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)

, potentiallyexplaining this difference in the perception. Furthermore, we quantitatively confirmed that evenwhen student groups collaborate with identical ChatGPT settings, the resulting product ideasdemonstrate a similar degree of linguistic diversity as those found in ideas generated solely by thestudents.While this paper introduced an application of genAI in the context of group brainstorming, itmerely scratched the surface of a much broader landscape filled with more complex questions. Tocomprehensively unravel the intricate relationship between human creativity and genAI, furthersystematic research is needed. For example, as highlighted in [26], creative ideas, particularlywithin the domain of engineering, require exploring the nuanced interplay of various

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)

that students are influenced by their observation of models (e.g., peers, parents,etc.). Beyond learning, researchers have identified that students’ retention rates are positivelycorrelated to their access to individuals who can provide affective, financial, or informationalsupport, especially in traditionally underrepresented groups [6]. Within these or similartheoretical foundations, engineering educators have identified several specific ways socialinteractions positively influence academic outcomes [7]–[12]. Among the methods for studying student interactions, Social Network Analysis (SNA) isuniquely suited to quantitatively explore relationships between social interactions and studentlearning. To conduct an SNA study, researchers

Conference Session

Computers in Education Division (COED) Track 2.A

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jason M. Keith, Iowa State University of Science and Technology; Jason Coleman, Kansas State University; Lis Pankl, Mississippi State University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

and machine learning & softcomputing). The major in AI requires the four courses listed above as well as courses in human-computer interaction, Introduction to Machine Learning, Introduction to Analysis of Algorithmsas well as one psychology course focused on cognitive science as well as four courses from achoice among electives in computer science, industrial engineering, mathematics or psychology.The emphasis on cognitive science came out of research collaborations among variousdepartments in engineering, computer science and some humanities disciplines within arts &sciences. This unique nature also allowed for the degree to be developed with only one newcourse.Additionally, at MSU, engineering students take a required technical

Conference Session

Computers in Education Division (COED) Track 2.C

Collection

2025 ASEE Annual Conference & Exposition

Authors

Tammy Mackenzie, The Aula Fellowship; Lisa D. McNair, Virginia Tech; Rubaina Khan, University of Toronto; Animesh Paul, University of Georgia; Sreyoshi Bhaduri, Private Corporation

Tagged Topics

Diversity

Tagged Divisions

Computers in Education Division (COED)

concerns among educators and institutions, primarily deal-ing with plagiarism [28], renegotiating the role of instructors [29], and ethical concerns, forexample around using student data [30]. These tensions have led researchers to beseech de-velopers to create solutions that speak to systemic barriers [31], employ transparent method-ologies [32], and co-design with educators [33]. In recent research in EE, the issues discussedabove are prevalent along with calls for EE programs to ensure that their graduates havethe technical skills to develop products and processes embedded in complex systems thatwork seamlessly [34]. Further, these systems must be developed with sustainable mindsetsand use ethical design methodologies [35]. However, such

Conference Session

Computers in Education Division (COED) Track 4.B

Collection

2025 ASEE Annual Conference & Exposition

Authors

Madison Melton, University of North Carolina at Charlotte; Mohsen M Dorodchi, University of North Carolina at Charlotte

Tagged Divisions

Computers in Education Division (COED)

in the classroom[28, 29, 30]. For this reason, studies were analyzed through the lens of Student-Focused orTeacher-Focused implementations of generative AI in education. After the analysis began, itbecame evident that some studies on generative AI in education did not have a clearly definedprimary beneficiary. To account for this, a third category (Both) was introduced. This categoryincludes research that provides equal benefits to both teachers and students. For example, studiesthat use generative AI to generate personalized feedback (beyond just a numerical grade) reduceteachers’ workload while also offering students tailored insights to improve their assignments. Interms of architecture, most studies on generative AI in education