Conference Session

Engineering Ethics Division (ETHICS) Technical Session - Ethics education methodologies and interventions

Collection

2025 ASEE Annual Conference & Exposition

Authors

Robyn Mae Paul, University of Calgary

Tagged Topics

Diversity

Tagged Divisions

Engineering Ethics Division (ETHICS)

ethics in engineering education. Science and Engineering Ethics, 10(2), 343–351. https://doi.org/10.1007/s11948-004-0030-8Paul, R. M., Hugo, R., & Falls, L. C. (2015). International expectations of engineering graduate attributes. 11th International CDIO Conference.Piers, C. (2024, February 7). Even ChatGPT Says ChatGPT Is Racially Biased. Scientific American. https://www.scientificamerican.com/article/even-chatgpt-says-chatgpt-is- racially-biased/Riley, D. (2012). Aiding and ABETing: The Bankruptcy of Outcomes-based Education as a Change Strategy. 2012 ASEE Annual Conference & Exposition Proceedings, 25.141.1- 25.141.13. https://doi.org/10.18260/1-2--20901Ross, S. R. (2019). Supporting your

Conference Session

Computing and Information Technology Division (CIT) Technical Session 4

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jason M. Keith, Iowa State University of Science and Technology; Amin Amirlatifi, Mississippi State University; Sudip Mittal, Mississippi State University; Subash Neupane, Mississippi State University; HIMANSHU TRIPATHI, Mississippi State University

Tagged Topics

Diversity

Tagged Divisions

Computing and Information Technology Division (CIT)

Education, 2025 Barkplug 2.0 and Beyond: a Chatbot for Assisting Students in High DFW CoursesAbstractHigher education continues to respond to the challenges and opportunities presented by artificialintelligence (AI) and large language models (LLM) such as ChatGPT. In our prior work weintroduced a chatbot that used AI and LLM to recruit prospective students, assist current studentswith academic advising (course selection, changing majors) and student affairs (directingstudents to university resources regarding the campus community, housing and dining, studentorganizations, mental health and more). Towards the promotion of student success initiatives wereport in this work our formulation of course specific teaching

Conference Session

WIP Poster Session: Emerging Research and Practices in Pre-College Engineering Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Syeda Fizza Ali, Texas A&M University; Ayaan Sunil Rege, The Hill School; Susanna Angela Ponniah, The Hill School; Hoda Ehsan, The Hill School ; Saira Anwar, Texas A&M University

Tagged Topics

Diversity

Tagged Divisions

Pre-College Engineering Education Division (PCEE)

developing systemscapable of performing tasks that usually require human intelligence, including learning,reasoning, and decision-making [21]. Generative AI (Gen-AI) is a subset of AI thatspecializes in creating human-like content, including text, images, and audio [22]. With AI'srecent innovations, many have explored its educational applications. Many educatorscurrently utilize AI tools to increase efficiency within the classroom [1]. Two examples ofGen AI tools include 1) ChatGPT, a generative AI chatbot, and 2) Grammarly, an AI-powered writing assistant. Both tools have proven valuable educational assistants [2, 3].GenAI can help educators with tasks like creating assessments and streamliningadministrative tasks and lessons [23, 24]. In the field

Conference Session

Industry and Technical Engineering Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Aakash Alpesh Patel, University of Georgia; Taiwo Raphael Feyijimi, University of Georgia; Sarah Jane Bork, University of Georgia

Tagged Divisions

Student Division (STDT)

Excel file. The retrieved transcripts were thenprocessed to convert them into text from transcript form. This involved the removal of timestamps and correction of word spacing. Stage 3: Transcript Evaluation: For this study, we built off ongoing work by members ofthe research team to adapt a framework to perform deductive thematic analyses [redacted; underreview]. This method leverages a combination of prompt engineering techniques (PETs), naturallanguage processing via large language models (NPL via LLMs; i.e., ChatGPT), and Bradley etal.’s framework on thematic analysis. Appendix B details the exact prompts used to extractrelevant themes and ideas from the transcripts. Bradley et al.’s study outlined a method whereseveral codes should

Collection

2025 ASEE -GSW Annual Conference

Authors

Md Nazmus Sakib, University of North Texas; Maurizio Manzo, University of North Texas; Rattaya Chowdhury Yalamanchili, University of North Texas

aid and others seeing it as a risk to independent critical thinking. This study also exploresstudents’ perspectives on integrating AI into future curricula and highlights their suggestions for itsresponsible and effective adoption in engineering education. IntroductionThe rapid advancements in artificial intelligence (AI) are reshaping the education sector. Engineeringeducation has long been at the forefront of adopting technological innovations, reflecting the field'sdynamic and solution-driven nature. AI tools such as ChatGPT, Copilot, Grammarly, Claude,Gemini, Wolfram Alpha are becoming indispensable to enhance learning experiences1,2. Fromautomated routine reminders to facilitating deeper

Collection

2025 Northeast Section Conference

Authors

Mohamed Elwakil; Tooran Emami Ph. D.

was assessed through pre/post-Likert-scaleemphasize the importance of strategically designed prompts for surveys (Q2, Q3) and skills assessments.AI tools like ChatGPT, which can foster engagement, critical 2. RQ2 What role does disciplinary context play in shapingthinking, and personalized instruction. The study outlines career relevance and proper perceptions?. This waseffective strategies such as assigning roles to AI, defining clear explored via Likert-scale ratings (Q5) and qualitativeobjectives, and employing iterative dialogue to refine outputs. themes from open-ended responses.Similarly, the authors of [2] explore integrating structuredprompt engineering with generative AI tools. This

Collection

2025 Northeast Section Conference

Authors

B. Kris Jaeger-Helton

the two lists. “Going to ChatGPT helped us create an outline from our B. Positive Aspects of AI use notes to organize the presentation.” Table I presents the profile of responses from Question 1 “Once we searched on kansei engineering + human factors we saw it was a thing and how they combined together.”that explicitly listed perceived advantages of AI. “It was so easy to ask AI to format our references, it saved TABLE I. OVERVIEW OF POSITIVE RESPONES FOR USE OF AI time that was better spent

Conference Session

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

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

Tagged Divisions

Computers in Education Division (COED)

, Use of AI tools and Peer Collaboration on AI Assisted Learning: Perceptions of the University students.,” Digit. Educ. Rev., no. 45, pp. 43–49, Jun. 2024, doi: 10.1344/der.2024.45.43-49.[5] M. Edali, A. Milad, H. Saad, Z. Sahem, T. Alajaili, and A. Elkamel, “ChatGPT and Artificial Intelligence (AI) Massive Transformation of Trainers’ Education Sector Revolutionizing How Students Learn,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, Dubai, UAE: IEOM Society International, Feb. 2024. doi: 10.46254/AN14.20240340.[6] C. Spreitzer, O. Straser, S. Zehetmeier, and K. Maaß, Mathematical Modelling Abilities of Artificial Intelligence Tools: The Case of ChatGPT., vol. 14

Conference Session

ELOS Technical Session 3: Advancing Engineering Competencies: From Labs to Writing

Collection

2025 ASEE Annual Conference & Exposition

Authors

Dave Kim, Washington State University-Vancouver; Charles Riley P.E., Oregon Institute of Technology; Sean St. Clair P.E., Oregon Institute of Technology; Olusola Adesope, Washington State University

Tagged Topics

Diversity

Tagged Divisions

Experimentation and Laboratory-Oriented Studies Division (DELOS)

community of practice focusing on engineering lab writing education. Thispaper presents the content, delivery, and results of the professional development workshop onengineering lab writing.2. Workshop Content and DeliveryThe workshop was designed for the participants to conduct the following in a small groupsetting: 1) develop engineering lab report assignments; 2) improve engineering lab reportassessment; 3) guide students in navigating writing with generative AI (ChatGPT-4); and 4) trainlab teaching assistants or lab report graders. Participants accessed the guides (available atengineeringlabwriting.org) to design and develop sample labs, discuss issues related to labwriting and how to deliver lab writing expectations, and provide feedback to

Conference Session

ERM Technical Session: Developing Engineering Competencies I

Collection

2025 ASEE Annual Conference & Exposition

Authors

Olivia Ryan, Virginia Polytechnic Institute and State University; Katherine Drinkwater, Virginia Polytechnic Institute and State University; Susan Sajadi, Virginia Polytechnic Institute and State University; Mark Vincent Huerta, Virginia Polytechnic Institute and State University

Tagged Divisions

Educational Research and Methods Division (ERM)

it is not taken personally. I think everyone made kind and helpful‬ ‭comments that reassures me and I appreciate that I am able to know what they think of‬ ‭me so I know how to improve.‬ ‭ he feedback I received was helpful and not as intimidating to receive so I think next‬ T ‭time I won't be as scared to share how I feel about my teammates in terms of the work‬ ‭they put in because I like that ChatGPT is another layer and it is ultimately helpful to get‬ ‭this feedback.‬‭ tudents also stated that they felt like they could provide negative feedback instead of focusing‬S‭on just positive feedback, “I enjoyed the AI-generated feedback reports because they allowed‬ ‭team members to be

Conference Session

Computing and Information Technology Division (CIT) Technical Session 7

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ismael Villegas Molina, University of California, San Diego; Audria Nikitza Montalvo, University of California, San Diego; Benjamin Ochoa, University of California, San Diego; Paul Denny, University of Auckland; Leonard Porter, University of California, San Diego

Tagged Topics

Diversity

Tagged Divisions

Computing and Information Technology Division (CIT)

Corrected Question P-Value Mean Mean P-Value I use CodeHelp because the professor told us we could use it in the class. 3.80 3.72 0.6615 1.0000 I prefer CodeHelp to ChatGPT because it does not give me the answer directly. 3.56 3.79 0.2056 0.9937 I believe that CodeHelp gives me just enough information to continue my work without

Conference Session

Computers in Education Division (COED) Track 4.E

Collection

2025 ASEE Annual Conference & Exposition

Authors

Michael Thomas Vaccaro Jr, University of Connecticut; Mikayla Friday, University of Connecticut; Zeynep Gonca Akdemir-Beveridge, University of Connecticut; Arash Esmaili Zaghi P.E., University of Connecticut

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

personalizationdevelop custom graphical user interfaces (GUI)—such as that developed in the follow-up studyby Vaccaro et al. [22]—rather than rely on public-facing interfaces like ChatGPT as it minimizesthe potential for user error. Such a controlled GUI is also beneficial from an experimental contextwhere consistency in implementation is of critical importance. Finally, it should be noted thatsuch an environment allows for strict control over the types of information students can sharewith an LLM, thus maintaining student privacy.Integration of Personalized Learning in Engineering Education through LLMsThe integration of PL into engineering education through advanced AI and LLMs represents atransformative yet nascent field. The use of cutting-edge LLMs, such

Conference Session

Computing and Information Technology Division (CIT) Technical Session 3

Collection

2025 ASEE Annual Conference & Exposition

Authors

Roberto Patricio Carú, Universidad Andres Bello; Juan Felipe Calderón, Universidad Andres Bello, Viña del Mar, Chile

Tagged Topics

Diversity

Tagged Divisions

Computing and Information Technology Division (CIT)

parameters.Appendix 4 details the performance of the Gemini, ChatGPT, and Perplexity AI tools in thesetasks, providing practical examples of their capabilities. Through a mixed-methodology approachthat includes a literature review, case studies, and practical experimentation, this researchexplores how AI can optimize these areas and develops a theoretical and practical frameworkthat guides its effective and ethical implementation.Research ObjectivesThe primary purpose of this study is to explore and assess the impact of Artificial Intelligence(AI) on the management and operation of Information Systems (IS) within educational andbusiness environments. Specifically, the research aims to:1. Evaluate how AI can improve operational efficiency in information

Conference Session

ME Division Technical Session 2 - Harnessing AI and Machine Learning to Transform ME Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Wenhai Li, State University of New York, College of Technology at Farmingdale; Yue Hung, Farmingdale State College; Gonca Altuger-Genc, State University of New York, College of Technology at Farmingdale; Sen Zhang, State University of New York, Oneonta; Akin Tatoglu, University of Hartford; Zhou Zhang, State University of New York, College of Technology at Farmingdale

Tagged Topics

Diversity

Tagged Divisions

Mechanical Engineering Division (MECH)

. The session then transitions to the transformative role of Transformers in NLP, focusing on their improvements over RNNs without delving into advanced mathematical details. This leads to the discussion of Large Language Models (LLMs), such as ChatGPT [8], emphasizing their engineering applications. Students learn to use ChatGPT’s API to integrate NLP into workflows, with a Python example showing how to send prompts, receive responses, and maintain conversational context by including prior interactions. These hands-on examples help MET students understand the practical applications of NLP tools like LLMs in solving engineering problems. • Topic 8: Reinforcement Learning (RL) – The last topic

Conference Session

Engineering Ethics Division (ETHICS) Technical Session - GenAI in ethics education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Bernd Steffensen, University of Applied Sciences Darmstadt/European University of Technology; Bettina von Römer, University of Applied Sciences Darmstadt

Tagged Divisions

Engineering Ethics Division (ETHICS)

: 1. the statement of the lawyer in printed form. This also con- tains the correct information about the sources that were en- tered into the chatbot to gener- ate the statement with ChatGPT. When creating the printed statement, the rules of academic

Conference Session

ENT-3: Transforming Engineering Curriculum through Entrepreneurial Approaches

Collection

2025 ASEE Annual Conference & Exposition

Authors

Joshua Gargac, Ohio Northern University; Shuvra Das, University of Detroit Mercy; Kurt M Degoede, Elizabethtown College; Sara A. Atwood, Elizabethtown College

Tagged Topics

Diversity

Tagged Divisions

Entrepreneurship & Engineering Innovation Division (ENT)

, showcasing an enhanced ability to analyze and learn from failure. Table 4: Summary of ChatGPT comparison of pre-course and post-course responses to “How would you define a healthy mindset toward failure?” Pre- Post- Change Example Pre-Course Theme Course Course Example Post-Course Response (%) Response (%) (%) "By viewing it as a steppingstone to fully Focus on learning and understanding the content

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)

from AI – and discovered a bimodal distribution. Thus, weshow that the student body at Mines is polarized with respect to future impacts of GenAI on theengineering workforce and society, despite being increasingly willing to explore GenAI overtime. We discuss implications of these findings for future research and for integrating GenAI inengineering education.IntroductionRecent advancements in Generative Artificial Intelligence (GenAI), esp. large language models(LLMs) like ChatGPT, have significantly impacted both industry and educational sectors [1, 2].These models, equipped with sophisticated algorithms and trained on vast datasets, canunderstand and generate human-like text [3], expanding their use from simple text prediction tocomposing

Collection

2025 Northeast Section Conference

Authors

Lina H. Kloub, University of Connecticut

NTRODUCTION ucators navigating the evolving landscape of AI in education. The integration of artificial intelligence (AI) tools in educa- II. M ETHODOLOGYtion has sparked both enthusiasm and concern among educa-tors and students alike. These tools, ranging from generative A. Course Context and Participants.AI systems like ChatGPT to specialized applications, have the This study was conducted in an undergraduate course titledpotential to reshape how students approach problem-solving, Algorithms and Complexity at the University of Connecticutcollaborate on projects, and prepare for their

Conference Session

First-Year Programs Division (FPD) Work-in-Progress 1: Curriculum Design and Innovative Pedagogy

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jeffrey Stransky, Rochester Institute of Technology; Ashish Agrawal, Rochester Institute of Technology; Mike Eastman Ph. D., Rochester Institute of Technology (CET)

Tagged Divisions

First-Year Programs Division (FPD)

problem generation has beenstudied since the mid-1960s [26], the accessibility and sophistication of modern AI models havesignificantly enhanced the personalization, generation speed, and robustness of these problems.Recent efforts, such as the use of OpenAI’s ChatGPT to generate problems in real-time withinclassroom settings, have demonstrated the potential of these tools to adapt dynamically tolearners’ needs [27]. This approach is gaining traction, particularly in K–12 education, wherepersonalized arithmetic problems are being used to establish meaningful context for students[28], [29]. While these tools have been emerging, a formal tool designed for engineeringeducation and the challenges first-year students face in calculus has yet to be

Conference Session

Engineering Technology Division (ETD) Technical Session 5

Collection

2025 ASEE Annual Conference & Exposition

Authors

Stephen Andrew Wilkerson P.E., York College of Pennsylvania; Yargo Teixeira Gomes de Melo, York College of Pennsylvania; Tamara Schwartz, York College of Pennsylvania; Dean Zeller, York College of Pennsylvania

Tagged Divisions

Engineering Technology Division (ETD)

. James XXXX, Dean XXXX, Devon XXXX, and SierraXXXX are introduced below.Dean XXXXDean XXXX is a college professor with over 25 years of teaching experience in computerscience and mathematics. Dean was given his first Artificial Intelligence course back in 2002,with students using the Lisp programming language to implement a Minimax lookahead strategyfor the classic board game Othello. Since then, he has taught classes, conducted personalresearch, and written stories involving AI. Since the boom of ChatGPT and other large languagemodels, Dean has focused his attention on the ethics of AI and its potential ramifications onsociety. This semester, Dean is teaching a 400-level computer science course and a 100-levelfirst-year seminar focusing on the

Conference Session

Computers in Education Division (COED) Track 2.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Peter Jamieson, Miami University; Suman Bhunia, Miami University; George D. Ricco, Miami University; Brian A Swanson, Miami University; Bryan Van Scoy, Miami University

Tagged Divisions

Computers in Education Division (COED)

most people in the first world to access (at reasonable cost) fabrication technology and computation at the scale of an individual — this trend, pushed by artists [5] and engineers, makes the public and our students not only aware but experienced in building things using these tools. However, the assumption that a student, because they lived before or during a technology emergence, is strongly skilled with that technology is false [6].The biggest of these trends that we address in this work is trend 4, commonly referred to in thegeneral population as chatbots such as ChatGPT — the continued emergence of AI capabilities —specifically, the emergence of Large Language Models (LLMs) [7] means our curriculums need tobe

Conference Session

Professional Papers

Collection

2025 ASEE Southeast Conference

Authors

Bryn Elizabeth Seabrook, University of Virginia

Tagged Topics

Professional Papers

learning in higher education.The rapid advancement of these technologies presents both opportunities and challenges foreducators, raising critical questions about the integration of AI into undergraduate classrooms.When systems such as ChatGPT were first introduced, many scholars, such as Noam Chomsky,demonstrated a visceral negative reaction to AI generated text. [1] Generative AI tools were, andlargely still are, seen as a threat to the creative process—ultimately something that academicsshould reject. While these sentiments are perfectly valid, there is a growing body of researchevaluating AI’s benefits. What if there was a way to harness this technology to improve studentengagement and outcomes? Can generative AI personalize learning, automate

Conference Session

NSF Grantees Poster Session II

Collection

2025 ASEE Annual Conference & Exposition

Authors

David Lattanzi, George Mason University

Tagged Topics

NSF Grantees Poster Session

–74. doi: 10.1007/978-1-4842-2256-0_3.[6] “Presentations.AI - ChatGPT for Presentations.” Accessed: Jan. 15, 2025. [Online]. Available: https://www.presentations.ai/

Conference Session

Software Engineering Division (SWED) Technical Session 4

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kevin A Gary, Arizona State University; Cecilia La Place, Arizona State University, Polytechnic Campus; Vidya Rupak; Rakshilkumar Modi; Karthik Vaida

Tagged Divisions

Software Engineering Division (SWED)

structure or by tweaking HTML and CSS. This activity tied in data structures, how the web works (client/server and networking), and basic AI.Figure 2. Eliza snapshotThe afternoon session introduced LLMs using ChatGPT. A two-server setup with provided codeenabled access to the OpenAI’s API, with one server a modified version of the Eliza server fromthe morning, and the other a custom proxy server to enable access to the API with revealing APIkeys. The proxy server also pruned prompts to ChatGPT to ensure responses matched the formatof non-ChatGPT version of the Eliza chatbot. Campers concluded the day by conducting theTuring test for themselves in their groups at each table.Day 3 featured game

Conference Session

Effective Teaching Potpourri

Collection

2025 ASEE Annual Conference & Exposition

Authors

Sotiria Koloutsou-Vakakis, University of Illinois at Urbana - Champaign; Megan L. Matthews, University of Illinois Urbana - Champaign; Jacob Henschen, University of Illinois Urbana-Champaign; John S Popovics P.E., University of Illinois at Urbana - Champaign; Ashlynn S. Stillwell, University of Illinois Urbana-Champaign; Cheryl Ann Cohen

Tagged Divisions

Civil Engineering Division (CIVIL)

of a departmental initiative to incorporatecomputation and computational thinking into the curriculum by integrating computational toolswith course fundamentals. This effort commenced just before the rapid emergence of ChatGPT[5] in late 2022. Since we only have anecdotal evidence about AI’s impact, we defer discussingthis topic to a future study. The insights here are based on surveys designed to collect baselineinformation about student attitudes toward computational tools in their courses, and to explorewhether these have changed over time in select courses, considering both lower level to higherlevel courses.2. BackgroundThe general framework for our effort to integrate computation and computational thinking isgrounded in our department

Conference Session

Materials Division (MATS) Technical Session 4

Collection

2025 ASEE Annual Conference & Exposition

Authors

Joel L Galos, California Polytechnic State University, San Luis Obispo; Mohsen Beyramali Kivy, California Polytechnic State University, San Luis Obispo; Lessa Grunenfelder, University of Southern California; Ken-ichi Nomura, University of Southern California; James E. Saal, Citrine Informatics, Inc.

Tagged Divisions

Materials Division (MATS)

-Trained Transformer (ChatGPT) in the classroom. Evidencesuggests student use of ChatGPT can enhance academic performance, boost affective-motivational states, improve higher-order thinking propensities and reduce mental effort [3].This evolving AI landscape encourages those in higher education to reassess goals, teachingmethods, and assessment strategies. The impact of AI tools is far-reaching and has alreadycaused educators to rethink Bloom’s taxonomy (Table 1) to distinguish between distinctivehuman skills in the learning process and the role of generative AI (Gen AI) tools such asChatGPT in the learning process. Table 1: Bloom’s Taxonomy comparison of human skills in learning and generative AI skills in learning. Adapted from [4

Conference Session

Computers in Education Division (COED) Track 2.D

Collection

2025 ASEE Annual Conference & Exposition

Authors

Edward F. Gehringer, North Carolina State University at Raleigh; Madhur Dixit, North Carolina State University at Raleigh; Kavya Lalbahadur Joshi

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

of the dual-submission strategies, there is more variety to what is submitted for thesecond deadline. Sometimes students are asked to self-grade their homework [2–4]; usually theyare asked to make corrections, and some strategies ask them to undertake other activities, such asa quiz [5], group discussion [6–7], filling in missing steps in a derivation [8], or filling out a“homework wrapper” [9–10] that asks about the strategies that students used in doing thehomework and how successful they proved to be.However, the rise of Large Language Models (LLMs) like ChatGPT presents a challenge. Thesemodels can solve simple homework problems, but can they also produce credible reflections? IfLLMs can generate authentic-looking reflections, the dual

Conference Session

DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Faiza Zafar, Rice University; Carolyn Nichol, Rice University; Matthew Cushing, Rice University

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

services to boost productivity and streamline tasks. Google Scholar,for instance, provides a free database that helps students find scholarly articles, research papers,and other academic resources for their projects [15]. Notion serves as an all-in-one productivityplatform, combining note-taking, project management, and collaboration features, making itespecially useful for group work and managing busy schedules [15]. Grammarly, an AI-poweredwriting assistant, helps students refine their writing by checking for grammar, spelling,punctuation, and style while also offering suggestions for improving clarity and organization[14]. ChatGPT stands out as a powerful tool for homework assistance, test preparation,language learning, and other

Collection

2025 ASEE -GSW Annual Conference

Authors

Colby Edward Kurtz, Houston Christian University

Tagged Topics

Diversity

AbstractThis paper demonstrates the design and implementation of an innovative gamified softwareapplication for learning human-spoken languages. The game serves as an interactive and enjoyablesupplement to aid the learning process of different languages for elementary-aged children. At its core,the application uses a translation Application Programming Interface (API) to process text and outputtranslations in the target language chosen by the learner. Additionally, it is AI-enabled, allowing theutilization of APIs such as OpenAIs’s ChatGPT to enhance the translation capabilities. Provided is abasic proof of concept that was developed as part of the Final Pi Project in the Intermediate ComputerProgramming (COSC 1352) course. The gamified program was

Collection

2025 ASEE -GSW Annual Conference

Authors

Lance Leon Allen White, Texas A&M University; Karan Watson P.E., Texas A&M University

. With the rise of tools like ChatGPT, faculty noticed a perceptible increase inunethical practices resulting in academic dishonesty proliferation throughout the first-yearengineering student population. This necessitated a re-evaluation of assessment methodologies.The first-year engineering cohort of faculty transitioned from the digital to paper format duringSummer 2024 in a small scale, resulting in no academically dishonest behaviors in that smallpopulation. This success positioned the faculty to employ this method of assessment into thestandard procedures of the academic unit.It is important to state the context in which this course of interest is situated. There are a total ofthree courses required by the college of engineering at TAMU