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