Conference Session

First-Year Programs Division (FPD) Technical Session 13: Equity in Action - Identity, Mentorship, and Inclusion

Collection

2025 ASEE Annual Conference & Exposition

Authors

Varun K Kasaraneni, Gannon University; Lin Zhao, Gannon University; Longfei Zhou, Gannon University; Xiaoxu Ji, Gannon University

Tagged Divisions

First-Year Programs Division (FPD)

assignments. Thispaper presents the results of the surveys, analyzing students' initial perceptions, expectations, andexperiences with AI tools, as well as the strategies they employed to enhance their interactionswith ChatGPT during a structured assignment 2. MethodsThis study utilized a survey-based approach to examine the understanding, expectations, andperceived applications of artificial intelligence (AI) among first-year engineering and computingstudents. The survey was designed to capture the students’ initial perceptions prior to any formallecture on AI tools and their perspectives after engaging with the lecture content.The study included participants enrolled in an "Introduction to Engineering and Computing"course at xxxxxx University

Conference Session

First-Year Programs Division (FPD) Technical Session 11: Shaping Engineers - Competency, Creativity, and Iteration in the First Year

Collection

2025 ASEE Annual Conference & Exposition

Authors

Mehek Kunal Vora, Tufts University; Ethan E Danahy, Tufts University

Tagged Divisions

First-Year Programs Division (FPD)

studies representative of student experiences from eachcategory that expands on the model and its implications in higher education learningenvironments. The findings emphasize that learning is not a static process; students’ interactionswith AI tools evolve over time, influenced by their initial attitudes and skills. The implications ofthis paper extend to curriculum design, pedagogical approaches, and the broader integration ofgenerative AI tools in higher education.IntroductionThe rapid advancement of generative artificial intelligence has revolutionized various industries,including education. As generative AI tools such as ChatGPT, Claude, and Gemini becomeincreasingly accessible, educators are exploring their potential to transform teaching

Conference Session

First-Year Programs Division (FPD) Work-in-Progress 3: Integration of Math, Computing, and AI in First-Year Courses

Collection

2025 ASEE Annual Conference & Exposition

Authors

Bishweshwor Rimal, University of Texas at Austin; Sneha Ballabh, University of Texas at Austin; Nina Kamath Telang, University of Texas at Austin

Tagged Divisions

First-Year Programs Division (FPD)

articlesummarizing the impact of ChatGPT on a variety of engineering assessments, the authorsconcluded that introductory-level programming assessments can be very accurately solved byChatGPT, and that instructors must add complex features to their assessments to deter studentcheating [7].However, there is no published research on the usage of generative AI to offer customizedquestion banks and explanations of course concepts based only on course materials, for anintroductory computing course, within the learning management system, thereby providing apersonalized learning experience tailored to each student’s needs. The primary innovation in ourstudy lies in ensuring that the generated questions remain entirely focused on the course content,strictly avoiding

Conference Session

First-Year Programs Division (FPD) GIFTS Session 1: Human-Centered and Project-Based Innovation in First-Year Engineering Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ethan E Danahy, Tufts University

Tagged Divisions

First-Year Programs Division (FPD)

Mahabharata) ●​ Chinua Achebe (Nigeria) ●​ Sun Wukong (Monkey King, China, from "Journey to ●​ Jane Austen (England) the West") ●​ Hans Christian Andersen (Denmark) ●​ Aladdin (Middle Eastern, from "One Thousand and ●​ Khalil Gibran (Lebanon) One Nights") ●​ William Shakespeare (England) ●​ Elizabeth Bennet (England, by Jane Austen) ●​ Anna Karenina (Russia, created by Leo Tolstoy)Generative AI ToolsText Generation: Microsoft Copilot, Open AI’s ChatGPT, Google’s Gemini, Anthropic’s Claude,Perplexity AIImage Generation: Microsoft Copilot, Open AI’s ChatGPT, Canva, PIXLR, OpenArt.aiNote: rapidly changing innovation in the generative AI

Conference Session

First-Year Programs Division (FPD) GIFTS Session 1: Human-Centered and Project-Based Innovation in First-Year Engineering Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Michael Shteynberg, Northeastern University; Andrew L Gillen, Northeastern University; Kaylla Cantilina, Northeastern University

Tagged Divisions

First-Year Programs Division (FPD)

sub-branch of artificial intelligence that uses machinelearning. It allows machines to understand, analyze, and generate responses that are easy forhumans to understand. NLP already facilitates the interactions between our students and all sortsof artificial intelligence like chatbots (ChatGPT), smart assistants (Siri), and more. Calls formore integration of artificial intelligence into education grow louder by the day. For instance, aspecial committee was established in the US to make recommendations, including around AI ineducation [1]. Outside of academia, regular interaction with AI tools is becoming commonplacein industry. Scholars have already outlined a plethora of opportunities and concerns aroundapplying this technology in the

Conference Session

First-Year Programs Division (FPD) Technical Session 4: Fostering Belonging - Identity, Self-Efficacy, and Retention

Collection

2025 ASEE Annual Conference & Exposition

Authors

Brian Patrick O'Connell, Northeastern University; Kathryn Schulte Grahame, Northeastern University; Richard Whalen, Northeastern University; Constantine Mukasa, Northeastern University; Susan F Freeman, Northeastern University

Tagged Topics

Diversity

Tagged Divisions

First-Year Programs Division (FPD)

using ChatGPT for high-level analysis. Datasets weremanually formatted to ensure consistent wrangling by the AI, using standardized key phrases andstructured formatting to enhance the AI's ability to parse and interpret the information accurately. Thisstudy implemented a simplistic segmentation, considering each sentence as a single statement, toimprove reliability and repeatability. This process was systematically repeated and refined by utilizingsubsets of the data with established qualities until preprocessing consistently achieved accurate parsingfollowing emerging best practices [23].Throughout the analysis, refinements were made to prompts and categorizations, ensuring alignmentwith the nuances of each reflection. Reanalysis occurred in

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

First-Year Programs Division (FPD) Technical Session 12: Bridging the Gap - Strategies to Support Diverse Learners in Early Engineering Courses

Collection

2025 ASEE Annual Conference & Exposition

Authors

Brainerd Prince, Plaksha University; Sohan Panda, Plaksha University; Shubham Goel, Plaksha University; Tanmay Ravi Chowdhary, Plaksha University

Tagged Divisions

First-Year Programs Division (FPD)

Metric Analysis of ‘The Future of Thinking Analysis of PBL Video (Part Two) Manifesto’ (Part One) AI Utilization The average AI-generated content of all AI tools were used to a lesser extent 15 students is about 80-85% generated for elaborating ideas and content using AI tools such as ChatGPT, primarily creation, complemented by for structuring, content creation and paraphrasing and incorporating research. The team relied heavily on class research papers. Around 30% of the content was AI-generated and later notes and ideas taught in class which were rephrased. As

Conference Session

First-Year Programs Division (FPD) Technical Session 6: Learning by Doing - Contextual and Community-Based Engineering

Collection

2025 ASEE Annual Conference & Exposition

Authors

David Gray, Virginia Polytechnic Institute and State University; Juan David Ortega Álvarez, Virginia Polytechnic Institute and State University

Tagged Divisions

First-Year Programs Division (FPD)

see several opportunities to refine the assignment based on the lessonslearned. Currently, the scenarios were developed by a single faculty member in the EngineeringEducation department through the use of generative AI (ChatGPT Model 4.0). To enhancedisciplinary representation, we will collaborate with colleagues from degree-granting majors todevelop scenarios that better highlight underrepresented fields, such as biological systems,mining, and materials science. Faculty from these disciplines are well-positioned to identifyemerging challenges and opportunities that reflect the nuances of their fields while remainingrelevant to first-year students.Additionally, we plan to guide students more explicitly toward resources that clarify both

Conference Session

First-Year Programs Division (FPD) Work-in-Progress 5: Academic Support, Retention, and Success Strategies

Collection

2025 ASEE Annual Conference & Exposition

Authors

Hiba Assi, University of Detroit Mercy; E. Prasad Venugopal, University of Detroit Mercy; Shuvra Das, University of Detroit Mercy; Dawn Archey, University of Detroit Mercy; Mark Andrew Steffka, University of Detroit Mercy; Darrell K. Kleinke P.E., University of Detroit Mercy

Tagged Topics

Diversity

Tagged Divisions

First-Year Programs Division (FPD)

and biases that seepinto the design of products and their effect on different populations and society at large.Increasing the representation of historically marginalized populations in the engineering pipelineand into the workforce is crucial in creating a more equitable future for all people.VI: AcknowledgementsThis project is being supported through an internal grant from the university president’s office tofoster innovation. ChatGPT was used for editing earlier drafts of this paper. Also, we wish toacknowledge several colleagues Drs. Kirstie Plantenberg, Michael Santora and Kenneth Lamb ofUniversity of Detroit Mercy, who contributed in various ways to the project discussed here.References[1] “Transforming Undergraduate Engineering