity/Jackson, C., Mohr-Schroeder, M. J., Bush, S. B., Maiorca, C., Roberts, T., Yost, C., & Fowler, A. (2021). Equity-Oriented Conceptual Framework for K-12 STEM literacy. International Journal of STEM

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

Tagged Divisions

Computers in Education Division (COED)

) gates, whichare shown in Figure 1a. These simulations are essential for understanding the foundationaloperations of QC, as they form a universal gate set. Figure 1b shows the user interface of the toolwhich allows users to manipulate parameters such as magnetic field values, dephasing times, andinitial quantum states, enabling a comprehensive exploration of quantum phenomena. Thesimulations are based on the Lindblad Master Equation (LME) [41], which accounts for thedecoherence effects that occur in quantum systems. (a) (b)Figure 1: Spin qubit array and device parameter configuration interface. Note: (a) The spin qubitarray consists of a single-qubit rotational gate and a two